An online optimization selling method for maximizing photovoltaic power generation electricity transaction revenue

Abstract

The application provides an online optimization selling method for maximizing photovoltaic power generation electricity transaction benefits, and relates to the technical field of intelligent transaction. The method comprises the following steps: acquiring power generation data; setting an expected selling price for the power generation data; acquiring a real-time selling price, generating a multi-period allocation strategy based on the real-time selling price and the expected selling price, and performing transaction on each power generation share in the power generation data in a preset period based on the multi-period allocation strategy. The application solves the problem of low stability of transaction strategies in the related art.

Description

Technical Field

[0001] This application relates to the field of intelligent trading technology, and more specifically, to an online optimized sales method for maximizing the revenue from photovoltaic power generation electricity trading. Background Technology

[0002] Solar energy, as an inexhaustible and clean energy source, offers hope for solving the energy crisis. Users can generate income by selling the energy produced by solar photovoltaic (PV) systems to the power grid. In recent years, an increasing amount of research has focused on improving the economic benefits of photovoltaic (PV) power generation.

[0003] In existing technologies, energy storage and sales strategies are formulated based on offline forecast data and historical information. Control strategies are designed to optimize the timing of photovoltaic (PV) power sales, attempting to sell electricity during periods of high electricity prices. However, this approach typically focuses only on specific scenarios or objectives, failing to achieve optimal overall returns and performing poorly in uncertain markets. It simply sells PV power all at once or employs a relatively singular sales strategy, resulting in poor revenue stability during market price fluctuations. The unpredictability of electricity market price volatility makes it difficult for sales strategies to adapt to price changes in a timely manner, thus failing to maximize returns and leading to low transaction efficiency in practical applications. Furthermore, for general online electricity sales problems, existing technologies employ online pricing and trading algorithms that focus on maximizing seller profits in the market, but these are not entirely compatible with the characteristics of PV power sales (such as battery storage losses and cyclical production capacity).

[0004] As can be seen from the above, how to improve the stability of trading strategies still needs to be addressed. Summary of the Invention

[0005] This application provides an online optimized sales method, apparatus, electronic device, and storage medium for maximizing the revenue from photovoltaic power generation trading, which can solve the problem of low stability of trading strategies in related technologies. The technical solutions are as follows:

[0006] According to one aspect of this application, an online optimized sales method for maximizing the revenue from photovoltaic power generation electricity trading includes:

[0007] Obtain production capacity data;

[0008] Set the expected selling price for the aforementioned production capacity data;

[0009] Obtain the real-time selling price, generate a multi-period allocation strategy based on the real-time selling price and the expected selling price, and conduct transactions on each capacity share in the capacity data in a preset period based on the multi-period allocation strategy.

[0010] According to one aspect of this application, a trading strategy apparatus includes:

[0011] The data acquisition module is used to acquire production capacity data;

[0012] The setting module is used to set the expected selling price for the production capacity data;

[0013] The transaction module is used to obtain the real-time selling price, generate a multi-period allocation strategy based on the real-time selling price and the expected selling price, and conduct transactions on each capacity share in the capacity data in a preset period based on the multi-period allocation strategy.

[0014] In one exemplary embodiment, the setting module includes:

[0015] A share allocation unit is used to allocate the capacity data into shares based on a preset period to obtain at least one capacity share corresponding to each preset period.

[0016] The pricing unit is used to set the expected pricing for each of the aforementioned production capacity shares.

[0017] In one exemplary embodiment, the transaction module includes:

[0018] The price acquisition unit is used to obtain the real-time price corresponding to each period;

[0019] The selling unit is configured to trade the capacity share if the expected selling price corresponding to the capacity share in the capacity data of the period is less than or equal to the real-time selling price; and if the expected selling price corresponding to the capacity share in the capacity data is greater than the real-time selling price, determine the longest waiting time corresponding to each capacity share based on a one-way transaction algorithm, and sell the capacity share based on the longest waiting time.

[0020] In one exemplary embodiment, the selling unit is configured to:

[0021] The model generation subunit is used to generate an expected return model based on a one-way transaction algorithm.

[0022] The time determination subunit is used to determine the threshold matrix corresponding to the expected selling price based on the expected revenue model, so as to determine the longest storage time corresponding to each of the production capacity shares.

[0023] In an exemplary embodiment, the time determination subunit is configured to:

[0024] The first revenue determination subunit is used to determine the immediate sale revenue corresponding to the real-time selling price based on the expected revenue model.

[0025] The second revenue determination subunit is used to determine the expected sales revenue corresponding to the expected selling price based on the expected revenue model.

[0026] The threshold determination subunit is used to determine the threshold matrix that makes the expected sale proceeds equal to the immediate sale proceeds.

[0027] In an exemplary embodiment, the threshold matrix is ​​represented as:

[0028] Among them, C loss For storage losses, For the expected selling price, R i For real-time pricing, t i，j This represents the longest waiting time for the j-th unit of electricity within the i-th cycle.

[0029] In one exemplary embodiment, the selling unit includes:

[0030] The detection subunit is used to detect the storage time corresponding to each capacity share.

[0031] Sell ​​sub-units for trading the capacity share if the storage time is greater than or equal to the maximum waiting time.

[0032] According to one aspect of this application, an electronic device includes at least one processor and at least one memory, wherein computer-readable instructions are stored on the memory; the computer-readable instructions are executed by one or more of the processors to cause the electronic device to implement the trading strategies and methods described above.

[0033] According to one aspect of this application, a storage medium stores computer-readable instructions thereon, which are executed by one or more processors to implement the trading strategies and methods described above.

[0034] According to one aspect of this application, a computer program product includes computer-readable instructions stored in a storage medium, wherein one or more processors of an electronic device read the computer-readable instructions from the storage medium, load and execute the computer-readable instructions, causing the electronic device to implement the trading strategies and methods described above.

[0035] The beneficial effects of the technical solution provided in this application are:

[0036] In the above technical solution, trading of each capacity share is conducted by acquiring real-time and expected selling prices. Capacity data is allocated across multiple preset periods, optimizing trading strategies to maximize profits when prices are high. This effectively solves the problem of low stability in trading strategies present in related technologies. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1 This is a schematic diagram based on the implementation environment involved in this application;

[0039] Figure 2 This is a flowchart illustrating an online optimized sales method for maximizing the revenue from photovoltaic power generation electricity trading, according to an exemplary embodiment.

[0040] Figure 3 yes Figure 2 A flowchart of step 230 in one embodiment corresponds to the following example;

[0041] Figure 4 yes Figure 2 A flowchart of step 250 in one embodiment corresponds to the following example;

[0042] Figure 5 yes Figure 4 In one embodiment, step 255 is shown in a flowchart.

[0043] Figure 6 yes Figure 5 A flowchart of step 2553 in one embodiment corresponds to the following example;

[0044] Figure 7 yes Figure 4 In one embodiment, step 255 is shown in a flowchart.

[0045] Figure 8 This is a schematic diagram illustrating the specific implementation of an online optimized sales method for maximizing the revenue from photovoltaic power generation trading in an application scenario.

[0046] Figure 9 This is a structural block diagram of a trading strategy device according to an exemplary embodiment;

[0047] Figure 10 This is a structural block diagram of an electronic device according to an exemplary embodiment. Detailed Implementation

[0048] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application.

[0049] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in this disclosure means the presence of the stated features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. It should be understood that when we say an element is “connected” or “coupled” to another element, it can be directly connected or coupled to the other element, or there may be intermediate elements. Furthermore, “connected” or “coupled” as used herein can include wireless connections or wireless coupling. The term “and / or” as used herein includes all or any units and all combinations of one or more associated listed items.

[0050] As mentioned earlier, existing technologies rely on offline forecast data and historical information to formulate energy storage and sales strategies. However, this approach typically focuses only on specific scenarios or objectives, failing to achieve optimal overall returns and performing poorly in uncertain markets. It simply sells all photovoltaic power generation at once or employs a relatively singular sales strategy, resulting in poor revenue stability during market price fluctuations. The difficulty in predicting electricity market price volatility makes it hard for sales strategies to adapt to price changes in a timely manner, thus failing to maximize returns and leading to low transaction efficiency in practical applications.

[0051] As can be seen from the above, the relevant technologies still suffer from the drawback of low stability in trading strategies.

[0052] Therefore, the trading strategy and method provided in this application can effectively improve the stability of the trading strategy. Accordingly, the trading strategy and method are applicable to trading strategy devices, which can be deployed on electronic devices. The electronic devices can be computer devices configured with the von Neumann architecture, such as desktop computers, laptops, servers, etc.; the electronic devices can also be electronic devices with central control functions, such as gateways; the electronic devices can also refer to portable mobile electronic devices, such as smartphones, tablets, etc.

[0053] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0054] Figure 1This is a schematic diagram illustrating an implementation environment for an online optimized sales method that maximizes the revenue from photovoltaic power generation trading. It should be noted that this implementation environment is merely an example adapted to the present invention and should not be construed as providing any limitation on the scope of the invention.

[0055] The implementation environment includes a data acquisition terminal 110 and a server terminal 130.

[0056] Specifically, acquisition terminal 110 can also be considered as an electrical energy data acquisition device. For example, acquisition terminal 110 is a power grid node.

[0057] Server 130 can be an electronic device such as a desktop computer, laptop computer, or server, or it can be a computer cluster consisting of multiple servers, or even a cloud computing center consisting of multiple servers. Server 130 is used to provide backend services, such as, but not limited to, electricity trading services.

[0058] The server 130 and the data acquisition terminal 110 establish a network communication connection in advance via wired or wireless means, and data transmission between the server 130 and the data acquisition terminal 110 is realized through this network communication connection. The transmitted data includes, but is not limited to: market price information, energy storage information, etc.

[0059] In one application scenario, through the interaction between the data acquisition terminal 110 and the server terminal 130, the data acquisition terminal 110 collects market price information and energy storage information for the power grid, and uploads the market price information and energy storage information to the server terminal 130 to request the server terminal 130 to provide trading strategy services.

[0060] For server 130, after receiving market price information and energy storage information uploaded by acquisition terminal 110, it calls the trading strategy service to control the timing of energy sales, thereby achieving comprehensive analysis and guidance of energy trading and solving the problem of low stability of trading strategies in related technologies.

[0061] Please see Figure 2 This application provides an online optimized sales method for maximizing the revenue from photovoltaic power generation electricity trading. This method is applicable to electronic devices, which can be... Figure 1 The server 130 in the implementation environment is shown.

[0062] In the following method embodiments, for ease of description, the execution subject of each step of the method is an electronic device, but this does not constitute a specific limitation.

[0063] like Figure 2 As shown, the method may include the following steps:

[0064] Step 210: Obtain production capacity data.

[0065] Among them, capacity data refers to data related to the trading object throughout the entire transaction process. Specifically, it involves acquiring data generated during the production process of the trading object to predict and quantify the changes in the trading object's reserves at each point in time during the transaction process.

[0066] In one possible implementation, the capacity data is the energy storage data generated by photovoltaic power generation.

[0067] Step 230: Set the expected selling price for the production capacity data.

[0068] The expected selling price is a pre-set price for the transaction object. By comparing the expected selling price with the real-time selling price, the value of the transaction object can be quickly determined, thereby instructing the transaction process for the transaction object.

[0069] One possible implementation involves dividing capacity data into different capacity shares and setting different expected selling prices for each capacity share.

[0070] Step 250: Obtain the real-time selling price, generate a multi-period allocation strategy based on the real-time selling price and the expected selling price, and trade each capacity share in the capacity data in a preset period based on the multi-period allocation strategy.

[0071] The real-time selling price is the market price of the traded object obtained in real time. The multi-period allocation strategy compares the real-time selling price with the expected selling price in preset periods and allocates the trading share to each preset period, ultimately determining the trading object corresponding to each production capacity share of the transaction.

[0072] In one possible implementation, the production process of the transaction object is divided into multiple cycles according to a preset period, and the real-time selling price corresponding to that cycle is obtained at the beginning of each cycle.

[0073] By comparing the real-time selling price and the expected selling price over multiple periods, a multi-period allocation strategy is implemented to rationally conduct transactions in each period, thereby balancing the fluctuations in the real-time selling price of the trading object across multiple periods and maximizing profits. This allows the trading object to be sold at a high price, resulting in greater profits.

[0074] In one exemplary embodiment, such as Figure 3 As shown, step 230 may include the following steps:

[0075] Step 231: Divide the capacity data into shares based on the preset period to obtain at least one capacity share for each preset period.

[0076] Specifically, the production process of the trading object is divided into discrete time periods by a preset period to obtain discrete time periods and corresponding capacity data in each time period. Then, by dividing the capacity data of the trading object in each time period into multiple parts, the capacity share corresponding to a portion of the trading object in each time period can be obtained.

[0077] Step 233: Set the corresponding expected selling price for each production capacity share.

[0078] The expected selling price is set at different prices within a preset threshold for each production capacity share.

[0079] In one possible implementation, the capacity data within a preset period is divided into logh+1 equal capacity shares, and for each capacity share j in 0, ..., logh, an expected selling price is defined.

[0080] That is, the j-th part The capacity share is expected to be at least 2 j Transactions are conducted at a unit price.

[0081] Through the above process, by segmenting production capacity data and setting expected selling prices, the value of the transaction object can be accurately measured by comparing different expected selling prices with the real-time selling price in the subsequent transaction process. This ensures flexible selling under different market conditions, improves transaction stability, and allows for the sale of a larger proportion of the transaction at high prices, thereby increasing transaction revenue.

[0082] In one exemplary embodiment, such as Figure 4 As shown, step 250 may include the following steps:

[0083] Step 251: Obtain the real-time selling price for each period.

[0084] One possible implementation involves obtaining the fluctuation range of real-time selling prices and setting expected selling prices for each capacity share within that fluctuation range.

[0085] Step 253: If the expected selling price corresponding to the capacity share in the capacity data during the cycle is less than or equal to the real-time selling price, then trade the capacity share.

[0086] After obtaining the real-time selling price corresponding to the cycle, the expected selling price corresponding to each capacity share is compared with the real-time selling price corresponding to that cycle, and then the capacity share whose expected selling price is lower than the actual selling price is traded at the actual selling price.

[0087] Step 255: If the expected selling price corresponding to the capacity share in the capacity data is greater than the real-time selling price, then determine the longest waiting time corresponding to each capacity share based on the one-way transaction algorithm, and sell the capacity share based on the longest waiting time.

[0088] It should be noted that during the transaction process, since the real-time selling price may be lower than the expected selling price, it is necessary to store the transaction objects corresponding to the production capacity share. This storage will cause a loss of the transaction objects, thereby reducing the storage volume corresponding to the production capacity share. At this time, the loss of the production capacity share is calculated by using a one-way transaction algorithm to determine the time required for the profit from the production capacity share to decrease to the profit from immediate sale after the loss has occurred, and thus generate the maximum waiting time.

[0089] In one possible implementation, a multi-period allocation strategy is executed using a one-way trading algorithm. The one-way trading algorithm is as follows:

[0090] Inputs: Maximum unit electricity price h, battery storage loss ratio C loss

[0091] Output: Total revenue Rev for period i i

[0092] 1: Initialization: Calculate the threshold matrix T

[0093] 2: Obtain P at the beginning of period i i and R i Set Rev i =0

[0094] 3: P i Divide into logh+1 parts, and initialize the profit Rev of each part j. i，j =0

[0095]

[0096] 5:for do

[0097] 6:for period k=i+1,...,i+t i，j do

[0098] 7: if then

[0099] 8: Sell share j, set

[0100] 9: break;

[0101] 10: else if k = i + t i，j then

[0102] 11: Sell share j at the current period price k. 12:

[0104]

[0105] 13:end if

[0106] 14:end for 15:

[0108] Rev i +=Rev i，j ;

[0109] 16:end for

[0110] 17: Total return Rev in return period i i .

[0111] In the one-way transaction algorithm, the first for loop iterates through all the transactions from... Calculate the revenue Rev per unit of logh, based on the share of j. i，j Add it to the current Rev i In the second for loop, the period k iterates from i+1 to i+t. i，j To determine the appropriate period for selling share j. If the unit price R of a certain period k k If Rexp is reached, then share j is sold, and the inner for loop ends. If share j is not sold before the end of the traversal of k, then the loop will continue in period k = ι + t. i，j At the price at that time R k The proceeds from the sale are:

[0112]

[0113] Finally, return Rev. i .

[0114] Through the above process, based on the longest waiting time storage capacity share, the capacity share of the current cycle is allocated to different cycles for trading, thereby reducing the impact of real-time price fluctuations in different cycles on the trading process and improving the stability of the trading process.

[0115] In one exemplary embodiment, such as Figure 5 As shown, step 255 may include the following steps:

[0116] Step 2551: Generate the expected return model based on the one-way transaction algorithm.

[0117] Among them, the expected revenue model is used to predict the revenue obtained from selling capacity share in each cycle based on the loss process of the trading object after obtaining the real-time selling price and the expected selling price.

[0118] In one possible implementation, the loss process of a transaction object is determined by obtaining the storage loss ratio during the storage process, thereby generating an expected revenue model.

[0119] Step 2553: Determine the threshold matrix corresponding to the expected selling price based on the expected revenue model, so as to determine the maximum storage time corresponding to each capacity share.

[0120] In one exemplary embodiment, such as Figure 6 As shown, step 2553 may include the following steps:

[0121] Step 25531: Determine the immediate sale revenue corresponding to the real-time selling price based on the expected revenue model.

[0122] The immediate sale revenue is the revenue obtained by selling the undamaged capacity share at the real-time selling price after acquiring the corresponding period.

[0123] In one possible implementation, logR is sold immediately within the i-th period. i The formula for the immediate sale proceeds of a unit of production capacity is:

[0124]

[0125] Among them, R i For real-time pricing, P i This refers to the production capacity data corresponding to the cycle.

[0126] Step 25533: Determine the expected sales revenue corresponding to the expected selling price based on the expected revenue model.

[0127] The expected sale proceeds are the proceeds from trading production capacity shares when the selling price reaches the expected selling price in a future cycle.

[0128] In one possible implementation, the formula for the expected sales revenue when selling the capacity share corresponding to period i in period k is:

[0129]

[0130] Among them, R k C is the real-time selling price for period k. loss This represents the storage loss rate of the transaction object during the storage process.

[0131] Step 25535: Determine the threshold matrix that makes the expected sale proceeds equal to the immediate sale proceeds.

[0132] The threshold matrix stores the longest waiting time for each capacity share in each cycle.

[0133] It is understandable that, due to the losses incurred during the storage process of the transaction object, as the storage time of the capacity share increases, the expected sales revenue will gradually decrease until it is equal to the immediate sales revenue. The storage time that has elapsed at this point is the longest waiting time.

[0134] In an exemplary embodiment, the threshold matrix is ​​represented as:

[0135]

[0136] Among them, the storage loss ratio constant C of the transaction object during the storage process loss ∈(0,1),

[0137] For the expected selling price, R i For real-time pricing, t i，j This represents the longest waiting time for the j-th unit of electricity within the i-th cycle.

[0138] Through the above process, the expected return model determines the return forecast for each capacity share, allowing the trading process to select the option with the highest overall return for each capacity share, thereby maximizing returns. By determining the maximum waiting time, it is ensured that each capacity share will not suffer a loss of returns due to trading too late.

[0139] In one exemplary embodiment, such as Figure 7 As shown, step 255 may also include the following steps:

[0140] Step 2555: Detect the storage time corresponding to each capacity share.

[0141] Step 2557: If the storage time is greater than or equal to the maximum waiting time, then the transaction capacity share is determined.

[0142] Specifically, after obtaining the real-time selling price, each production capacity share with an expected selling price greater than the real-time selling price can be stored according to the longest waiting time, in order to wait for the real-time selling price to fluctuate to the expected selling price before being sold, or to be traded immediately after the storage time is greater than or equal to the longest waiting time.

[0143] Through the above process, it is ensured that the production capacity share is traded before any loss of revenue occurs, thus preventing revenue loss due to fluctuations in the actual selling price at different times.

[0144] Figure 8 This is a schematic diagram illustrating the specific implementation of an online optimized sales method for maximizing the revenue from photovoltaic power generation trading in an application scenario. In this scenario, the trading object is photovoltaic power generation. First, real-time electricity price information is collected, and the price is set between the lowest real-time price *l* and the highest real-time price *h*. Let *l* = 2. 0 =1 and h=2 loghAnd assume that any electricity price in the i-th period satisfies R i ∈[2 0 ,2 logh In each period i, the photovoltaic power generation is divided into logh+1 parts, with each part containing an amount of electricity. For photovoltaic power generation P i For each unit of electricity j, the expected selling price is defined as... The constant battery storage loss rate it produces is C loss .

[0145] Define the threshold matrix T = t i，j This controls the longest waiting time for each production capacity share, where the threshold matrix represents the time when the j-th unit of electricity reaches the selling price within the i-th period. The longest waiting time.

[0146] Through formula Calculate the threshold matrix, where the left side of the formula represents the price reaching a certain level in a future period. At that time, the sales revenue of the j-th unit of electricity, and the battery storage loss. If the energy has been stored for t cycles, then the remaining energy will be reduced to (1-C) of the original energy. loss ) t The formula on the right side represents the immediate sale of the [number]th [unit] within the [i]th period.

[0147] The revenue per unit of electricity.

[0148] By detecting each capacity share and the real-time selling price, capacity shares with expected selling prices less than or equal to the real-time selling price are traded immediately, while capacity shares with expected selling prices greater than the real-time selling price are sold after the maximum storage waiting time, or when the real-time selling price matches the expected selling price.

[0149] Specifically, the transaction process for each capacity share is as follows: Figure 8 As shown in the figure, the dashed boxes represent periods, the dark bars represent the share of capacity sold in the current period, and the blank bars represent the share of capacity sold in previous periods. The numbers below the bars correspond to the expected selling price. In period i, photovoltaic power is allocated and may be sold over multiple periods. The numbers are assigned to j ≤ logR. i The production capacity share is sold immediately within cycle i. For the number j > logR i The production capacity share will be sold in future cycle k, provided that... And k≤i+t i，j Any capacity share j in the threshold period i+t i，j For sale.

[0150] The resulting threshold matrix is ​​shown in the table below:

[0151]

[0152] The threshold matrix T stores the t values ​​of each j segment in each period i. ij Value, t ij This indicates waiting for the j-th unit of electricity in the i-th cycle to reach the selling price. The longest time. For any period k, j ≤ logR. k production capacity share, I k，j Set it to zero.

[0153] In this application scenario, the performance of this trading strategy and method is demonstrated by comparing the Optimal Offline Algorithm (OPT) with the one-way trading algorithm.

[0154] Specifically, the performance of the one-way transaction algorithm relative to the optimal offline algorithm is quantified by the competition ratio. Results show that the competition ratio achieved by the one-way transaction algorithm matches the lower bound of the problem, proving its optimality at Θ(logh).

[0155] The competition ratio is defined as the worst-case ratio of the profit obtained by the Optimal Offline Transaction (OPT) algorithm to the profit obtained by the one-way transaction algorithm. For the one-way transaction algorithm, the competition ratio is expressed as:

[0156]

[0157] Among them, the OPT algorithm has a period of ι+t i，logh Previously sold all P i The amount of electricity, and sell all P at a sufficiently high unit price in one go. i Electricity. OPT's chosen sales period.

[0158] i+opt at the maximum waiting threshold ι+t i，logh Before it arrives, the OPT algorithm and the one-way trading algorithm will be in period i′. max Stop. At this point, the lower bound for the energy trading problem handled by the one-way trading algorithm is γ = Ω(logh).

[0159] The benefit of the OPT algorithm is:

[0160]

[0161] Where opt represents the time interval between period i and the optimal selling period i+opt, and opt≤t i，logh

[0162] One-way transaction algorithm revenue satisfy

[0163] in This represents the profit achieved by the one-way transaction algorithm. This represents the benefit achieved by the OPT algorithm.

[0164] By providing a maximum unit electricity price h, the one-way trading algorithm achieves a competition ratio for the photovoltaic power generation trading problem.

[0165] α = O(logh). As shown above, the matching of the competition ratio with the lower bound proves that the proposed method is tight in the modeled trading environment, and the proposed trading strategy and method have better stability than existing methods.

[0166] The following are embodiments of the apparatus described in this application, which can be used to execute the trading strategies and methods involved in this application. For details not disclosed in the apparatus embodiments of this application, please refer to the method embodiments of the trading strategies and methods involved in this application.

[0167] Please see Figure 9 This application provides a trading strategy device 900, including but not limited to:

[0168] Data acquisition module 910 is used to acquire production capacity data.

[0169] Setting module 930 is used to set the expected selling price for production capacity data.

[0170] The trading module 950 is used to obtain real-time selling prices, generate multi-period allocation strategies based on real-time and expected selling prices, and trade each capacity share in the capacity data in a preset period based on the multi-period allocation strategies.

[0171] It should be noted that the trading strategy device provided in the above embodiments is only illustrated by the division of the above functional modules when performing trading strategies. In actual applications, the above functions can be assigned to different functional modules as needed. That is, the internal structure of the trading strategy device will be divided into different functional modules to complete all or part of the functions described above.

[0172] Furthermore, the trading strategy apparatus and trading strategy and method embodiments provided in the above embodiments belong to the same concept, and the specific way in which each module performs operations has been described in detail in the method embodiments, and will not be repeated here.

[0173] Please see Figure 10 This application provides an electronic device 4000 suitable for an online optimized sales method that maximizes the revenue from photovoltaic power generation electricity trading. Figure 10 In this context, the electronic device 4000 includes at least one processor 4001 and at least one memory 4003.

[0174] The data interaction between the processor 4001 and the memory 4003 can be achieved through at least one communication bus 4002. This communication bus 4002 may include a path for transmitting data between the processor 4001 and the memory 4003. The communication bus 4002 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The communication bus 4002 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 10 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0175] Optionally, the electronic device 4000 may further include a transceiver 4004, which can be used for data interaction between the electronic device and other electronic devices, such as sending and / or receiving data. It should be noted that in practical applications, the transceiver 4004 is not limited to one type, and the structure of the electronic device 4000 does not constitute a limitation on the embodiments of this application.

[0176] Processor 4001 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 4001 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0177] The memory 4003 may be a ROM (Read Only Memory) or other type of static storage device capable of storing static information and instructions, RAM (Random Access Memory) or other type of dynamic storage device capable of storing information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program instructions or code in the form of instructions or data structures and accessible by the electronic device 400, but not limited thereto.

[0178] The memory 4003 stores computer-readable instructions, and the processor 4001 can read the computer-readable instructions stored in the memory 4003 through the communication bus 4002.

[0179] The computer-readable instructions are executed by one or more processors 4001 to implement the trading strategies and methods in the above embodiments.

[0180] Furthermore, this application provides a storage medium suitable for an online optimized sales method that maximizes the revenue from photovoltaic power generation trading. The storage medium stores computer-readable instructions that are executed by one or more processors to implement the above-mentioned trading strategies and methods.

[0181] This application provides a computer program product applicable to an online optimized sales method for maximizing the revenue from photovoltaic power generation electricity trading. The computer program product includes computer-readable instructions stored in a storage medium. One or more processors of an electronic device read the computer-readable instructions from the storage medium, load and execute the computer-readable instructions, so that the electronic device implements the above-mentioned trading strategy and method.

[0182] Compared to related technologies, this invention can respond to market price fluctuations in real time, achieving dynamic optimization. Through a one-way trading algorithm, this invention reveals market price information in real time each cycle and dynamically adjusts capacity sales and storage strategies to flexibly respond to market fluctuations, ensuring that more capacity is sold when prices are high and stored when prices are low, resulting in more stable and maximized returns. This invention introduces a storage loss constant and sets a maximum waiting time threshold for capacity shares, enabling capacity to be sold in time before storage losses occur, avoiding revenue loss due to excessive storage time. It ensures optimal capacity returns by balancing storage losses and market prices. This invention divides photovoltaic capacity into multiple shares in each cycle, setting different expected selling prices and waiting thresholds for each share, flexibly capturing high-price opportunities during price fluctuations, ensuring greater returns when market prices are high. Timely adjustment of the capacity sales ratio within the cycle achieves a dynamic optimal balance of returns. The online algorithm design of this invention fully considers the constraints of market electricity price fluctuations and storage losses, and through theoretical proof, achieves a competition ratio close to the optimal offline algorithm. This competition ratio ensures that the algorithm can still achieve near-theoretical optimal returns even in the worst case, guaranteeing stability and efficiency in practical applications.

[0183] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0184] The above are only some embodiments of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. An online optimized sales method for maximizing the revenue from photovoltaic power generation electricity trading, characterized in that, include: Obtain production capacity data; Set the expected selling price for the aforementioned production capacity data; Obtain the real-time selling price, generate a multi-period allocation strategy based on the real-time selling price and the expected selling price, and conduct transactions on each capacity share in the capacity data in a preset period based on the multi-period allocation strategy.

2. The method as described in claim 1, characterized in that, Setting the expected selling price for the production capacity data includes: The capacity data is divided into shares based on a preset period to obtain at least one capacity share for each preset period. Set corresponding expected selling prices for each of the aforementioned production capacity shares.

3. The method as described in claim 1, characterized in that, The process of obtaining the real-time selling price, generating a multi-period allocation strategy based on the real-time selling price and the expected selling price, and trading each capacity share in the capacity data within a preset period based on the multi-period allocation strategy includes: Get the real-time selling price for each period; If the expected selling price corresponding to the capacity share in the capacity data during the period is less than or equal to the real-time selling price, then the capacity share is traded. If the expected selling price corresponding to the capacity share in the capacity data is greater than the real-time selling price, then the longest waiting time corresponding to each capacity share is determined based on the one-way transaction algorithm, and the capacity share is sold based on the longest waiting time.

4. The method as described in claim 3, characterized in that, The determination of the longest waiting time corresponding to each of the aforementioned capacity shares based on the one-way transaction algorithm includes: Generate expected return model based on one-way transaction algorithm; Based on the expected revenue model, a threshold matrix corresponding to the expected selling price is determined to determine the maximum storage time corresponding to each of the aforementioned capacity shares.

5. The method as described in claim 4, characterized in that, The step of determining the threshold matrix corresponding to the expected selling price based on the expected revenue model to determine the maximum storage time includes: The immediate selling revenue corresponding to the real-time selling price is determined based on the expected revenue model. Determine the expected sales revenue corresponding to the expected selling price based on the expected revenue model; Determine the threshold matrix that makes the expected sale proceeds equal to the immediate sale proceeds.

6. The method as described in claim 3, characterized in that, The threshold matrix is ​​represented as follows: Among them, C loss For storage losses, For the expected selling price, R i For real-time pricing, t i，j This represents the longest waiting time for the j-th unit of electricity within the i-th cycle.

7. The method as described in claim 3, characterized in that, The sale of the capacity share based on the longest waiting time includes: Detect the storage time corresponding to each production capacity share; If the storage time is greater than or equal to the maximum waiting time, then the capacity share is traded.

8. A trading strategy device, characterized in that, include: The data acquisition module is used to acquire production capacity data; The setting module is used to set the expected selling price for the production capacity data; The transaction module is used to obtain the real-time selling price, generate a multi-period allocation strategy based on the real-time selling price and the expected selling price, and conduct transactions on each capacity share in the capacity data in a preset period based on the multi-period allocation strategy.

9. An electronic device, characterized in that, include: At least one processor and at least one memory, wherein, The memory stores computer-readable instructions; The computer-readable instructions are executed by one or more of the processors, causing the electronic device to implement the trading strategy and method as described in any one of claims 1 to 7.

10. A storage medium having computer-readable instructions stored thereon, characterized in that, The computer-readable instructions are executed by one or more processors to implement the trading strategy and method as described in any one of claims 1 to 7.

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