Medium-and-long-term electricity market transaction optimization method and device based on participation of wind, light and water

Abstract

The invention relates to the field of medium-and-long-term electricity market transaction, in particular to a medium-and-long-term electricity market transaction optimization method and device based on participation of wind, light and water. The method comprises the following steps: collecting electricity market data; based on the electricity market data, constructing a medium-term business simulation model; based on the power market data, constructing a long-term optimization model embedded with the medium-term business simulation model; based on the electricity market data, constructing an evaluation index model; and based on the long-term optimization model and the evaluation index model, optimization of the output of the wind-light-water participated medium-and-long-term electricity market transaction is carried out.

Description

Technical Field

[0001] The present application relates to the field of medium- and long-term electricity market transactions, and specifically to a medium- and long-term electricity market transaction optimization method and device based on the participation of wind, solar and water. Background Art

[0002] At present, most studies focus on the optimization of short-term power market transactions, while there are few studies on the operation mode of hybrid power systems involving wind, solar, water and thermal in medium- and long-term power market transactions. Under the ever-changing power market rules, the lack of effective medium- and long-term operation modes will restrict market participants. Therefore, it is particularly important to study and develop medium- and long-term power market transaction optimization methods that adapt to changes in settlement rules. Summary of the invention

[0003] In view of this, the embodiments of the present application are committed to providing a method and device for optimizing medium- and long-term electricity market transactions based on the participation of wind, solar and water.

[0004] This application provides a medium- and long-term electricity market transaction optimization method based on the participation of wind, solar and water, including:

[0005] Collect electricity market data;

[0006] Based on the electricity market data, construct a medium-term business simulation model;

[0007] Based on the electricity market data, construct a long-term optimization model embedded with the medium-term business simulation model;

[0008] Based on the electricity market data, construct an evaluation index model;

[0009] Based on the long-term optimization model and the evaluation index model, the output of medium- and long-term electricity market transactions involving wind, solar and hydropower is optimized.

[0010] In some embodiments, in the mid-term business simulation model: the total revenue formula for the mth month is as follows:

[0011]

[0012] in, is the total income of the mth month; is the contract amount for the mth month; is the liquidated damages for the mth month; is the deduction due to supply shortage in month m;

[0013]

[0014] Where l is the load period index, and pk, ft, and vl are used to represent the peak, flat, and valley load periods, respectively. lIt refers to the time-of-use electricity price during load period l. is the planned delivered power for load period l in the mth month. is the penalty for load period l in month m. is the deduction for load period l in the mth month.

[0015] In some embodiments, if the difference between the planned supply amount and the actual supply amount is ΔΩ m,l is not equal to 0, the amount directly deducted from the supply shortage will be adjusted upward in different periods. The deduction amount can be calculated by formula (5). m,l | If the threshold for exemption from penalty is exceeded, a penalty will be imposed, which can be estimated by formula (6).

[0016]

[0017]

[0018] in, is the actual delivered electricity in load period l in the mth month; is the penalty electricity in load period l in the mth month; is the deduction for load period l in month m; is the penalty in load period l in the mth month; α is the penalty price coefficient for supply shortage; δ is the coefficient for supply shortage exemption penalty; λ w The contract weighted average electricity price.

[0019] In some embodiments, the power balance is as follows:

[0020]

[0021] in, is the actual delivered power at time t on day n of month m; and It is the renewable energy power generation, hydropower generation, pumping power consumption and power reduction at time t on the nth day of the mth month.

[0022] Renewable energy output is set as the sum of photovoltaic and wind energy output.

[0023]

[0024] in, are the normalized monitoring output data of the wind farm and photovoltaic power station at the tth hour on the nth day of the mth month; C New is the installed capacity of renewable energy; β is the proportion of wind power capacity to the total capacity of renewable energy.

[0025] In some embodiments, net load may be defined as the difference between the renewable energy output and the daily target transmission power.

[0026]

[0027] in, is the target transmission power at time t on day n of month m; Describes the net load at time t on day n of month m; It is used to represent the operating status coefficient at time t on the nth day of the mth month. -1 indicates the pumping condition; if is 1, representing power generation conditions; if If it is 0, it means neither pumping water nor generating electricity.

[0028] The operation model of the hydropower unit is as follows.

[0029]

[0030] in, and are the maximum hydraulic output and the maximum power consumption of the pump at time t on the nth day of the mth month, respectively; η hydro and η pump They are the total power generation efficiency of the hydroelectric system and the total pumping efficiency of the pumping system; and are the average hydroelectric power generation head and average pumping head at time t on the nth day of the mth month, respectively; is the average water storage capacity of the upper reservoir at time t-1 on the nth day of the mth month; and is the minimum and maximum water storage capacity of the upper reservoir at the tth time on the nth day; Δt is the scheduling interval.

[0031] In some embodiments, the optimization objective formula of the long-term optimization model is as follows:

[0032]

[0033] in, The total annual income.

[0034] In some embodiments, the relationship between the load period and the corresponding load sub-period is as follows:

[0035]

[0036] in, is the planned delivered power in the sth load sub-period of load period l in the mth month. l is the set of load sub-periods of load period l;

[0037]

[0038] in, is the target transmission power of the system at time t on day n in month m; T m,l,s is the duration of a load subperiod in load period l in the mth month. N is the number of days in the mth month; is the set of dispatch intervals in the sth load subperiod of load period l in the mth month; and They are the target transmission power of the system during the peak, flat or valley load period on the nth day of the mth month.

[0039] In some embodiments, the power output constraints are as follows:

[0040]

[0041] in, and It is the minimum hydraulic output and minimum pumping power consumption at the tth time on the nth day of the mth month.

[0042] In some embodiments, the formula of the evaluation index in the evaluation index model is as follows:

[0043] Electricity reduction:

[0044] Ω Srp =∑P t srp ×Δt (20)

[0045] Reduction ratio:

[0046]

[0047] Among them, F sr p is the reduction ratio; srp is the amount of power reduction.

[0048] Probability of shortage event:

[0049]

[0050] Among them, p stg is the probability of a shortage event; T stg and T are the duration of the shortage event and the total duration.

[0051] Monthly maximum supply shortage rate:

[0052]

[0053] Among them, Fstg is the maximum monthly supply shortage rate.

[0054] The present application also provides a medium- and long-term electricity market transaction optimization device based on the participation of wind, solar and water, including:

[0055] A collection module for collecting power market data;

[0056] A construction module, for constructing a medium-term business simulation model based on the power market data; constructing a long-term optimization model embedded with the medium-term business simulation model based on the power market data; and constructing an evaluation index model based on the power market data;

[0057] The optimization module is used to optimize the output of medium- and long-term electricity market transactions involving wind, solar and hydropower based on the long-term optimization model and the evaluation index model.

[0058] The present application provides a medium- and long-term electricity market transaction optimization method based on the participation of wind, solar and water, which collects electricity market data; constructs a medium-term business simulation model based on the electricity market data; constructs a long-term optimization model embedded with the medium-term business simulation model based on the electricity market data; constructs an evaluation index model based on the electricity market data; and optimizes the output of medium- and long-term electricity market transactions involving wind, solar and water based on the long-term optimization model and the evaluation index model. The medium-term business simulation model allows forecasting and planning for the upcoming months, providing the ability to respond quickly to market changes. By simulating different scenarios, the cost-effectiveness of various operational decisions can be evaluated, and resource allocation can be optimized. The long-term optimization model supports strategic planning based on annual goals, ensuring the consistency and realization of medium- and long-term goals; by embedding the medium-term model, the long-term model can more accurately predict and optimize annual revenue and maximize revenue. The long-term model can adapt to changes in market rules and provide flexible adjustment strategies to cope with changes in policies and market environment. Through evaluation indicators, the effects of different trading strategies can be more objectively evaluated, providing data support for decision-making. The evaluation results can guide the continuous improvement of operational processes and strategies to enhance market competitiveness; the optimization model can more effectively integrate wind, solar and hydropower resources, improve energy efficiency, help improve the economic benefits of electricity trading, reduce waste and increase revenue through precise output planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] By describing the embodiments of the present application in more detail in conjunction with the accompanying drawings, the above and other purposes, features and advantages of the present application will become more apparent. The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the accompanying drawings, the same reference numerals generally represent the same components or steps.

[0060] Figure 1 It is a flow chart of a method for optimizing medium- and long-term electricity market transactions based on the participation of wind, solar and hydropower provided in an embodiment of the present application.

[0061] Figure 2 This is a structural schematic diagram of a medium- and long-term electricity market transaction optimization device based on the participation of wind, solar and water provided in an embodiment of the present application.

[0062] Figure 3 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0063] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0064] Renewable energy technologies such as hydropower and new energy (wind or solar) can help reduce carbon emissions and mitigate the impact of climate change. Among them, wind power and photovoltaic power generation pose challenges to the stability and security of power generation systems and corresponding power grids due to their volatility, intermittency and uncertainty. Combining hydropower and gas power generation with wind or photovoltaic power generation to form a hybrid power system is an effective solution to improve the quality of renewable energy supply. In terms of the market, the Chinese government is vigorously developing the power trading market, aiming to break the market monopoly of the power grid. It is hoped that the power generation company's income will be further increased by taking advantage of the difference in power prices between provinces rich in renewable energy and major power receiving provinces (especially in the medium and long term). If the rules change, the medium and long-term power supply planning strategy should be adjusted accordingly. Most studies do not provide a suitable medium and long-term operating model for a hybrid power system involving wind, light, water and fire. Under the changing power market rules, this will limit the maximum income of market participants. Therefore, it is worth studying the medium and long-term delivery strategy to adapt to changes in settlement rules.

[0065] In order to solve the above problems, the present invention proposes a medium-term (monthly) operation simulation model, which embeds the medium-term (monthly) operation simulation model into the long-term (annual) optimization model, with the aim of maximizing annual revenue. According to the variable power settlement rules (including electricity charges and penalties), the provided electricity is used as the decision variable for the medium- and long-term operation strategy. By allocating the planned delivered electricity, the complexity and uncertainty of the information flow from the power generation side to the power grid side are reduced; the number of decision variables is reasonable and greatly reduced, which can improve the simulation efficiency of the system optimization model; and it can also compromise on the deficiencies in the medium- and long-term forecast of new energy output. Therefore, the present invention can be widely used in the field of medium- and long-term electricity market transactions.

[0066] After introducing the basic principles of the present application, various non-limiting embodiments of the present application will be described in detail with reference to the accompanying drawings.

[0067] Exemplary Methods

[0068] Figure 1 This is a flow chart of a method for optimizing medium- and long-term electricity market transactions based on wind, solar, and hydropower participation provided by an embodiment of the present application. Figure 1 As shown, the method includes the following contents.

[0069] Step S110, collecting electricity market data;

[0070] In this step, key data needs to be collected from the electricity market, which may include but is not limited to:

[0071] Price data: Historical and real-time electricity market prices, including peak and valley prices.

[0072] Supply and demand data: The supply and demand of electricity, and how these trends change over time.

[0073] Weather data: Weather conditions such as wind speed and sunshine that affect renewable energy generation.

[0074] Policies and regulations: Market rules, policy changes, and their impact on the market.

[0075] Equipment status: operating status and maintenance plan of power generation equipment.

[0076] This data is collected to ensure that subsequent models can be built and optimized based on the most accurate and up-to-date market information.

[0077] Step S120, constructing a mid-term business simulation model based on the power market data;

[0078] In this step, the data collected in step S110 is used to build a mid-term business simulation model, which will:

[0079] Predict market trends: Forecast electricity demand and price changes in the coming months based on historical data.

[0080] Simulate different scenarios: Simulate different market scenarios, including extreme weather, equipment failures, etc., to assess their impact on the business.

[0081] Develop response strategies: Based on the simulation results, develop strategies to respond to market changes, such as adjusting power generation plans or purchasing additional electricity.

[0082] Step S130, constructing a long-term optimization model embedded with the medium-term business simulation model based on the power market data;

[0083] In this step, the medium-term business simulation model is embedded into the long-term optimization model to achieve:

[0084] Annual goal setting: Based on annual business goals, such as maximizing revenue or minimizing costs, set long-term optimization goals.

[0085] Optimal resource allocation: Optimize the allocation of wind, solar and hydroelectric resources to achieve long-term goals.

[0086] Adapt to market changes: Enable the model to adapt to changes in market rules, such as electricity price adjustments or policy changes.

[0087] Step S140, constructing an evaluation index model based on the power market data;

[0088] The technical indicators of the evaluation mainly involve two aspects, namely, reduction, such as reduction ratio and electricity reduction amount; supply shortage, such as the possibility of shortage events, the maximum monthly supply shortage ratio and penalties.

[0089] Step S150, based on the long-term optimization model and the evaluation index model, optimize the output of medium- and long-term electricity market transactions involving wind, solar and hydropower.

[0090] In this step, the long-term optimization model and the evaluation index model are combined to optimize the output:

[0091] Optimize output plan: According to the long-term optimization model, formulate the output plan of wind, solar and hydropower generation to maximize economic benefits and meet market requirements.

[0092] Real-time adjustment: Based on real-time market data and forecasts from mid-term business simulation models, output plans are adjusted in real time to respond to market changes.

[0093] Performance monitoring: Use evaluation indicator models to monitor optimization results, ensure that transactions meet expected goals, and make necessary adjustments based on evaluation results.

[0094] These steps will ensure that electricity market transactions are efficient, economical and sustainable in the medium and long term, while being able to adapt to changes in the market and environment.

[0095] Specifically, the establishment of a mid-term (monthly) business simulation model is as follows:

[0096] Generally speaking, the difference between the actual power supply and the planned power supply will lead to oversupply or shortage. Oversupply can be avoided by actively reducing or increasing local consumption and other measures. In contrast, power supply shortage has a greater impact on the operational security of the power grid, which is an inducement for settlement penalties. It is assumed that the planned power delivery is greater than or equal to the actual power delivery. Potential energy losses caused by excessive new energy production will be regarded as energy reduction.

[0097]

[0098] in, is the total income of the mth month; is the contract amount for the mth month; is the liquidated damages for the mth month; is the deduction due to supply shortage in month m.

[0099]

[0100] Where l is the load period index, and pk, ft, and vl are used to represent the peak, flat, and valley load periods, respectively. l It refers to the time-of-use electricity price during load period l. is the planned delivered power for load period l in the mth month. is the penalty for load period l in month m. is the deduction for load period l in the mth month.

[0101] There are three possible situations.

[0102] (1) Ideal supply: ΔΩ m,l =0

[0103] (2) Insufficient supply without penalty: ΔΩ m,l <0 and

[0104] (3) Insufficient supply and punished: ΔΩ m,l <0 and

[0105] If the difference between the planned supply and the actual supply is ΔΩ m,l If it is not equal to 0, the amount directly deducted from the supply shortage will be adjusted upwards in different periods. The deduction amount can be calculated using formula (5).

[0106] If |ΔΩ m,l | If the threshold for exemption from penalty is exceeded, a penalty will be imposed, which can be estimated by formula (6).

[0107]

[0108]

[0109] in, is the actual delivered electricity in load period l in the mth month; is the penalty electricity in load period l in the mth month; is the deduction for load period l in month m; is the penalty in load period l in the mth month; α is the penalty price coefficient for supply shortage; δ is the coefficient for supply shortage exemption penalty; λ w The contract weighted average electricity price.

[0110] The power balance is as follows:

[0111]

[0112] in, is the actual delivered power at time t on day n of month m; and It is the renewable energy power generation, hydropower generation, pumping power consumption and power reduction at time t on the nth day of the mth month.

[0113] Renewable energy output is set as the sum of photovoltaic and wind energy output.

[0114]

[0115] in, are the normalized monitoring output data of the wind farm and photovoltaic power station at the tth hour on the nth day of the mth month; C New is the installed capacity of renewable energy; β is the proportion of wind power capacity to the total capacity of renewable energy.

[0116] The present invention assumes that new energy sources have priority in grid connection. In addition, net load can be defined as the difference between the output of new energy sources and the daily target transmission power.

[0117]

[0118] in, is the target transmission power at time t on day n of month m; Describes the net load at time t on day n of month m; It is used to represent the operating status coefficient at time t on the nth day of the mth month. -1 indicates the pumping condition; if is 1, representing power generation conditions; if If it is 0, it means neither pumping water nor generating electricity.

[0119] The operation model of the hydropower unit is as follows.

[0120]

[0121] in, and are the maximum hydraulic output and the maximum power consumption of the pump at time t on the nth day of the mth month, respectively; η hydro and η pump They are the total power generation efficiency of the hydroelectric system and the total pumping efficiency of the pumping system; and are the average hydroelectric power generation head and average pumping head at time t on the nth day of the mth month, respectively; is the average water storage capacity of the upper reservoir at time t-1 on the nth day of the mth month; and is the minimum and maximum water storage capacity of the upper reservoir at the tth time on the nth day; Δt is the scheduling interval.

[0122] Furthermore, a long-term (annual) optimization model is established.

[0123] For power system stakeholders, annual revenue can be the primary optimization objective.

[0124]

[0125] in, The total annual income.

[0126] Generally speaking, there may be several peaks, flats, or valleys in a day, which are called "load sub-periods". The relationship between the load period and the corresponding load sub-period is as follows.

[0127]

[0128] in, is the planned delivered power in the sth load sub-period of load period l in the mth month. l is the set of load sub-periods of load period l.

[0129] In the present invention, First, we need to evenly distribute it to each day, and then evenly distribute it to each scheduling interval, and get Right now

[0130]

[0131] in, is the target transmission power of the system at time t on day n in month m; T m,l,s is the duration of a load subperiod in load period l in the mth month. N is the number of days in the mth month;

[0132] is the set of dispatch intervals in the sth load subperiod of load period l in the mth month; and They are the target transmission power of the system during the peak, flat or valley load period on the nth day of the mth month.

[0133] This allocation reduces the complexity and uncertainty of information flow from the power generation side to the grid side; the number of decision variables is reasonable and greatly reduced, which can improve the simulation efficiency of the system optimization model; and it can also make compromises on the shortcomings of medium- and long-term predictions of new energy output.

[0134] The power output constraints are as follows.

[0135]

[0136] in, and It is the minimum hydraulic output and minimum pumping power consumption at the tth time on the nth day of the mth month.

[0137] Furthermore, an evaluation index model is established.

[0138] The technical indicators of the evaluation mainly involve two aspects, namely, reduction, such as reduction ratio and power reduction amount; supply shortage, such as the possibility of shortage events, the maximum monthly supply shortage ratio and penalties, etc. The formula of the evaluation indicator is as follows.

[0139] Electricity reduction:

[0140]

[0141] Reduction ratio:

[0142]

[0143] Among them, F sr p is the reduction ratio; srp is the amount of power reduction.

[0144] Probability of shortage event:

[0145]

[0146] Among them, p stg is the probability of a shortage event; T stgand T are the duration of the shortage event and the total duration.

[0147] Monthly maximum supply shortage rate:

[0148]

[0149] Among them, F stg is the maximum monthly supply shortage rate.

[0150] In summary, in the solution provided by this application, a medium-term (monthly) operation simulation model is embedded in a long-term (annual) optimization model with the aim of maximizing annual revenue. According to the variable power settlement rules (including electricity charges and penalties), the provided electricity is used as a decision variable for the medium- and long-term operation strategy. By allocating the planned delivered electricity, the complexity and uncertainty of the information flow from the power generation side to the power grid side are reduced; the number of decision variables is reasonable and greatly reduced, which can improve the simulation efficiency of the system optimization model; and compromises can also be made on the deficiencies in the medium- and long-term forecasts of new energy output. Therefore, the present invention can be widely used in the field of medium- and long-term electricity market transactions.

[0151] Exemplary Devices

[0152] The device embodiments of the present application can be used to execute the method embodiments of the present application. For details not disclosed in the device embodiments of the present application, please refer to the method embodiments of the present application.

[0153] Figure 2 The figure shows a block diagram of a medium- and long-term electricity market transaction optimization device based on wind, solar and water participation provided by an embodiment of the present application. Figure 2 As shown, the device comprises:

[0154] A collection module 21, used for collecting power market data;

[0155] A construction module 22 is used to construct a medium-term business simulation model based on the power market data; to construct a long-term optimization model embedded with the medium-term business simulation model based on the power market data; and to construct an evaluation index model based on the power market data;

[0156] The optimization module 23 is used to optimize the output of medium- and long-term electricity market transactions involving wind, solar and hydropower based on the long-term optimization model and the evaluation index model.

[0157] Exemplary Electronic Devices

[0158] Below, reference Figure 3 To describe an electronic device according to an embodiment of the present application. Figure 3 A block diagram of an electronic device according to an embodiment of the present application is illustrated.

[0159] like Figure 3 As shown, electronic device 300 includes one or more processors 310 and memory 320 .

[0160] The processor 310 may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device 300 to perform desired functions.

[0161] The memory 320 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory (cache), etc. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 310 may run the program instructions to implement the medium- and long-term electricity market transaction optimization method based on wind, solar, and water participation of the various embodiments of the present application described above and / or other desired functions. Various contents such as category correspondences may also be stored in the computer-readable storage medium.

[0162] In one example, the electronic device 300 may further include: an input device 330 and an output device 340 , and these components are interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0163] In addition, the input device 330 may also include, for example, a keyboard, a mouse, an interface, etc. The output device 340 may output various information to the outside, including analysis results, etc. The output device 340 may include, for example, a display, a speaker, a printer, a communication network and a remote output device connected thereto, etc.

[0164] Of course, to simplify, Figure 3 Only some of the components in the electronic device related to the present application are shown, and components such as a bus, an input / output interface, etc. are omitted. In addition, the electronic device may further include any other appropriate components according to specific application conditions.

[0165] Exemplary computer program products and computer-readable storage media

[0166] In addition to the above-mentioned methods and devices, an embodiment of the present application may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps of the medium- and long-term electricity market transaction optimization method based on the participation of wind, solar and water according to various embodiments of the present application described in the above "Exemplary Method" section of this specification.

[0167] The computer program product may be written in any combination of one or more programming languages ​​to write program codes for performing the operations of the embodiments of the present application, including object-oriented programming languages, such as Java, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as an independent software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0168] In addition, an embodiment of the present application may also be a computer-readable storage medium on which computer program instructions are stored. When the computer program instructions are executed by a processor, the processor executes the steps of the medium- and long-term electricity market transaction optimization method based on the participation of wind, solar and water according to various embodiments of the present application described in the above "Exemplary Method" section of this specification.

[0169] The computer readable storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can include, for example, but is not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0170] The above description has been given for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present application to the forms disclosed herein. Although multiple example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions and sub-combinations thereof.

Claims

1. A medium- and long-term electricity market transaction optimization method based on the participation of wind, solar and water, characterized in that: include: Collect electricity market data; Based on the electricity market data, construct a medium-term business simulation model; Based on the electricity market data, construct a long-term optimization model embedded with the medium-term business simulation model; Based on the electricity market data, construct an evaluation index model; Based on the long-term optimization model and the evaluation index model, the output of medium- and long-term electricity market transactions involving wind, solar and hydropower is optimized.

2. The medium- and long-term electricity market transaction optimization method based on wind, solar and water participation according to claim 1 is characterized in that: In the mid-term business simulation model: the total income formula for the mth month is as follows: in, is the total income of the mth month; is the contract amount for the mth month; is the liquidated damages for the mth month; is the deduction due to supply shortage in month m; Among them, λ l It refers to the time-of-use electricity price during load period l; is the planned delivered power in load period l in month m; is the penalty for load period l in the mth month; is the deduction for load period l in the mth month.

3. The medium- and long-term electricity market transaction optimization method based on wind, solar and water participation according to claim 2 is characterized in that: If the difference between the planned supply and the actual supply is ΔΩ m,l is not equal to 0, the amount directly deducted from the supply shortage will be adjusted upward in different periods; the deduction amount can be calculated by formula (5); if |ΔΩ m,l | If the threshold for exemption from punishment is exceeded, a penalty will be imposed, which can be estimated by formula (6); in, is the actual delivered power in load period l in the mth month; is the penalty electricity in load period l in the mth month; is the deduction for load period l in month m; is the penalty in load period l in the mth month; α is the penalty price coefficient for supply shortage; δ is the coefficient for supply shortage exemption penalty; λ w The contract weighted average electricity price.

4. The method for optimizing medium- and long-term electricity market transactions based on wind, solar, and water participation according to claim 3 is characterized in that: The power balance is as follows: in, is the actual delivered power at time t on day n of month m; and It is the renewable energy power generation, hydropower generation, pumping power consumption and power reduction at time t on the nth day of the mth month; New energy output is set as the sum of photovoltaic and wind output; in, are the normalized monitoring output data of the wind farm and photovoltaic power station at the tth hour on the nth day of the mth month; C New is the installed capacity of renewable energy; β is the proportion of wind power capacity to the total capacity of renewable energy.

5. The method for optimizing medium- and long-term electricity market transactions based on wind, solar, and water participation according to claim 4 is characterized in that: Net load can be defined as the difference between the new energy output and the daily target transmission power; in, is the target transmission power at time t on day n of month m; Describes the net load at time t on day n of month m; It is used to represent the operating status coefficient at time t on the nth day of the mth month; if -1 indicates the pumping condition; if is 1, representing power generation conditions; if If it is 0, it means neither pumping water nor generating electricity; The operation model of the hydropower unit is as follows; in, and are the maximum hydraulic output and the maximum power consumption of the pump at time t on the nth day of the mth month, respectively; η hydro and η pump They are the total power generation efficiency of the hydroelectric system and the total pumping efficiency of the pumping system; and are the average hydroelectric power generation head and average pumping head at time t on the nth day of the mth month, respectively; is the average water storage capacity of the upper reservoir at time t-1 on the nth day of the mth month; and is the minimum and maximum water storage capacity of the upper reservoir at the tth time on the nth day; Δt is the scheduling interval.

6. The method for optimizing medium- and long-term electricity market transactions based on wind, solar, and water participation according to claim 5 is characterized in that: The optimization objective formula of the long-term optimization model is as follows: in, The total annual income.

7. The method for optimizing medium- and long-term electricity market transactions based on wind, solar, and water participation according to claim 6 is characterized in that: The relationship between the load period and the corresponding load sub-period is as follows: in, is the planned delivered power in the sth load sub-period of load period l in the mth month; U l is the set of load sub-periods of load period l; in, is the target transmission power of the system at time t on day n in month m; T m,l,s is the duration of a load subperiod in load period l in the mth month; N is the number of days in the mth month; is the set of dispatch intervals in the sth load subperiod of load period l in the mth month; and They are the target transmission power of the system during the peak, flat or valley load period on the nth day of the mth month.

8. The method for optimizing medium- and long-term electricity market transactions based on wind, solar, and water participation according to claim 7 is characterized in that: The power output constraints are as follows: in, and It is the minimum hydraulic output and minimum pumping power consumption at the tth time on the nth day of the mth month.

9. The method for optimizing medium- and long-term electricity market transactions based on wind, solar, and water participation according to claim 8 is characterized in that: The formula of the evaluation index in the evaluation index model is as follows: Electricity reduction: Reduction ratio: Among them, F sr p is the reduction ratio; srp is the amount of electricity reduction; Probability of shortage event: Among them, p stg is the probability of a shortage event; T stg and T is the duration of the shortage event and the total duration; Monthly maximum supply shortage rate: Among them, F stg is the maximum monthly supply shortage rate.

10. A medium- and long-term electricity market transaction optimization device based on wind, solar and water participation, characterized in that: include: A collection module for collecting power market data; A construction module, for constructing a medium-term business simulation model based on the power market data; Based on the electricity market data, construct a long-term optimization model embedded with the medium-term business simulation model; Based on the electricity market data, construct an evaluation index model; The optimization module is used to optimize the output of medium- and long-term electricity market transactions involving wind, solar and hydropower based on the long-term optimization model and the evaluation index model.

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