Spot trading optimization method and device considering uncertainty of renewable energy

Abstract

The present invention discloses a spot trading optimization method and device that considers the uncertainty of renewable energy. The method comprises obtaining historical output data of renewable energy; constructing an uncertainty set using the historical output data through a data-driven robust optimization model; constructing a spot trading optimization model based on the uncertainty set, an objective function with the goal of maximizing social welfare, and constraints corresponding to the objective; and solving the spot trading optimization model to obtain an optimization result. The present invention incorporates uncertainty factors into a data-driven robust optimization method, and uses historical renewable energy output data to construct an uncertainty set to characterize uncertain parameters, allowing for fluctuations within the set. This method effectively balances the optimality and feasibility of robust optimization, solving the problem of renewable energy output uncertainty while achieving optimal overall operating costs in the electricity spot market.

Description

Technical Field

[0001] The present invention relates to the field of electric power technology, and in particular to a spot transaction optimization method and device taking into account the uncertainty of renewable energy. Background Art

[0002] Currently, the electricity market has essentially formed a two-tiered trading system, both inter-provincial and intra-provincial. A power market system primarily based on medium- and long-term trading, supplemented by spot trading, has taken shape. With the gradual expansion of the liberalization of power generation and consumption plans, the continuous enrichment of spot market theory, and the gradual accumulation of experience in spot market operations, the development environment for renewable energy, particularly wind power, has undergone significant changes. Traditional policies based on full guaranteed purchases will be phased out, and the market will become a key channel for renewable energy consumption. Due to the volatility and randomness of wind power, wind power has inherent disadvantages in market participation. Consequently, most renewable energy players in the current market still utilize a fixed output method. Since renewable energy forecast deviations are the primary driver of traditional power dispatch and reserve reserves in the power market, carefully accounting for the uncertainty of renewable energy output is a key approach to improving the economic efficiency of power market operations.

[0003] The integration of numerous renewable energy sources has brought numerous challenges to the power grid. The most prominent of these is the significant intermittency and volatility of wind and photovoltaic power generation systems, as well as large load forecasting errors. This poses risks of overshooting line flows and node voltages. To ensure grid security and stability, traditional grid dispatching and the spot market need to reserve more system-wide positive and negative backup resources to account for the uncertainties of renewable energy units. However, the pre-emptive reservation of backup resources in local power grids dilutes the economic viability of the overall power market.

[0004] The optimization methods for renewable energy output uncertainty in power systems are mainly divided into stochastic optimization methods (SO), fuzzy optimization methods (FO) and robust optimization methods (RO).

[0005] Among them, the stochastic programming method has very high requirements for the distribution information of uncertain variables. Accurate probability distribution information is necessary for modeling and solving, and in order to obtain accurate distribution information of uncertain parameters, sufficient sample information is needed; but in engineering practice, due to the constraints of technical level, human management level and economic factors, it is often difficult to obtain sufficient sample information.

[0006] The fuzzy optimization method uses fuzzy membership functions to represent the degree of satisfaction of constraints, the expected level of the objective function, and the uncertain range of model coefficients. When making fuzzy decisions, the intersection of the fuzzy sets of the fuzzy constraints and the fuzzy objectives is usually taken, and then the decision with the maximum membership value is taken as the optimal fuzzy decision. The solution process is similar to the random optimization method, and the premise is that the accurate fuzzy membership function of the uncertain parameters must be obtained. However, in actual applications, the fuzzy membership function of the uncertain parameters is derived from the limited sample space data and the subjective experience of the decision maker, which brings large errors to the FO solution.

[0007] As one of the effective methods for dealing with uncertain variables, the robust optimization method has been gradually applied to the field of power system optimization. Robust optimization expresses uncertain variables in the form of a "set", which contains all possible values of the uncertain variables. Therefore, the optimal solution obtained by robust optimization can guarantee the feasibility and effectiveness of any element in the uncertain set, but it also leads to the over-conservativeness of robust optimization. The uncertainty set of traditional robust optimization is usually described by a box set, that is, the uncertainty variable The value is taken within the bounded symmetric interval. This method solves the model in the worst case, so the optimal solution cannot be obtained. Due to the characteristic of robust optimization that sacrifices optimality in exchange for model feasibility, traditional robust optimization cannot be applied in actual power system engineering. Summary of the Invention

[0008] In view of this, the purpose of the present invention is to overcome the shortcomings of the existing technology and provide a spot trading optimization method and device that takes into account the uncertainty of renewable energy, so as to solve the problem that the optimization method for processing renewable energy uncertainty in the power system in the existing technology cannot obtain the optimal solution.

[0009] To achieve the above object, the present invention is implemented by adopting the following technical solutions:

[0010] In a first aspect, the present invention provides a spot trading optimization method considering the uncertainty of renewable energy, the method comprising:

[0011] Obtain historical output data of renewable energy;

[0012] Constructing an uncertainty set using the historical output data through a data-driven robust optimization model;

[0013] Based on the uncertain set, an objective function with the goal of maximizing social welfare and constraints corresponding to the objective are used to construct a spot trading optimization model;

[0014] Solve the spot trading optimization model to obtain an optimization result.

[0015] In combination with the first aspect, preferably, the historical output data includes:

[0016] Historical output data of wind farms and photovoltaic power plants.

[0017] In combination with the first aspect, preferably, constructing an uncertainty set using the historical output data through a data-driven robust optimization model includes:

[0018] The historical output data of the wind farm and the historical output data of the photovoltaic power plant are input into a pre-built data-driven robust optimization model for solution, thereby obtaining an uncertainty set of the wind power output and an uncertainty set of the photovoltaic output.

[0019] In combination with the first aspect, preferably, the objective function is:

[0020]

[0021] Where, Indicates the total number of time periods considered, with each 15-minute period being a time period; Indicates the number of units; Indicates the unit In the period contribution; 、 、 Respectively for units In the period Operating costs, startup costs, and shutdown costs, including unit operating costs It is a multi-segment linear function related to the output ranges declared by the unit and the corresponding energy prices; is the penalty factor for network constraint relaxation used for market clearing optimization; 、 Line Forward and reverse power flow slack variables; is the total number of lines; 、 Section Forward and reverse power flow slack variables; The total number of sections.

[0022] In conjunction with the first aspect, preferably, the constraint conditions corresponding to the target include:

[0023] System load balancing constraints,

[0024]

[0025] Where, For the crew In the period of efforts, For contact lines In the period The planned power, is the total number of contact lines, For the period System load;

[0026] The system has positive spare capacity constraints,

[0027]

[0028] Where, Indicates the unit In the period The start-stop state, Indicates that the unit is shut down. Indicates that the unit is turned on; Indicates the unit In the period Maximum output; For the period System positive spare capacity requirements;

[0029] System negative spare capacity constraint,

[0030]

[0031] Where, Indicates the unit In the period The minimum output; For the period System negative reserve capacity requirements;

[0032] System spinning reserve constraints,

[0033]

[0034] Where, For the crew Maximum climbing rate, For the crew Maximum downhill climbing rate; 、 The units In the period Maximum and minimum output; 、 Periods Adjusting spinning reserve requirements upwards or downwards;

[0035] Special unit state constraints,

[0036]

[0037] Where, Refers to the set of units that must be turned on;

[0038] Upper and lower limits of unit output,

[0039]

[0040] Upper and lower limit constraints on the unit group output,

[0041]

[0042] Where, For crew groups In the period The minimum output, For crew groups In the period Maximum output;

[0043] Unit climbing constraint,

[0044]

[0045] Minimum continuous start and stop time constraints for units,

[0046]

[0047] Where, The minimum continuous start time of the unit; The minimum continuous downtime of the unit; 、 For the crew In the period The continuous startup time and shutdown time;

[0048] Renewable energy uncertainty set constraints,

[0049]

[0050] Where, represents the set of renewable energy uncertainty constraints, For an uncertain set Mid-scene Renewable energy units Output deviation occurs probability; Renewable energy units In the scene Medium output deviation; It is the lower limit of the output deviation of new energy; It is the upper limit of the deviation of the output of new energy; is the minimum output of new energy unit i in period t, is the maximum output of new energy unit i in period t, is the predicted output of new energy unit i in period t;

[0051] Line flow constraints,

[0052]

[0053] Where, For the line The power transmission limit of For the crew Node to line The generator output power transfer distribution factor; For contact lines Node to line The generator output power transfer distribution factor; For nodes Line The generator output power transfer distribution factor; For nodes In the period Bus load value; 、 Line Forward and reverse power flow slack variables;

[0054] Sectional tidal flow constraints,

[0055]

[0056] Where, For cross section The lower limit of power flow transmission, For cross section The upper limit of power flow transmission; For the crew Node section The generator output power transfer distribution factor; For contact lines Node section The generator output power transfer distribution factor; For nodes Cross-section The generator output power transfer distribution factor; For nodes In the period Bus load value; 、 Section The forward and reverse power flow slack variables.

[0057] In combination with the first aspect, preferably, the renewable energy source includes a plurality of sources, and output data of different renewable energy sources are independent of each other.

[0058] In combination with the first aspect, preferably, solving the spot trading optimization model to obtain an optimization result includes:

[0059] CPLEX is used to solve the spot trading optimization model to obtain the unit segment winning quantity, unit start and shutdown status, and grid-wide node electricity price information for each trading period.

[0060] In a second aspect, the present invention provides a spot trading optimization device that takes into account the uncertainty of renewable energy, comprising:

[0061] An acquisition module is used to obtain historical output data of renewable energy;

[0062] A determination module, configured to construct an uncertainty set using the historical output data through a data-driven robust optimization model;

[0063] A construction module is used to construct a spot trading optimization model based on the uncertainty set, an objective function with the goal of maximizing social welfare, and boundary conditions such as the unit ramp-up rate, the unit output upper and lower limits, the transmission capacity constraint of the transmission line, and the decision variable limit constraint;

[0064] The solution module is used to solve the spot trading optimization model and obtain the optimization result.

[0065] In a third aspect, the present invention provides a spot trading optimization device that considers the uncertainty of renewable energy, including a processor and a storage medium;

[0066] The storage medium is used to store instructions;

[0067] The processor is configured to operate according to the instructions to execute the steps of the spot trading optimization method considering renewable energy uncertainty as described in any one of the first aspects.

[0068] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the spot trading optimization method considering the uncertainty of renewable energy as described in any one of the first aspects are implemented.

[0069] Compared with the prior art, the present invention has the following beneficial effects:

[0070] The present invention constructs an uncertainty set through a robust optimization model using historical output data of renewable energy, and then constructs a spot trading optimization model based on the uncertainty set with the goal of maximizing social welfare and the constraints corresponding to the goal; solves the spot trading optimization model to obtain an optimization result; the application incorporates uncertainty factors into the data-driven robust optimization model, and uses historical data on renewable energy output to construct an uncertainty set to characterize uncertain parameters, allowing fluctuations within the set, and has a good balance between the optimality and feasibility of robust optimization. While solving the problem of uncertainty in renewable energy output, it achieves the optimal overall operating cost of the electricity spot market, and further improves the economy and safety of spot market operations while achieving the goal of maximizing social welfare. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 This is a flow chart of a spot trading optimization method considering the uncertainty of renewable energy provided by an embodiment of the present invention;

[0072] Figure 2 This is a structural principle block diagram of a spot trading optimization device that takes into account the uncertainty of renewable energy, provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0073] The technical solution of the present invention is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present application and the specific features in the embodiments are detailed descriptions of the technical solution of the present application, rather than limitations on the technical solution of the present application. Unless there is a conflict, the embodiments of the present application and the technical features in the embodiments can be combined with each other.

[0074] The term "and / or" in this document simply describes a relationship between related objects, indicating that three possible relationships exist. For example, "A and / or B" can mean: A exists alone, A and B exist simultaneously, or B exists alone. Additionally, the character " / " in this document generally indicates an "or" relationship between the related objects.

[0075] Example 1:

[0076] like Figure 1 As shown, this embodiment introduces a spot trading optimization method considering the uncertainty of renewable energy, which specifically includes the following steps:

[0077] S101, obtaining historical output data of renewable energy;

[0078] Among them, renewable energy includes wind farm energy and photovoltaic energy; historical output data includes wind farm historical output data and photovoltaic historical output data.

[0079] S102, constructing an uncertainty set using the historical output data through a data-driven robust optimization model;

[0080] It should be noted that the data-driving robust optimization (DDRO) optimization model is based on constructing an uncertainty set using historical data. Data-driven robust optimization does not require the probability distribution of uncertainty parameters, but only requires sufficient historical data. As the amount of historical data increases, the uncertainty set better encompasses all possible uncertain variables; the target value is less conservative, achieving better optimality while ensuring the robustness of the solution.

[0081] For a given wind farm output error historical data Historical data of photovoltaic output error , the system has The output data of different renewable energy sources are independent of each other. Wind power output error In the interval Similarly, the photovoltaic output error In the interval In this paper, the wind power output error range is the same as the photovoltaic output error range, that is, .

[0082] S103, constructing a spot trading optimization model based on the uncertainty set, an objective function with the goal of maximizing social welfare, and constraints corresponding to the objective;

[0083] In the embodiment of the present invention, an objective function is constructed with the goal of maximizing social welfare, and constraints corresponding to the objective function are used to construct a spot trading optimization model; wherein the objective function is:

[0084]

[0085] Where, Indicates the total number of time periods considered, with each 15-minute period being a time period; Indicates the number of units; Indicates the unit In the period contribution; 、 、 Respectively for units In the period Operating costs, startup costs, and shutdown costs, including unit operating costs It is a multi-segment linear function related to the output ranges declared by the unit and the corresponding energy prices; is the penalty factor for network constraint relaxation used for market clearing optimization; 、 Line Forward and reverse power flow slack variables; is the total number of lines; 、 Section Forward and reverse power flow slack variables; The total number of sections.

[0086] S104, solving the spot trading optimization model to obtain an optimization result.

[0087] Finally, the optimal solution of the spot trading optimization model is obtained through solving, and the optimization results are output.

[0088] The working principle of the spot trading optimization method of the present invention that takes into account the uncertainty of renewable energy is as follows: using historical data of wind power and photovoltaic power plants to obtain the uncertainty set of wind power and photovoltaic output, with the goal of maximizing the social welfare of both buyers and sellers, considering multiple constraints such as unit ramp-up rate, unit output upper and lower limits, transmission line transmission capacity constraints and decision variable limit constraints as boundary conditions, and establishing a corresponding mathematical model.

[0089] The present invention fully considers the uncertainty of renewable energy output, effectively absorbs the volatility of renewable energy output, and constructs an uncertainty set of renewable energy output through data-driven theory, which takes into account the economy and safety of system operation and achieves the goal of maximizing social welfare.

[0090] As an embodiment of the present invention, in step S102, using the historical output data to construct an uncertainty set through a data-driven robust optimization model includes:

[0091] Historical wind farm output data and photovoltaic output data are fed into a pre-built robust optimization model to generate uncertainty sets for wind power output and photovoltaic output. It should be noted that the uncertainty sets are derived using existing data-driven robust optimization algorithms in this embodiment of the present invention, and this application will not elaborate further here.

[0092] As an embodiment of the present invention, the constraint conditions corresponding to the target in step S103 include:

[0093] (1) System load balancing constraints,

[0094]

[0095] Where, For the crew In the period of efforts, For contact lines In the period The planned power, is the total number of contact lines, For the period System load;

[0096] (2) System positive reserve capacity constraint,

[0097]

[0098] Where, Indicates the unit In the period The start-stop state, Indicates that the unit is shut down. Indicates that the unit is turned on; Indicates the unit In the period Maximum output; For the period System positive spare capacity requirements;

[0099] (3) System negative reserve capacity constraint,

[0100]

[0101] Where, Indicates the unit In the period The minimum output; For the period System negative reserve capacity requirements;

[0102] (4) System spinning reserve constraints,

[0103]

[0104] Where, For the crew Maximum climbing rate, For the crew Maximum downhill climbing rate; 、 The units In the period Maximum and minimum output; 、 Periods Adjusting spinning reserve requirements upwards or downwards;

[0105] (5) Special unit state constraints,

[0106]

[0107] Where, Refers to the set of units that must be turned on;

[0108] (6) Upper and lower limits of unit output,

[0109]

[0110] (7) Upper and lower limits of the unit group output,

[0111]

[0112] Where, For crew groups In the period The minimum output, For crew groups In the period Maximum output;

[0113] (8) Unit climbing constraints,

[0114]

[0115] (9) Minimum continuous start and stop time constraints for units,

[0116]

[0117] Where, The minimum continuous start time of the unit; The minimum continuous downtime of the unit; 、 For the crew In the period The continuous startup time and shutdown time;

[0118] (10) Uncertainty set constraints on renewable energy,

[0119]

[0120] Where, represents the set of renewable energy uncertainty constraints, For an uncertain set Mid-scene Renewable energy units Output deviation occurs probability; Renewable energy units In the scene Medium output deviation; It is the lower limit of the output deviation of new energy; It is the upper limit of the deviation of the output of new energy; is the minimum output of new energy unit i in period t, is the maximum output of new energy unit i in period t, is the predicted output of new energy unit i in period t;

[0121] (11) Line flow constraints,

[0122]

[0123] Where, For the line The power transmission limit of For the crew Node to line The generator output power transfer distribution factor; For the contact line Node to line The generator output power transfer distribution factor; For nodes Line The generator output power transfer distribution factor; For nodes In the period Bus load value; 、 Line Forward and reverse power flow slack variables;

[0124] (12) Sectional tidal flow constraints,

[0125]

[0126] Where, For cross section The lower limit of power flow transmission, For cross section The upper limit of power flow transmission; For the crew Node section The generator output power transfer distribution factor; For the contact line Node section The generator output power transfer distribution factor; For nodes Cross-section The generator output power transfer distribution factor; For nodes In the period Bus load value; 、 Section The forward and reverse power flow slack variables.

[0127] As an embodiment of the present invention, the step S104 of solving the spot trading optimization model to obtain the optimization result includes:

[0128] CPLEX is used to solve the spot trading optimization model to obtain the unit segment winning quantity, unit start and shutdown status, and grid-wide node electricity price information for each trading period.

[0129] The embodiment of the present invention uses an uncertainty set constructed from historical data on renewable energy output and a data-driven spot trading optimization model that considers the uncertainty of renewable energy, as well as corresponding objective functions and constraints. The model is a mixed integer linear programming model that can be solved using mature commercial solvers such as CPLEX to ultimately obtain market trading results, namely the unit's segmented winning bid quantity and the unit's on / off status.

[0130] In summary, the spot trading optimization method considering the uncertainty of renewable energy provided by the embodiment of the present invention, first, based on data-driven mathematical theory, proposes a data-driven robust optimization spot trading optimization model that takes into account the uncertainty of wind power and photovoltaic output, which takes into account both the economy and safety of the system; in actual operation, the optimal solution of the data-driven model shows strong robustness, and is almost completely immune to the uncertainty of renewable energy output. Moreover, compared with the traditional box-type robust optimization model, the data-driven robust optimization model adopted in this embodiment, the data-driven day-ahead spot market clearing model, takes into account the economy of the system on the basis of good security, and its comprehensive performance is better than the traditional box-type robust optimization set; second, it is the first time that the data-driven robust optimization theory is introduced into the day-ahead spot market trading model, and effective countermeasures are proposed for the problem that the traditional day-ahead spot trading optimization model cannot consider the uncertainty of renewable energy output, thereby further improving the economy and safety of spot market operations.

[0131] Example 2:

[0132] like Figure 2 As shown, the embodiment of the present invention provides a spot trading optimization device that takes into account the uncertainty of renewable energy, which can be used to implement the method described in Example 1, specifically including:

[0133] An acquisition module 201 is used to acquire historical output data of renewable energy;

[0134] A determination module 202 is configured to construct an uncertainty set using the historical output data through a data-driven robust optimization model;

[0135] A construction module 203 is configured to construct a spot trading optimization model based on the uncertainty set, an objective function with the goal of maximizing social welfare, and boundary conditions such as the unit ramp-up rate, the unit output upper and lower limits, the transmission capacity constraint of the transmission line, and the decision variable limitation constraint;

[0136] The solution module 204 is used to solve the spot transaction optimization model to obtain an optimization result.

[0137] The working principle of the spot trading optimization device considering the uncertainty of renewable energy provided by the embodiment of the present invention is as follows: the acquisition module 201 obtains the historical output data of renewable energy; the determination module 202 constructs an uncertainty set using the historical output data based on the data-driven robust optimization model; the construction module 203 constructs a spot trading optimization model based on the uncertainty set, with the goal of maximizing social welfare, and with the unit ramp-up rate, unit output upper and lower limits, transmission line transmission capacity constraints and decision variable restriction constraints as boundary conditions; the solution module 204 solves the spot trading optimization model to obtain an optimization result.

[0138] The spot trading optimization device considering the uncertainty of renewable energy provided in the embodiment of the present invention and the spot trading optimization method considering the uncertainty of renewable energy provided in Example 1 are based on the same technical concept, and can produce the beneficial effects as described in Example 1. For the contents not fully described in this embodiment, please refer to Example 1.

[0139] Example 3:

[0140] An embodiment of the present invention provides a spot trading optimization device that considers the uncertainty of renewable energy, including a processor and a storage medium;

[0141] The storage medium is used to store instructions;

[0142] The processor is configured to operate according to the instruction to execute the steps of any one of the methods in embodiment 1.

[0143] An embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of any one of the methods in the first embodiment are implemented.

[0144] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0145] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0146] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0147] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0148] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A spot trading optimization method considering the uncertainty of renewable energy, characterized by: The method comprises: Obtain historical output data of renewable energy; Constructing an uncertainty set using the historical output data through a data-driven robust optimization model; Based on the uncertain set, an objective function with the goal of maximizing social welfare and constraints corresponding to the objective are used to construct a spot trading optimization model; Solving the spot trading optimization model to obtain an optimization result; The objective function is: ， Where, Indicates the total number of time periods considered, with each 15-minute period being a time period; Indicates the number of units; Indicates the unit In the period contribution; 、 、 Respectively for units In the period Operating costs, startup costs, and shutdown costs, including unit operating costs It is a multi-segment linear function related to the output ranges declared by the unit and the corresponding energy prices; is the penalty factor for network constraint relaxation used for market clearing optimization; 、 Line Forward and reverse power flow slack variables; is the total number of lines; 、 Section Forward and reverse power flow slack variables; is the total number of sections; The constraints corresponding to the target include: System load balancing constraints, ， Where, For the crew In the period of efforts, For contact lines In the period The planned power, is the total number of contact lines, For the period System load; The system has positive spare capacity constraints, ， Where, Indicates the unit In the period The start-stop state, Indicates that the unit is shut down. Indicates that the unit is turned on; Indicates the unit In the period Maximum output; For the period System positive spare capacity requirements; System negative spare capacity constraint, ， Where, Indicates the unit In the period The minimum output; For the period System negative reserve capacity requirements; System spinning reserve constraints, ， Where, For the crew Maximum climbing rate, For the crew Maximum downhill climbing rate; 、 The units In the period Maximum and minimum output; 、 Periods Adjusting spinning reserve requirements upwards or downwards; Special unit state constraints, ， Where, Refers to the set of units that must be turned on; Upper and lower limits of unit output, ， Upper and lower limit constraints on the unit group output, ， Where, For crew groups In the period The minimum output, For crew groups In the period Maximum output; Unit climbing constraint, ， Minimum continuous start and stop time constraints for units, ， Where, The minimum continuous start time of the unit; The minimum continuous downtime of the unit; 、 For the crew In the period The continuous startup time and shutdown time; Renewable energy uncertainty set constraints, ， Where, represents the set of renewable energy uncertainty constraints, For an uncertain set Mid-scene Renewable energy units Output deviation occurs probability; Renewable energy units In the scene Medium output deviation; It is the lower limit of the output deviation of new energy; It is the upper limit of the deviation of the output of new energy; is the minimum output of new energy unit i in period t, is the maximum output of new energy unit i in period t, is the predicted output of new energy unit i in period t; Line flow constraints, ， Where, For the line The power transmission limit of For the crew Node to line The generator output power transfer distribution factor; For contact lines Node to line The generator output power transfer distribution factor; For nodes Line The generator output power transfer distribution factor; For nodes In the period Bus load value; 、 Line Forward and reverse power flow slack variables; Sectional tidal flow constraints, ， Where, For cross section The lower limit of power flow transmission, For cross section The upper limit of power flow transmission; For the crew Node section The generator output power transfer distribution factor; For contact lines Node section The generator output power transfer distribution factor; For nodes Cross-section The generator output power transfer distribution factor; For nodes In the period Bus load value; 、 Section The forward and reverse power flow slack variables.

2. The spot trading optimization method considering the uncertainty of renewable energy according to claim 1 is characterized in that: The historical output data includes: Historical output data of wind farms and photovoltaic power plants.

3. The spot trading optimization method considering the uncertainty of renewable energy according to claim 2 is characterized in that: The method of constructing an uncertainty set using the historical output data through a data-driven robust optimization model includes: The historical output data of the wind farm and the historical output data of the photovoltaic power plant are input into a pre-built data-driven robust optimization model for solution, thereby obtaining an uncertainty set of the wind power output and an uncertainty set of the photovoltaic output.

4. The spot trading optimization method considering the uncertainty of renewable energy according to claim 1 is characterized in that: The renewable energy sources include multiple ones, and the output data of different renewable energy sources are independent of each other.

5. The spot trading optimization method considering the uncertainty of renewable energy according to claim 1 is characterized in that: Solving the spot trading optimization model to obtain an optimization result includes: CPLEX is used to solve the spot trading optimization model to obtain the unit segment winning quantity, unit start and shutdown status, and grid-wide node electricity price information for each trading period.

6. A spot trading optimization device considering the uncertainty of renewable energy, characterized in that: include: An acquisition module is used to obtain historical output data of renewable energy; A determination module, configured to construct an uncertainty set using the historical output data through a data-driven robust optimization model; A construction module is used to construct a spot trading optimization model based on the uncertainty set, an objective function with the goal of maximizing social welfare, and boundary conditions such as the unit ramp-up rate, the unit output upper and lower limits, the transmission capacity constraint of the transmission line, and the decision variable limit constraint; A solution module, used to solve the spot trading optimization model and obtain an optimization result; The objective function is: ， Where, Indicates the total number of time periods considered, with each 15-minute period being a time period; Indicates the number of units; Indicates the unit In the period contribution; 、 、 Respectively for units In the period Operating costs, startup costs, and shutdown costs, including unit operating costs It is a multi-segment linear function related to the output ranges declared by the unit and the corresponding energy prices; is the penalty factor for network constraint relaxation used for market clearing optimization; 、 Line Forward and reverse power flow slack variables; is the total number of lines; 、 Section Forward and reverse power flow slack variables; is the total number of sections; The constraints corresponding to the target include: System load balancing constraints, ， Where, For the crew In the period of efforts, For the contact line In the period The planned power, is the total number of contact lines, For the period System load; The system has positive spare capacity constraints, ， Where, Indicates the unit In the period The start-stop state, Indicates that the unit is shut down. Indicates that the unit is turned on; Indicates the unit In the period Maximum output; For the period System positive spare capacity requirements; System negative spare capacity constraint, ， Where, Indicates the unit In the period The minimum output; For the period System negative reserve capacity requirements; System spinning reserve constraints, ， Where, For the crew Maximum climbing rate, For the crew Maximum downhill climbing rate; 、 The units In the period Maximum and minimum output; 、 Periods Adjusting spinning reserve requirements upwards or downwards; Special unit state constraints, ， Where, Refers to the set of units that must be turned on; Upper and lower limits of unit output, ， Upper and lower limit constraints on the unit group output, ， Where, For crew groups In the period The minimum output, For crew groups In the period Maximum output; Unit climbing constraint, ， Minimum continuous start and stop time constraints for units, ， Where, The minimum continuous startup time of the unit; The minimum continuous downtime of the unit; 、 For the crew In the period The continuous startup time and shutdown time; Renewable energy uncertainty set constraints, ， Where, represents the set of renewable energy uncertainty constraints, For an uncertain set Mid-scene Renewable energy units Output deviation occurs probability; Renewable energy units In the scene Medium output deviation; It is the lower limit of the output deviation of new energy; It is the upper limit of the deviation of the output of new energy; is the minimum output of new energy unit i in period t, is the maximum output of new energy unit i in period t, is the predicted output of new energy unit i in period t; Line flow constraints, ， Where, For the line The power transmission limit of For the crew Node to line The generator output power transfer distribution factor; For the contact line Node to line The generator output power transfer distribution factor; For nodes Line The generator output power transfer distribution factor; For nodes In the period Bus load value; 、 Line Forward and reverse power flow slack variables; Sectional tidal flow constraints, ， Where, For cross section The lower limit of power flow transmission, For cross section The upper limit of power flow transmission; For the crew Node section The generator output power transfer distribution factor; For contact lines Node section The generator output power transfer distribution factor; For nodes Cross-section The generator output power transfer distribution factor; For nodes In the period Bus load value; 、 Section The forward and reverse power flow slack variables.

7. A spot trading optimization device considering the uncertainty of renewable energy, characterized in that: including processors and storage media; The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the spot transaction optimization method considering renewable energy uncertainty according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the spot transaction optimization method considering the uncertainty of renewable energy are implemented as described in any one of claims 1 to 5.

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