Virtual power plant transaction and scheduling optimization method and system participating in spot market

Abstract

The invention provides a transaction and scheduling optimization method and system for a virtual power plant participating in a spot market, relates to the technical field of energy systems, and solves the problem of electricity purchase cost minimization of the virtual power plant participating in spot transaction and the problem of how to report and regulate adjustable resources in the virtual power plant containing multiple distributed energy main bodies. The method comprises the following steps: acquiring first data and second data; according to the spot transaction price, the static data, the first data and the second data, constructing an optimization target for minimizing the overall power purchase cost of the virtual power plant, and setting a comprehensive power constraint condition and an energy storage system constraint condition; and solving through a linear programming solver to obtain an optimal spot declaration strategy and a regulation and control strategy of corresponding equipment. The method is used in the virtual power plant transaction and scheduling optimization process, the declaration strategy of the electric power spot market is considered, the electric quantity of the real-time market is adjusted through adjustable resources such as adjustable equipment and energy storage equipment on the user side, and the electricity purchase cost can be reduced and the marketization risk can be controlled.

Description

Technical Field

[0001] This application relates to the field of energy system technology, and in particular to a method and system for virtual power plant trading and dispatch optimization in the spot market. Background Technology

[0002] As the global energy system transitions towards a low-carbon model, distributed energy, with its advantages of being clean, low-carbon, efficient, flexible, and economical, has gradually become an important component of the modern power system.

[0003] However, with the expansion of distributed energy scale and the growth of trading demand, existing models, based on static electricity prices and load curves for internal power allocation, fail to incorporate the dynamic impact of day-ahead and real-time market price fluctuations on bidding strategies. Currently, electricity trading and adjustable load are often treated separately, lacking a collaborative optimization mechanism. Furthermore, under the background of open electricity sales, virtual power plant models still follow the generation-side optimization paradigm, failing to construct a cost-optimal model for the purchasing side. Therefore, it is urgent to construct a coupled spot trading bidding-regulation model to address these issues. Summary of the Invention

[0004] This application provides a method for optimizing the trading and dispatch of virtual power plants participating in the spot market, which solves the problem of minimizing the electricity purchase cost of virtual power plants participating in spot trading, as well as the problem of how to obtain the optimal application and controllable resources in a virtual power plant containing multiple distributed energy entities.

[0005] To achieve the above objectives, this application adopts the following technical solution:

[0006] Firstly, a method for optimizing virtual power plant trading and scheduling in the spot market is provided, including:

[0007] S1: Obtain historical electricity trading events and corresponding spot trading prices;

[0008] S2: Predict the first data based on historical electricity trading events and corresponding spot trading prices; where the first data includes the future day-ahead spot price and the real-time spot price;

[0009] S3: Collect and store historical dynamic and static data of equipment resources; among which, historical dynamic data includes power generation, power consumption, power storage and operating status of power equipment; static data includes installed capacity and maximum power data of power generation equipment;

[0010] S4: Predict the second data based on historical dynamic and static data, wherein the second data includes the electricity consumption of electrical equipment and the energy storage capacity of energy storage equipment in the future period;

[0011] S5: Based on spot transaction prices, static data, first data, and second data, construct an optimization objective that minimizes the overall electricity purchase cost of the virtual power plant;

[0012] S6: Set comprehensive power constraints and energy storage system constraints; where comprehensive power includes adjustable load power and photovoltaic power generation.

[0013] S7: Based on the comprehensive power constraints and energy storage system constraints, the optimization objective is solved using a linear programming solver to obtain the optimal spot market reporting strategy and the corresponding equipment control strategy.

[0014] In conjunction with the first aspect above, in one possible implementation, the optimization objective of minimizing the overall electricity purchase cost of the virtual power plant is:

[0015]

[0016] Where T represents the total trading hours in a day; t represents a specific trading session in a day; The real-time spot market electricity purchase cost for virtual power plants; The real-time spot market electricity purchase cost for virtual power plants; The penalty fees stipulated in the spot trading rules; The revenue gained by virtual power plants participating in demand response; These are the costs of load-adjustable power equipment participating in demand response, and the costs of charging and discharging photovoltaic power generation and energy storage equipment.

[0017] In conjunction with the first aspect above, in one possible implementation, the method for obtaining the spot market electricity purchase cost of the virtual power plant is as follows: through the formula...

[0018] In the formula, The electricity volume reported by the virtual power plant recently. The virtual power plant plan participates in demand response electricity volume. The price is the spot price for the period t before the current date.

[0019] In conjunction with the first aspect above, in one possible implementation, the method for obtaining the day-ahead declared electricity volume is: through the formula

[0020] In the formula, For the nth unadjustable device, the predicted load power during time period t. Let be the predicted power generation for the nth photovoltaic period t. Let β be the planned charging and discharging amount for the nth energy storage period t, and β be the adjusted predicted power coefficient.

[0021] In conjunction with the first aspect above, in one possible implementation, the method for obtaining the real-time spot electricity purchase cost of the virtual power plant is as follows: through the formula...

[0022] In the formula, Real-time electricity consumption for the virtual power plant The actual demand response volume of the virtual power plant. The price is the real-time spot price for time period t.

[0023] In conjunction with the first aspect above, in one possible implementation, the real-time electricity consumption of the virtual power plant is obtained by: using the formula

[0024] In the formula, Let t be the actual load power of the nth device during time period t. This represents the actual power generation during the nth photovoltaic period t. This represents the actual charge and discharge amount during the nth energy storage period t.

[0025] In conjunction with the first aspect above, in one possible implementation, the virtual power plant obtains its demand response benefits through the following formula:

[0026] In the formula, For virtual power plants to participate in demand response electricity volume, Price subsidies are provided in response to demand.

[0027] In conjunction with the first aspect above, in one possible implementation, the method for obtaining the demand response cost of the adjustable power electrical equipment is as follows: through the formula...

[0028] In the formula, Costs associated with participating in demand response load regulation.

[0029] In conjunction with the first aspect above, in one possible implementation, the cost of photovoltaic power generation is obtained by: using the formula

[0030] In the formula, For photovoltaic fixed costs, For photovoltaic power generation price, This represents the actual power generation that photovoltaic systems should generate. This represents the real-time power generation from photovoltaic systems.

[0031] In conjunction with the first aspect above, in one possible implementation, the method for obtaining the charging and discharging cost of the energy storage device satisfies the following formula:

[0032]

[0033] In the formula, α t,n For the charging and discharging state of energy storage devices, α t,n =0 represents the charging status of the energy storage device, α t,n =1 represents the discharge state of the energy storage device; The cost of charging energy storage devices. The amount of electricity used to charge energy storage devices. Improve the charging efficiency of energy storage devices. The cost of discharging electricity for energy storage devices. The discharge capacity of the energy storage device. The discharge efficiency of the energy storage device.

[0034] In conjunction with the first aspect above, in one possible implementation, the comprehensive power constraint condition satisfies the formula:

[0035] In the formula, For total power, For the nth non-adjustable load power, This represents the power of the nth adjustable load. For the nth photovoltaic power generation, The charging and discharging power of the nth energy storage device.

[0036] In conjunction with the first aspect above, in one possible implementation, the adjustable load power constraint condition is:

[0037] in, Adjustable load power; and This is a Boolean variable representing the adjustment state of the adjustable load; when the adjustable load power is increased... Before adjustment When the adjustable load power is reduced When not lowered To increase power; To increase the power limit; To reduce power; To lower the power limit; Maximum load adjustment is allowed throughout the entire scheduling period;

[0038] In conjunction with the first aspect above, in one possible implementation, the photovoltaic power generation constraint condition is:

[0039] in, Let P be the predicted photovoltaic power generation at time t. pv,max This represents the maximum power generation capacity of the photovoltaic system.

[0040] In conjunction with the first aspect above, in one possible implementation, the constraints of the energy storage system are:

[0041]

[0042] in, The charging power of the energy storage device at time t. Let t be the upper limit of the charging power of the energy storage device at time t. Let t be the discharge power of the energy storage device. Let t be the upper limit of the discharge power of the energy storage device at time t. Let t be the amount of electricity stored in the energy storage device. The energy stored in the energy storage device at time t-1, Let t be the lower limit of the amount of electricity that the energy storage device can store. Let t be the maximum amount of electricity that the energy storage device can store at time t. and This is a Boolean variable representing the charging and discharging state of the energy storage device: when the energy storage device is charging... When not charging When the energy storage device discharges When not discharged Let be the charging efficiency of the energy storage device at time t. Let be the discharge efficiency of the energy storage device at time t.

[0043] Based on the above technical solutions, this application provides a method for optimizing virtual power plant trading and dispatching in the spot market. By introducing the minimization of virtual power plant electricity purchase costs as the optimization objective, and coupling trading application strategies with corresponding control strategies, a virtual power plant strategy system is constructed under certain physical and rule constraints. Compared to existing technologies, this invention considers the application strategy of the electricity spot market, using adjustable resources such as user-side adjustable equipment and energy storage devices to regulate the real-time market electricity volume, thereby reducing electricity purchase costs and controlling market risks.

[0044] In a second aspect, an electronic device is provided, comprising: a communication unit and a processing unit;

[0045] The communication unit is used to acquire historical electricity trading events and corresponding spot trading prices; and to collect and store historical dynamic and static data of power equipment; wherein, the historical dynamic data includes the power generation, electricity consumption, power storage, and operating status of the power equipment.

[0046] The processing unit is used to predict first data based on historical electricity trading events and corresponding spot trading prices; wherein the first data includes future day-ahead spot prices and real-time spot prices; predict second data based on historical dynamic and static data, wherein the second data includes electricity consumption, power generation, and energy storage in the future period; construct an optimization objective based on spot trading prices, static data, the first data, and the second data; set comprehensive power constraints and energy storage system constraints; wherein the comprehensive power includes adjustable load power and photovoltaic power generation; and solve the optimization objective using a linear programming solver based on the comprehensive power constraints and energy storage system constraints to obtain the optimal spot trading strategy and the corresponding equipment control strategy.

[0047] Thirdly, this application provides an electronic device, including: a processor and a storage medium; the storage medium includes instructions, and the processor is configured to execute the instructions to implement the methods described in the first aspect and any possible implementation thereof. This electronic device may be an electronic device or a chip within an electronic device.

[0048] Fourthly, this application provides a virtual power plant trading and dispatch optimization system participating in the spot market, comprising: a data acquisition module, an optimization target construction module, a constraint setting module, and a solution module for spot trading declarations and corresponding dispatch optimization; wherein, the data acquisition module is used to acquire historical power trading events and corresponding spot trading prices; predict first data based on historical power trading events and corresponding spot trading prices; wherein, the first data includes future day-ahead spot electricity prices and real-time spot electricity prices; collect and store historical dynamic data and static data of equipment resources; wherein, the historical dynamic data includes the power generation, power consumption, power storage, and operating status of power equipment; the static data includes the installed capacity, maximum capacity, and maximum operating status of power generation equipment. Power data; prediction of second data based on historical dynamic and static data, where the second data includes the electricity consumption of electrical equipment and the storage capacity of energy storage equipment in the future period; construction of optimization objective module to construct the optimization objective of minimizing the overall electricity purchase cost of virtual power plant based on spot transaction price, static data, first data and second data; setting of constraint condition module to set comprehensive power constraint condition and energy storage system constraint condition, where comprehensive power includes adjustable load power and photovoltaic power generation power; solving spot transaction declaration and corresponding scheduling optimization module to solve the optimization objective through linear programming solver based on comprehensive power constraint condition and energy storage system constraint condition to obtain the optimal spot declaration strategy and corresponding equipment control strategy.

[0049] Fifthly, this application provides a computer-readable storage medium storing instructions that, when executed on an electronic device, cause the electronic device to perform the methods described in the first aspect and any possible implementation thereof.

[0050] In a sixth aspect, this application provides a computer program product containing instructions that, when run on an electronic device, cause the electronic device to perform the methods described in the first aspect and any possible implementation thereof.

[0051] This application provides a method and system for optimizing virtual power plant trading and dispatching in the spot market. By minimizing the purchase cost of virtual power plants as the optimization objective, it couples a trading application strategy with a corresponding control strategy to construct a virtual power plant strategy system under certain physical and rule constraints. Compared to existing technologies, this invention considers the application strategy of the electricity spot market and uses adjustable resources such as user-side adjustable equipment and energy storage devices to regulate the real-time market electricity volume, thereby reducing purchase costs and controlling market risks.

[0052] It should be understood that the descriptions of technical features, technical solutions, beneficial effects, or similar language in this application do not imply that all features and advantages can be achieved in any single embodiment. Rather, it is understood that the description of a feature or beneficial effect means that a specific technical feature, technical solution, or beneficial effect is included in at least one embodiment. Therefore, the descriptions of technical features, technical solutions, or beneficial effects in this specification do not necessarily refer to the same embodiment. Furthermore, the technical features, technical solutions, and beneficial effects described in this embodiment can be combined in any suitable manner. Those skilled in the art will understand that embodiments can be implemented without one or more specific technical features, technical solutions, or beneficial effects of a particular embodiment. In other embodiments, additional technical features and beneficial effects may be identified in specific embodiments that do not embody all embodiments. Attached Figure Description

[0053] Figure 1 A system architecture diagram of a power plant trading and dispatch optimization system provided in this application embodiment;

[0054] Figure 2 A flowchart illustrating a method for virtual power plant trading and dispatch optimization in the spot market, provided as an embodiment of this application;

[0055] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application;

[0056] Figure 4 This is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0057] In the description of this application, unless otherwise stated, " / " means "or," for example, A / B can mean A or B. The "and / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone. Furthermore, "at least one" means one or more, and "multiple" means two or more. The terms "first," "second," etc., do not limit the quantity or order of execution, and "first," "second," etc., do not necessarily imply differences.

[0058] It should be noted that, in this application, the terms "exemplary" or "for example" are used to indicate that something is being described as an example, illustration, or illustration. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or design solutions. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0059] The virtual power plant trading and scheduling optimization method for participating in the spot market provided in this application embodiment can be applied to, for example... Figure 1 In the power plant trading and dispatch optimization system 100 shown, such as Figure 1 As shown, the communication system includes: an information capture terminal 101, a cloud computing device 102, and an edge computing node 103.

[0060] Among them, the information capture terminal 101 is used to acquire historical power trading events and corresponding spot trading prices, and to collect and store historical dynamic and static data of equipment resources.

[0061] The cloud computing device 102 is used to predict first data based on historical electricity trading events and corresponding spot trading prices, and to predict second data based on historical dynamic and static data.

[0062] Edge computing node 103 is used to construct an optimization objective that minimizes the overall electricity purchase cost of the virtual power plant based on spot trading prices, static data, first data, and second data; set comprehensive power constraints and energy storage system constraints; and solve the optimization objective through a linear programming solver based on the comprehensive power constraints and energy storage system constraints to obtain the optimal spot bidding strategy and the corresponding equipment control strategy.

[0063] To address the problem of minimizing electricity purchase costs for virtual power plants participating in spot market transactions in existing technologies, and the technical challenges of obtaining optimal bidding and controllable resources within virtual power plants containing multiple distributed energy entities, this application provides a method for optimizing virtual power plant transactions and scheduling in the spot market. This method includes: acquiring historical electricity trading events and corresponding spot market prices; predicting first data based on historical electricity trading events and corresponding spot market prices; collecting and storing historical dynamic and static data of power equipment; predicting second data based on historical dynamic and static data; constructing an optimization objective based on spot market prices, static data, the first data, and the second data; setting comprehensive power constraints and energy storage system constraints; and solving the optimization objective using a linear programming solver based on the comprehensive power constraints and energy storage system constraints to obtain the optimal spot market bidding strategy and the corresponding equipment control strategy. Based on this, the present invention considers the bidding strategy in the electricity spot market, using adjustable resources such as user-side adjustable equipment and energy storage equipment to regulate the real-time market electricity volume, thereby reducing electricity purchase costs and controlling market risks.

[0064] like Figure 2 As shown in the embodiment of this application, a method for optimizing virtual power plant trading and scheduling in the spot market includes:

[0065] S201. Obtain historical electricity trading events and corresponding spot trading prices.

[0066] In some implementations, historical electricity trading events and corresponding spot trading prices are obtained by accessing provincial or municipal trading centers or load management centers.

[0067] S202. Predict the first data based on historical electricity trading events and corresponding spot trading prices;

[0068] The first data includes future day-ahead spot electricity prices and real-time spot electricity prices; and is based on historical electricity trading events and corresponding spot trading prices.

[0069] In some implementations, machine learning or neural network training methods such as XGBoost and LSTM are used to predict future day-ahead spot electricity prices and real-time spot electricity prices.

[0070] S203. Collect and store historical dynamic and static data of equipment resources.

[0071] Historical dynamic data includes power generation from power generation equipment, power consumption from power consumption equipment, power storage capacity from energy storage equipment, and the operating status of each power equipment; static data includes installed capacity and maximum power of power generation equipment.

[0072] In some implementations, the power generation of power generation equipment, the power consumption of power consumption equipment, the power storage capacity of energy storage equipment, and the operating status of each power device can be obtained by calling the load management center.

[0073] S204. Predict the second data based on historical dynamic and static data;

[0074] The second set of data includes the electricity consumption of electrical equipment and the energy storage capacity of energy storage devices in the future time period.

[0075] In one possible implementation of this application embodiment, in the above S204, when predicting the second data, meteorological information, user behavior, historical electricity consumption data and baseline load can be combined, and a time series prediction algorithm can be used to predict the second data.

[0076] It should be noted that the construction of the baseline load needs to take into account the reference days for non-working days and working days, and the energy storage capacity prediction of energy storage devices needs to take into account the coupling effect of battery health and ambient temperature to avoid the accumulation of model errors due to overcharging / over-discharging.

[0077] For example, during the peak electricity consumption period at midday in summer, when the outdoor temperature exceeds 35°C and the humidity reaches 80%, based on users' historical air conditioning usage habits, it is predicted that the air conditioning power consumption during this period will increase by 40% compared to the baseline. Here, the baseline is the average air conditioning power consumption at a certain moment on a reference day selected according to the demand response rules.

[0078] S205. Based on spot transaction prices, static data, first data, and second data, construct an optimization objective to minimize the overall electricity purchase cost of the virtual power plant.

[0079] The first and second data structures include future day-ahead spot electricity prices, real-time spot electricity prices, electricity consumption of electrical equipment, and energy storage capacity of energy storage equipment.

[0080] In one possible implementation of this application embodiment, the optimization objective of minimizing the overall electricity purchase cost of the virtual power plant is:

[0081]

[0082] Where T represents the total trading hours in a day; t represents a specific trading session in a day; For virtual power plants, the day-ahead market purchase price of electricity; The real-time spot market electricity purchase cost for virtual power plants; The penalty fees stipulated in the spot trading rules; The revenue gained by virtual power plants participating in demand response; These are the costs of load-adjustable power equipment participating in demand response, and the costs of charging and discharging photovoltaic power generation and energy storage equipment.

[0083] It should be noted that in the process of pursuing cost minimization, the operational constraints and safety standards of the equipment must be met to avoid power supply interruptions or equipment instability due to over-optimization.

[0084] In one possible implementation of this application embodiment, the method for obtaining the real-time spot market electricity purchase cost of the virtual power plant is as follows: through the formula

[0085] in, The electricity volume reported by the virtual power plant recently. The virtual power plant plan participates in demand response electricity volume. The day-ahead spot price for period t is determined by the market clearing results of the day before the electricity market operation day, reflecting the day-ahead market value of electricity during that period.

[0086] It should be noted that this formula is a simplified calculation of the day-ahead market electricity purchase cost of a virtual power plant under ideal conditions. In practical applications, the calculation should also take into account the electricity trading settlement rules of various regions.

[0087] For example, suppose a virtual power plant has a day-ahead market electricity price of t during a certain trading session of a day. The price is 0.5 yuan / kWh, and the electricity volume reported recently is... The planned electricity consumption is 1000 kWh, which will participate in demand response. If the electricity consumption is 200 kWh, then the day-ahead market electricity purchase cost for that period, calculated according to the formula, is:

[0088] In one possible implementation of this application embodiment, the method for obtaining the day-ahead reported electricity volume is: through a formula

[0089] in, For the nth unadjustable device, the predicted load power during time period t. Let be the predicted power generation for the nth photovoltaic period t. Let β be the planned charge / discharge amount for the nth energy storage device during time period t, and let β be the adjusted predicted power coefficient.

[0090] It should be noted that, due to unforeseen factors in practical applications, appropriate adjustments need to be made based on real-time data when submitting applications, while also adhering to the application rules of the electricity market and the safety constraints of the power grid.

[0091] For example, suppose a virtual power plant has three unadjustable devices with predicted loads of 100, 150, and 200 kWh during a trading session t of a day; two photovoltaic devices with predicted power generation of 80 and 120 kWh; and one energy storage device with a planned charge / discharge capacity of 50 kWh. If the predicted power coefficient β for the adjusted declaration is taken as 0.9, then the day-ahead declared power for this period is...

[0092] In one possible implementation of this application embodiment, the method for obtaining the real-time spot electricity purchase cost of the virtual power plant is: through the formula

[0093] in, Real-time electricity consumption for the virtual power plant The actual demand response volume of the virtual power plant. The price is the real-time spot price for time period t.

[0094] It should be noted that in practical applications, the rules for electricity market transactions and settlements must be followed.

[0095] For example, suppose that during a certain trading period t, the real-time electricity consumption of the virtual power plant is... The figure is 1500 kWh, based on the electricity volume declared in the market the day before yesterday. The actual amount of electricity reduced through demand response is 1000 kWh. For 200 kWh, real-time spot price The price is 0.6 yuan / kWh, so the spot market electricity purchase cost for that period is calculated using the formula as follows:

[0096] In one possible implementation of this application embodiment, the real-time electricity consumption of the virtual power plant is obtained by: using the formula

[0097] in, Let t be the actual load power of the nth device during time period t. This represents the actual power generation during the nth photovoltaic period t. This represents the actual charge and discharge amount of the nth energy storage device during time period t.

[0098] It should be noted that this formula assumes that the real-time operating data of each device can accurately reflect its actual status. However, in practical applications, it is necessary to further process and verify the data by combining data correction algorithms and device operating status assessment methods.

[0099] For example, suppose a virtual power plant, during a certain trading session t of a day, includes three electrical devices with actual loads of 100, 150, and 200 kWh respectively; two photovoltaic devices with actual power generation of 80 and 120 kWh respectively; and one energy storage device with an actual charging and discharging capacity of 50 kWh. Then the real-time electricity consumption during this session is:

[0100] In one possible implementation of this application embodiment, the virtual power plant participates in demand response and obtains its benefits through the formula:

[0101] in, For virtual power plants to participate in demand response electricity volume, Price subsidies are provided in response to demand.

[0102] It should be noted that this formula assumes that the calculation of demand response revenue is only related to the response electricity volume and subsidy price. In reality, it is also affected by many factors such as response time, response rate, and response quality, and needs to be calculated according to the demand response rules of each region.

[0103] For example, during a certain period, a virtual power plant reduces electricity consumption in response to grid demand. For 500 kWh, the subsidized price If the price is 3 yuan / kWh, then the demand response revenue for that period is...

[0104] In one possible implementation of this application embodiment, the demand response cost of the adjustable power electrical equipment is obtained by: using the formula

[0105] in, Costs associated with participating in demand response load regulation.

[0106] In some implementations, power equipment is categorized and managed, with different cost assessment models developed based on equipment type and application scenario. The corresponding power equipment's control system monitors its operating status in real time, and automatically calculates costs based on preset adjustment strategies and cost parameters when responding to grid demands. And combined with the actual response power The cost is calculated.

[0107] It should be noted that this formula is an idealized demand response cost calculation model. In actual implementation, factors such as equipment operating curves and maintenance costs need to be comprehensively considered.

[0108] For example, during a certain period of time, a certain electrical device increases its power consumption according to the instructions of the power grid. To support grid stability, the load regulation cost of this equipment... (Considering equipment wear and tear, additional operating costs, etc.), the demand response cost of this equipment during this period is:

[0109] In one possible implementation of this application embodiment, the cost of photovoltaic power generation is obtained by: using the formula

[0110] in, For photovoltaic fixed costs, For photovoltaic power generation price, This represents the actual power generation that photovoltaic systems should generate. This represents the real-time power generation from photovoltaic systems.

[0111] It should be noted that the cost of photovoltaic power generation is affected by factors such as weather conditions and equipment maintenance. Therefore, in practical applications, these factors need to be considered comprehensively to ensure the comprehensiveness and accuracy of cost calculation.

[0112] For example, in a specific photovoltaic power generation project, assuming the fixed cost on a certain day is 1000 yuan, the electricity price is 0.5 yuan per kilowatt-hour, the actual expected power generation is 2000 kilowatt-hours, and the real-time power generation is 1800 kilowatt-hours, then the photovoltaic power generation cost for that day can be calculated using the following formula:

[0113] In one possible implementation of this application embodiment, the method for obtaining the charging and discharging cost of the energy storage device satisfies the following formula:

[0114]

[0115] Where, α t,n In the charging / discharging state, α t,n =0 represents the charging state, α t,n =1 represents the discharge state; The cost of charging energy storage devices. The amount of electricity used to charge energy storage devices. Improve the charging efficiency of energy storage devices. The cost of discharging electricity for energy storage devices. The discharge capacity of the energy storage device. The discharge efficiency of the energy storage device.

[0116] It should be noted that the long-term operation of energy storage systems needs to balance economic efficiency and equipment lifespan. In practical applications, a life cost model needs to be introduced to include maintenance costs and equipment replacement cycles in the total cost accounting.

[0117] For example, in an energy storage project in an industrial park, the system analyzes the time-of-use pricing policy (e.g., off-peak price is 0.3 yuan / kWh, peak price is 1.5 yuan / kWh) and automatically executes a "off-peak charging - peak discharging" strategy. If the charging amount on a certain day is 1000kWh (charging efficiency is 86%) and the discharging amount is 1000kWh (discharging efficiency is 88%), then the electricity cost of the energy storage system is 0.3*1000 / 86% - 1.5*1000*88% = -971.16 yuan.

[0118] S206. Set comprehensive power constraints and energy storage system constraints.

[0119] The total power includes adjustable load power and photovoltaic power generation.

[0120] In one possible implementation of this application embodiment, the comprehensive power constraint condition satisfies the following formula:

[0121] In the formula, For total power, For the nth non-adjustable load power, This represents the power of the nth adjustable load. For the nth photovoltaic power generation, The charging and discharging power of the nth energy storage device.

[0122] It should be noted that virtual power plants need to be evaluated using real-time monitoring and forecasting technologies. The fluctuation range is considered, and redundant capacity is designed in conjunction with the response speed of the energy storage system to cope with the risk of a sudden drop in power generation caused by extreme weather.

[0123] In one possible implementation of this application embodiment, the adjustable load power constraint condition is satisfied as follows:

[0124]

[0125] in, Adjustable load power; and This is a Boolean variable representing the adjustment state of the adjustable load; when the adjustable load power is increased... Before adjustment When the adjustable load power is reduced When not lowered To increase power; To increase the power limit; To reduce power; To lower the power limit; Allows for adjustment of the maximum load throughout the entire scheduling period.

[0126] In some implementations, algorithms such as linear programming and solvers like CBC and Gurobi can be used to find the optimal load adjustment strategy that satisfies the adjustable load power constraint, thereby achieving the goal of effectively balancing the supply and demand of the power system.

[0127] It should be noted that this adjustable load power constraint model assumes that the parameters are relatively stable within the scheduling cycle and does not consider the impact of sudden events such as equipment failure on load adjustment. In practical applications, the model can be appropriately modified according to the actual situation.

[0128] In one possible implementation of this application embodiment, the photovoltaic power generation constraint condition satisfies the following formula:

[0129] in, Let P be the predicted photovoltaic power generation at time t. pv,max This represents the maximum power generation capacity of the photovoltaic system.

[0130] In some implementations, the output power of the photovoltaic system is monitored in real time, and the prediction and control strategies for photovoltaic power generation are optimized by combining weather forecasts and historical data analysis, thereby better meeting the power constraints.

[0131] It should be noted that this photovoltaic power generation constraint is applicable to situations where the illumination conditions are relatively stable. However, in situations where the illumination intensity changes rapidly or is blocked by clouds, the power constraint strategy needs to be further adjusted and optimized.

[0132] In one possible implementation of this application embodiment, the energy storage system constraints are as follows:

[0133]

[0134] in, The charging power of the energy storage device at time t. Let t be the upper limit of the charging power of the energy storage device at time t. Let t be the discharge power of the energy storage device. Let t be the upper limit of the discharge power of the energy storage device at time t. Let t be the amount of electricity stored in the energy storage device. The energy stored in the energy storage device at time t-1, Let t be the lower limit of the amount of electricity that the energy storage device can store. Let t be the maximum amount of electricity that the energy storage device can store at time t. and This is a Boolean variable representing the charging and discharging state of the energy storage device: when the energy storage device is charging... When not charging When the energy storage device discharges When not discharged Let be the charging efficiency of the energy storage device at time t. Let be the discharge efficiency of the energy storage device at time t.

[0135] It should be noted that the constraints of this energy storage system assume that the charging and discharging efficiency, self-discharge loss and other parameters of the energy storage system are relatively stable within the scheduling cycle, and do not consider the impact of sudden events such as equipment failure on the energy storage system. In practical applications, the model can be appropriately modified according to the actual situation.

[0136] S207. Based on the comprehensive power constraints and energy storage system constraints, the optimization objective is solved using a linear programming solver to obtain the optimal spot market reporting strategy and the corresponding equipment control strategy.

[0137] Among them, the spot reporting strategy is the spot reporting coefficient; the control strategy is the adjustable load power and the charging and discharging power of energy storage equipment.

[0138] It should be noted that the setting of the spot market declaration coefficient needs to take into account both market rules and equipment response speed.

[0139] The foregoing mainly describes the solutions of the embodiments of this application from the perspective of device implementation. It is understood that each device, such as an electronic device, includes at least one of the hardware structures and software modules corresponding to the execution of each function in order to achieve the above-mentioned functions. Those skilled in the art should readily recognize that, based on the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in a hardware or computer software-driven hardware manner depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0140] This application embodiment can divide the electronic device into functional units according to the above method example. For example, each function can be divided into a separate functional unit, or two or more functions can be integrated into one processing unit. The integrated unit can be implemented in hardware or as a software functional unit. It should be noted that the unit division in this application embodiment is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods.

[0141] When using integrated units, Figure 3 A possible structural schematic diagram of the electronic device (referred to as electronic device 50) involved in the above embodiments is shown. The electronic device 50 includes a processing unit 501 and a communication unit 502, and may also include a storage unit 503. Figure 3The structural diagram shown can be used to illustrate the structure of the electronic device involved in the above embodiments.

[0142] when Figure 3 The schematic diagram shown is used to illustrate the structure of the electronic device involved in the above embodiments. The processing unit 501 is used to control and manage the operation of the electronic device, the communication unit 502 is used for the electronic device to communicate with other devices, and the storage unit 503 is used to store the program code and data of the electronic device.

[0143] For example, communication unit 502 is used to acquire first data, which is predicted based on historical electricity trading events and corresponding spot trading prices. The first data includes future day-ahead spot prices and real-time spot prices.

[0144] The communication unit 502 is also used to acquire second data, which is predicted based on historical dynamic data and static data. The second data includes the electricity consumption of electrical equipment and the energy storage capacity of energy storage equipment in the future period.

[0145] Processing unit 502 is used to construct an optimization objective that minimizes the overall electricity purchase cost of the virtual power plant based on spot transaction prices, static data, first data, and second data, and to set comprehensive power constraints and energy storage system constraints; and to solve the optimization objective by combining the comprehensive power constraints and energy storage system constraints through a linear programming solver to obtain the optimal spot bidding strategy and the corresponding equipment control strategy.

[0146] The processing unit 501 can be a processor or a controller, and the communication unit 502 can be a communication interface, transceiver, transceiver circuit, transceiver device, etc. The term "communication interface" is a general term and may include one or more interfaces. The storage unit 503 can be a memory. When the electronic device 50 is a chip, the processing unit 501 can be a processor or a controller, and the communication unit 502 can be an input interface and / or an output interface, pins, or circuits, etc. The storage unit 503 can be a storage unit within the chip (e.g., a register, cache, etc.) or a storage unit located outside the chip (e.g., read-only memory (ROM), random access memory (RAM, etc.).

[0147] The communication unit can also be called a transceiver unit. The antenna and control circuit with transceiver functions in the electronic device 50 can be considered as the communication unit 502 of the electronic device 50, and the processor with processing functions can be considered as the processing unit 501 of the electronic device 50. Optionally, the device in the communication unit 502 used to implement the receiving function can be considered as the communication unit. The communication unit is used to execute the receiving steps in the embodiments of this application, and the communication unit can be a receiver, a receiver circuit, etc. The device in the communication unit 502 used to implement the transmitting function can be considered as the transmitting unit. The transmitting unit is used to execute the transmitting steps in the embodiments of this application, and the transmitting unit can be a transmitter, a transmitter, a transmitting circuit, etc.

[0148] Figure 3 If the integrated units in the process are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, in essence, or the parts that contribute to the prior art, or all or part of the technical solutions, can be embodied in the form of software products. These computer software products are stored in a storage medium and include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. Storage media for storing computer software products include various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory, random access memory, magnetic disks, or optical disks.

[0149] Figure 3 The units in the process can also be called modules; for example, a processing unit can be called a processing module.

[0150] This application also provides a hardware structure diagram of an electronic device (denoted as electronic device 60), see [link to diagram]. Figure 4 The electronic device 60 includes a processor 601, and optionally, a memory 602 connected to the processor 601.

[0151] In the first possible implementation, see Figure 4 The electronic device 60 also includes a transceiver 603. The processor 601, memory 602, and transceiver 603 are connected via a bus. The transceiver 603 is used to communicate with other devices or communication networks. Optionally, the transceiver 603 may include a transmitter and a receiver. The device in the transceiver 603 that implements the receiving function can be considered as a receiver, which is used to perform the receiving steps in the embodiments of this application. The device in the transceiver 603 that implements the transmitting function can be considered as a transmitter, which is used to perform the transmitting steps in the embodiments of this application.

[0152] Based on the first possible implementation method Figure 4 The structural diagram shown can be used to illustrate the structure of the electronic device involved in the above embodiments.

[0153] in, Figure 4 This can also be illustrated by a system chip in an electronic device. In this case, the actions performed by the aforementioned electronic device can be implemented by this system chip; the specific actions performed can be found above and will not be repeated here.

[0154] In implementation, each step of the method provided in this embodiment can be completed by integrated logic circuits in the processor or by instructions in software form. The steps of the method disclosed in the embodiments of this application can be directly manifested as being executed by a hardware processor, or being executed by a combination of hardware and software modules in the processor.

[0155] The processor in this application may include, but is not limited to, at least one of the following: a central processing unit (CPU), a microprocessor, a digital signal processor (DSP), a microcontroller unit (MCU), or an artificial intelligence processor, etc., and other computing devices that run software. Each computing device may include one or more cores for executing software instructions to perform calculations or processing. The processor may be a separate semiconductor chip or integrated with other circuits into a single semiconductor chip. For example, it may be integrated with other circuits (such as encoding / decoding circuits, hardware acceleration circuits, or various bus and interface circuits) to form a System-on-a-Chip (SoC), or it may be integrated as a built-in processor within an ASIC. The ASIC with the integrated processor may be packaged separately or together with other circuits. In addition to the cores for executing software instructions to perform calculations or processing, the processor may further include necessary hardware accelerators, such as field-programmable gate arrays (FPGAs), PLDs (programmable logic devices), or logic circuits that implement dedicated logic operations.

[0156] The memory in the embodiments of this application may include at least one of the following types: read-only memory (ROM) or other types of static storage devices capable of storing static information and instructions; random access memory (RAM) or other types of dynamic storage devices capable of storing information and instructions; or electrically erasable programmable-only memory (EEPROM). In some scenarios, the memory may also be a compact disc read-only memory (CD-ROM) 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 code in the form of instructions or data structures and accessible by a computer, but is not limited thereto.

[0157] This application also provides a computer-readable storage medium including instructions that, when run on a computer, cause the computer to perform any of the methods described above.

[0158] This application also provides a computer program product containing instructions that, when run on a computer, cause the computer to perform any of the methods described above.

[0159] This application also provides a chip including a processor and an interface circuit. The interface circuit is coupled to the processor. The processor is used to run computer programs or instructions to implement the above-described method. The interface circuit is used to communicate with other modules outside the chip.

[0160] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented using software programs, implementation can be, in whole or in part, in the form of a computer program product. This computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device containing one or more servers, data centers, etc., that can be integrated with the medium. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state disks (SSDs)).

[0161] Although this application has been described herein in conjunction with various embodiments, those skilled in the art, by reviewing the accompanying drawings, the disclosure, and the appended claims, will understand and implement other variations of the disclosed embodiments in carrying out the claimed application. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude multiple instances. A single processor or other unit can implement several functions listed in the claims. While different dependent claims may recite certain measures, this does not mean that these measures cannot be combined to produce good results.

[0162] Although this application has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made thereto without departing from the spirit and scope of this application. Accordingly, this specification and drawings are merely exemplary illustrations of this application as defined by the appended claims, and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from the spirit and scope of this application. Thus, if such modifications and modifications of this application fall within the scope of the claims of this application and their equivalents, this application is also intended to include such modifications and modifications.

Claims

1. A method for optimizing virtual power plant trading and dispatching in the spot market, characterized in that, include: Obtain historical electricity trading events and corresponding spot trading prices; The first data is predicted based on historical electricity trading events and corresponding spot trading prices. The first data includes the future day-ahead spot price and the real-time spot price. Collect and store historical dynamic and static data of power equipment; among which, historical dynamic data includes power generation, power consumption, power storage and operating status of power equipment; The second data is predicted based on historical dynamic and static data, which includes the electricity consumption, power generation and energy storage of electrical equipment in the future period. An optimization objective is constructed based on spot transaction prices, static data, first data, and second data. Set comprehensive power constraints and energy storage system constraints; where comprehensive power includes adjustable load power and photovoltaic power generation. Based on the comprehensive power constraints and energy storage system constraints, the optimization objective is solved using a linear programming solver to obtain the optimal spot market reporting strategy and the corresponding equipment control strategy.

2. The method according to claim 1, characterized in that, The optimization objective of minimizing the overall electricity purchase cost of the virtual power plant is: Where T represents the total trading hours in a day; t represents a specific trading session in a day; This refers to the cost of purchasing electricity in the spot market before the current price date; The cost of purchasing electricity in the spot market in real time; The penalty fees stipulated in the spot trading rules; The benefits gained from participating in demand response; These are the costs of load-adjustable power equipment participating in demand response, and the costs of charging and discharging photovoltaic power generation and energy storage equipment.

3. The method according to claim 2, characterized in that, The formula Obtain real-time spot market electricity purchase costs In the formula, The electricity volume reported so far. To plan for participation in demand response electricity, The price is the spot price for the period t before the current date.

4. The method according to claim 2, characterized in that, The method for obtaining the real-time spot electricity purchase fee is as follows: through the formula In the formula, For real-time electricity consumption, For the actual amount of electricity used in demand response, The price is the real-time spot price for time period t.

5. The method according to claim 2, characterized in that, The charging and discharging cost of the energy storage device is: In the formula, α t,n Used to indicate the charging and discharging status of energy storage devices. The cost of charging energy storage devices. The amount of electricity used to charge energy storage devices. Improve the charging efficiency of energy storage devices. The cost of discharging electricity for energy storage devices. The discharge capacity of the energy storage device. The discharge efficiency of the energy storage device.

6. The method according to claim 1, characterized in that, The comprehensive power constraint condition satisfies the following formula: In the formula, For total power, For the nth non-adjustable load power, This represents the power of the nth adjustable load. For the nth photovoltaic power generation, The charging and discharging power of the nth energy storage device.

7. The method according to claim 6, characterized in that, The adjustable load power constraint condition is: in, Adjustable load power; and These are the first Boolean variable and the second Boolean variable, respectively, used to represent the adjustment status of the adjustable load; To increase power; To increase the power limit; To reduce power; To lower the power limit; Allows for adjustment of the maximum load throughout the entire scheduling period.

8. The method according to claim 6, characterized in that, The photovoltaic power generation constraint condition is as follows: in, Let P be the predicted photovoltaic power generation at time t. pv,max This represents the maximum power generation capacity of the photovoltaic system.

9. The method according to claim 6, characterized in that, The constraints of the energy storage system are: in, The charging power of the energy storage device at time t. Let t be the upper limit of the charging power of the energy storage device at time t. Let t be the discharge power of the energy storage device. The upper limit of the discharge power of the energy storage device at time t; Let t be the amount of electricity stored in the energy storage device. The energy stored in the energy storage device at time t-1, Let t be the lower limit of the amount of electricity that the energy storage device can store. Let t be the maximum amount of electricity that the energy storage device can store at time t. and The third and fourth Boolean variables represent the charging and discharging states of the energy storage device. Let be the charging efficiency of the energy storage device at time t. Let be the discharge efficiency of the energy storage device at time t.

10. An electronic device, characterized in that, include: Communication unit and processing unit; The communication unit is used to acquire historical electricity trading events and corresponding spot trading prices; It also collects and stores historical dynamic and static data of power equipment; among which, historical dynamic data includes the power generation, power consumption, power storage and operating status of power equipment; The processing unit is used to predict first data based on historical electricity trading events and corresponding spot trading prices; wherein the first data includes future day-ahead spot prices and real-time spot prices; predict second data based on historical dynamic and static data, wherein the second data includes electricity consumption, power generation, and energy storage in the future period; construct an optimization objective based on spot trading prices, static data, the first data, and the second data; set comprehensive power constraints and energy storage system constraints; wherein the comprehensive power includes adjustable load power and photovoltaic power generation; and solve the optimization objective using a linear programming solver based on the comprehensive power constraints and energy storage system constraints to obtain the optimal spot trading strategy and the corresponding equipment control strategy.

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