Optimized operation method and device for variable-speed pumped storage power station

Abstract

The invention relates to the technical field of power system operation optimization, and discloses an optimized operation method and device for a variable-speed pumped storage power station, and the method comprises the steps: building a day-ahead market bidding model; the day-ahead market bidding model aims at maximizing the total income of a new energy base provided with a variable-speed pumped storage power station in a day-ahead market and a real-time market; establishing a real-time market self-scheduling model; the real-time market self-scheduling model aims at maximizing the total income of the new energy base provided with the variable-speed pumped storage power station in the real-time market; and carrying out iterative solution on the two-stage operation optimization model comprising the day-ahead market bidding model and the real-time market self-scheduling model to obtain an optimization operation scheme of the variable-speed pumped storage power station. According to the method, the influence of the variable-speed pumped storage power station on the power market is fully considered, the operation efficiency and benefits of variable-speed pumped storage are improved, and scientific basis and reference are provided for dispatching operation of the variable-speed pumped storage power station matched with a new energy base.

Description

Technical Field

[0001] The present invention relates to the technical field of power system operation optimization, and in particular to an optimized operation method and device for a variable-speed pumped-storage power station. Background Art

[0002] With the rapid development of renewable energy, pumped storage plays an increasingly important role in the power system. Researching the optimal operation strategy of pumped storage participating in the power market is crucial to improving the economic benefits of pumped storage operators and the whole society.

[0003] However, the relevant research on power system operation optimization has not considered the variable-speed pumped storage operation optimization of supporting new energy bases under the power market environment, which reduces the operating efficiency and benefits of variable-speed pumped storage. Summary of the invention

[0004] In view of this, the present invention provides an optimized operation method and device for a variable-speed pumped-storage power station to solve the problem that the power system operation optimization process does not take into account the variable-speed pumped-storage operation optimization of the supporting new energy base under the power market environment, thereby reducing the operating efficiency and benefits of the variable-speed pumped-storage.

[0005] In a first aspect, the present invention provides a method for optimizing operation of a variable speed pumped storage power station, the method comprising:

[0006] Establish a day-ahead market bidding model; the day-ahead market bidding model aims to maximize the total revenue of a new energy base equipped with a variable-speed pumped storage power station in the day-ahead market and the real-time market;

[0007] Establish a real-time market self-dispatching model; the real-time market self-dispatching model aims to maximize the total revenue of the new energy base equipped with a variable-speed pumped storage power station in the real-time market;

[0008] The two-stage operation optimization model including the day-ahead market bidding model and the real-time market self-dispatching model is iteratively solved to obtain the optimal operation plan of the variable-speed pumped-storage power station.

[0009] The present embodiment provides an optimized operation method for a variable speed pumped storage power station. A day-ahead market bidding model is established with the goal of maximizing the total revenue of a new energy base equipped with a variable speed pumped storage power station in the day-ahead market and the real-time market. A real-time market self-dispatching model is established with the goal of maximizing the total revenue of a new energy base equipped with a variable speed pumped storage power station in the real-time market. Then, a two-stage operation optimization model including the day-ahead market bidding model and the real-time market self-dispatching model is iteratively solved. The impact of the variable speed pumped storage power station on the power market is fully considered, and the operation problems of the variable speed pumped storage power station in the power market environment are effectively solved, the operation efficiency and revenue of the variable speed pumped storage are improved, and a scientific basis and reference are provided for the dispatching and operation of the variable speed pumped storage power station supporting the new energy base.

[0010] In an optional implementation, a two-stage operation optimization model including a day-ahead market bidding model and a real-time market self-dispatching model is iteratively solved to obtain an optimal operation plan for a variable-speed pumped-storage power station, including:

[0011] Obtain the predicted power data of renewable energy and the market electricity price forecast data, process the predicted power data of renewable energy and the market electricity price forecast data respectively, and obtain the set of severe output scenarios of renewable energy, the day-ahead market electricity price data and the real-time market electricity price data;

[0012] Input the severe output scenario of new energy, the day-ahead market electricity price data and the real-time market electricity price data into the day-ahead market bidding model to obtain the power bidding amount of the new energy base in the day-ahead market and the lower bound of the two-stage operation optimization model;

[0013] The power bidding amount of the new energy base in the day-ahead market is input into the real-time market self-dispatching model to obtain the upper bound of the two-stage operation optimization model.

[0014] It is judged whether the lower bound and the upper bound of the two-stage operation optimization model meet the convergence conditions. Based on the judgment results, the day-ahead market bidding model and the real-time market self-dispatching model are alternately iterated to obtain the optimal operation plan of the variable-speed pumped-storage power station.

[0015] The present embodiment provides an optimized operation method for a variable speed pumped storage power station, which processes the predicted power data of renewable energy and the predicted market electricity price data respectively, obtains a set of severe output scenarios of renewable energy, day-ahead market electricity price data and real-time market electricity price data, and then calculates the lower bound and upper bound of a two-stage operation optimization model, and determines whether the lower bound of the two-stage operation optimization model and the upper bound of the two-stage operation optimization model meet the convergence conditions. The method fully considers the interaction and influence of the variable speed pumped storage power station on the power market and the output of renewable energy power sources, fills the gap in the optimization method for pumped storage operation of supporting renewable energy bases in areas where the power market reform is implemented, and can effectively solve the problem of balancing multiple factors such as the operating cost of the variable speed pumped storage power station, the system peak load demand and the new energy consumption in the power market environment. The method can effectively smooth out the fluctuation of renewable energy, reflect the synergistic effect of the variable speed pumped storage power station, the new energy power source and the power market, and improve the operating efficiency and benefits of the variable speed pumped storage, providing a scientific basis and reference for the dispatching and operation of the variable speed pumped storage power station supporting the new energy base.

[0016] In an optional implementation, the predicted power data of new energy sources and the market electricity price prediction data are processed separately to obtain a set of new energy source severe output scenarios, day-ahead market electricity price data, and real-time market electricity price data, including:

[0017] Based on the predicted power data of renewable energy and the predicted market electricity price data, the Monte Carlo method is used to generate multiple random scenarios;

[0018] The synchronous back-substitution method is used to reduce the scenarios of multiple random scenarios, and the set of scenarios with poor output of new energy and the set of scenarios with day-ahead market electricity prices are obtained. The day-ahead market electricity price data is determined based on the probability of occurrence of the day-ahead market electricity price scenario set.

[0019] Obtain real-time historical electricity price data, and based on the real-time historical electricity price data, use the autoregressive sliding average model to obtain real-time market electricity price data.

[0020] This embodiment provides an optimized operation method for a variable-speed pumped-storage power station. Since it is difficult for new energy bases to obtain accurate and detailed internal and external information on the electricity market and power system model data, practical data of new energy bases equipped with variable-speed pumped-storage power stations participating in electricity market simulations are processed to achieve accurate acquisition of day-ahead market electricity price data and real-time market electricity price data, thereby providing a data basis for iterative solution of the subsequent two-stage operation optimization model.

[0021] In an optional implementation, it is determined whether the lower bound of the two-stage operation optimization model and the two-stage operation optimization model meet the convergence condition, and based on the determination result, the day-ahead market bidding model and the real-time market self-dispatching model are alternately iterated to obtain an optimal operation plan of the variable-speed pumped storage power station, including:

[0022] If the lower bound of the two-stage operation optimization model and the upper bound of the two-stage operation optimization model do not meet the convergence condition, the scenario of the new energy bad output scenario concentration is updated to obtain the updated new energy bad output scenario, the day-ahead market electricity price data and the real-time market electricity price data;

[0023] Based on the updated renewable energy adverse output scenario, day-ahead market electricity price data and real-time market electricity price data, the day-ahead market bidding model and the real-time market self-dispatching model are used to update the lower bound of the two-stage operation optimization model and the upper bound of the two-stage operation optimization model.

[0024] The updated lower bound of the two-stage operation optimization model and the upper bound of the two-stage operation optimization model are compared with the convergence condition. If the updated lower bound of the two-stage operation optimization model and the upper bound of the two-stage operation optimization model meet the convergence condition, the iteration is stopped to obtain the optimal operation plan of the variable speed pumped storage power station.

[0025] The present embodiment provides a method for optimizing the operation of a variable-speed pumped-storage power station. By continuously updating the scenarios where new energy poor output scenarios are concentrated, the updates of new energy poor output scenarios, day-ahead market electricity price data, and real-time market electricity price data are realized. Then, according to the updated new energy poor output scenarios, day-ahead market electricity price data, and real-time market electricity price data, the lower and upper bounds of the two-stage operation optimization model are updated. During the operation of the pumped-storage power station supporting the new energy base, a simulation of the electricity market is introduced, so that the optimized operation plan of the variable-speed pumped-storage power station is the operation plan of the variable-speed pumped-storage power station when the new energy base has the highest benefit from participating in the electricity market, thereby improving the operation efficiency and benefits of the variable-speed pumped storage.

[0026] In an optional implementation, establishing a day-ahead market bidding model includes:

[0027] Obtain basic parameters of a new energy base equipped with a variable-speed pumped storage power station;

[0028] Taking the maximum total revenue of the new energy base equipped with variable speed pumped storage power station in the day-ahead market and the real-time market as the goal, the first-stage optimization objective function is established based on the basic parameters of the new energy base equipped with variable speed pumped storage power station;

[0029] The output constraints of new energy bases and the state constraints of pumped storage reservoir capacity in the current period are obtained, and the output constraints of new energy bases and the state constraints of pumped storage reservoir capacity in the current period are used as the constraints of the first-stage optimization objective function to build a day-ahead market bidding model.

[0030] This embodiment provides a method for optimizing the operation of a variable-speed pumped-storage power station. By establishing a day-ahead market bidding model and taking the day-ahead market bidding model as the model of the first stage, it takes into account the mutual influence between the charging and discharging activities of the variable-speed pumped-storage power station and the electricity market, laying a foundation for the subsequent optimization of the operation of the variable-speed pumped-storage power station.

[0031] In an optional implementation, a real-time market self-dispatching model is established, including:

[0032] Taking the maximum total revenue of the new energy base equipped with a variable speed pumped storage power station in the real-time market as the goal, the second-stage optimization objective function is established based on the basic parameters of the new energy base equipped with a variable speed pumped storage power station;

[0033] The output constraints of new energy bases and the state constraints of pumped storage reservoir capacity in multiple time periods are obtained, and the output constraints of new energy bases and the state constraints of pumped storage reservoir capacity in multiple time periods are used as the constraints of the second-stage optimization objective function to build a real-time market self-dispatching model.

[0034] The present embodiment provides an optimized operation method for a variable-speed pumped-storage power station. By establishing a real-time market self-dispatching model and using the real-time market self-dispatching model as the model of the second stage, the method fully considers the impact of the charging and discharging plan of the variable-speed pumped-storage power station in the real-time market on the initial state of pumped storage in the subsequent real-time market. The method can effectively solve the problem of balancing multiple factors such as the operating cost of the variable-speed pumped-storage power station, system peak-shaving demand, and new energy consumption in the power market environment, thereby improving the operating efficiency and benefits of the variable-speed pumped-storage power station.

[0035] In a second aspect, the present invention provides an optimized operation device for a variable speed pumped storage power station, the device comprising:

[0036] The first establishment module is used to establish a day-ahead market bidding model; the day-ahead market bidding model aims to maximize the total revenue of a new energy base equipped with a variable-speed pumped storage power station in the day-ahead market and the real-time market;

[0037] The second establishment module is used to establish a real-time market self-dispatching model; the real-time market self-dispatching model aims to maximize the total revenue of the new energy base equipped with a variable-speed pumped storage power station in the real-time market;

[0038] The iterative solution module is used to iteratively solve the two-stage operation optimization model including the day-ahead market bidding model and the real-time market self-dispatching model to obtain the optimal operation plan of the variable-speed pumped storage power station.

[0039] In a third aspect, the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to execute the method for optimizing operation of a variable-speed pumped-storage power station according to the first aspect or any corresponding embodiment thereof.

[0040] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the method for optimizing operation of a variable-speed pumped-storage power station according to the first aspect or any corresponding embodiment thereof.

[0041] In a fifth aspect, the present invention provides a computer program product, comprising computer instructions for causing a computer to execute the method for optimizing operation of a variable-speed pumped-storage power station according to the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0043] Figure 1 is a flow chart of an optimized operation method of a variable speed pumped storage power station according to an embodiment of the present invention;

[0044] Figure 2 is a flow chart of another method for optimizing operation of a variable speed pumped storage power station according to an embodiment of the present invention;

[0045] Figure 3 is a flow chart of another method for optimizing operation of a variable speed pumped storage power station according to an embodiment of the present invention;

[0046] Figure 4 is a schematic diagram of a curve for predicting the available power generated by new energy according to an embodiment of the present invention;

[0047] Figure 5 is a schematic diagram of a curve of a market predicted electricity price according to an embodiment of the present invention;

[0048] Figure 6 is a curve diagram of the power bidding amount of a new energy base with or without a supporting variable-speed pumped-storage power station according to an embodiment of the present invention;

[0049] Figure 7is a schematic diagram of a curve showing storage capacity changes of a variable-speed pumped-storage power station according to an embodiment of the present invention;

[0050] Figure 8 is a structural block diagram of an optimized operation device of a variable speed pumped storage power station according to an embodiment of the present invention;

[0051] Fig. 9 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are 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 those skilled in the art without creative work are within the scope of protection of the present invention.

[0053] The research on power system operation optimization includes: (1) considering the operation optimization of new energy bases equipped with small-capacity energy storage equipment participating in the power market; (2) conducting market clearing simulation of pumped storage units based on the full regional market information and power grid data of the power market development area.

[0054] However, the above research on power system operation optimization has the following defects:

[0055] (1) In the study of the participation of new energy bases in the electricity market, only small-capacity energy storage devices are considered, and it is believed that the impact of the charging and discharging activities of these small-capacity energy storage devices on the electricity market price is negligible. It is not considered that pumped storage power stations, as a large-scale energy storage device, have charging and discharging activities that will not only be affected by the market electricity price, but will also in turn affect the actual situation of the market in many aspects such as clearing prices and dispatch plans.

[0056] (2) In the study of power system operation optimization considering the power market environment, very detailed market data and power grid data are required, ignoring the reality that power generation companies such as new energy bases have difficulty in obtaining this information.

[0057] (3) The optimization of variable-speed pumped storage operation in supporting new energy bases under the power market environment is not considered.

[0058] In order to solve the above technical problems, an embodiment of the present invention provides a method for optimizing the operation of a variable-speed pumped-storage power station. It should be noted that the embodiment of the present invention provides a method for optimizing the operation of a variable-speed pumped-storage power station, and its execution subject can be a device for optimizing the operation of a variable-speed pumped-storage power station. The device for optimizing the operation of a variable-speed pumped-storage power station can be implemented as part or all of an electronic device through software, hardware, or a combination of software and hardware, wherein the electronic device can be a server or a terminal, wherein the server in the embodiment of the present application can be a single server or a server cluster composed of multiple servers, and the terminal in the embodiment of the present application can be a smart phone, a personal computer, a tablet computer, or other intelligent hardware devices. In the following method embodiments, the execution subject is an electronic device as an example for explanation.

[0059] According to an embodiment of the present invention, an embodiment of an optimized operation method of a variable-speed pumped-storage power station is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0060] In this embodiment, a method for optimizing operation of a variable speed pumped storage power station is provided, which can be used for the above-mentioned electronic equipment. Figure 1 is a flow chart of an optimized operation method of a variable speed pumped storage power station according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0061] Step S101, establishing a day-ahead market bidding model; the day-ahead market bidding model aims to maximize the total revenue of a new energy base equipped with a variable-speed pumped-storage power station in the day-ahead market and the real-time market.

[0062] Specifically, the decision variable of the day-ahead market bidding model is the electricity bid amount of the new energy base in the day-ahead market.

[0063] Step S102, establishing a real-time market self-dispatching model; the real-time market self-dispatching model aims to maximize the total revenue of the new energy base equipped with a variable-speed pumped storage power station in the real-time market.

[0064] Specifically, considering that the charging and discharging plan of the variable-speed pumped-storage power station in the real-time market at time t will affect the initial state of the pumped-storage power station in the subsequent real-time market, the real-time market self-dispatching model aims to maximize the total revenue of the new energy base in the real-time market from the subsequent time t to the T time.

[0065] Step S103, iteratively solving a two-stage operation optimization model including a day-ahead market bidding model and a real-time market self-dispatching model to obtain an optimized operation plan for the variable-speed pumped-storage power station.

[0066] Specifically, the C&CG algorithm is adopted to make the two-stage operation optimization model alternately iteratively calculate the total revenue of the new energy base corresponding to different pumped storage scheduling schemes, and obtain the optimal operation plan of the variable-speed pumped storage power station when the new energy base has the highest benefit from participating in the electricity market; among them, the C&CG algorithm is an algorithm for solving two-stage robust optimization problems, which combines Benders decomposition (an algorithm for solving mixed integer programming problems) with a row and column generation algorithm.

[0067] The present embodiment provides an optimized operation method for a variable speed pumped storage power station. A day-ahead market bidding model is established with the goal of maximizing the total revenue of a new energy base equipped with a variable speed pumped storage power station in the day-ahead market and the real-time market. A real-time market self-dispatching model is established with the goal of maximizing the total revenue of a new energy base equipped with a variable speed pumped storage power station in the real-time market. Then, a two-stage operation optimization model including the day-ahead market bidding model and the real-time market self-dispatching model is iteratively solved. The impact of the variable speed pumped storage power station on the power market is fully considered, and the operation problems of the variable speed pumped storage power station in the power market environment are effectively solved, the operation efficiency and revenue of the variable speed pumped storage are improved, and a scientific basis and reference are provided for the dispatching and operation of the variable speed pumped storage power station supporting the new energy base.

[0068] In this embodiment, a method for optimizing operation of a variable speed pumped storage power station is provided, which can be used for the above-mentioned electronic equipment. Figure 2 is a flow chart of an optimized operation method of a variable speed pumped storage power station according to an embodiment of the present invention. Figure 2 As shown, the process includes the following steps:

[0069] Step S201, establishing a day-ahead market bidding model; the day-ahead market bidding model aims to maximize the total revenue of a new energy base equipped with a variable-speed pumped-storage power station in the day-ahead market and the real-time market.

[0070] Specifically, the above step S201 includes:

[0071] Step S2011, obtaining basic parameters of a new energy base equipped with a variable-speed pumped-storage power station.

[0072] Specifically, the basic parameters of a new energy base equipped with a variable-speed pumped-storage power station include: wind farm and photovoltaic power station parameter data, such as installed capacity of units; variable-speed pumped-storage parameter data, including variable-speed pumped-storage capacity, minimum pumping power, minimum power generation power, pumping efficiency, power generation efficiency, operation and maintenance cost data, etc.

[0073] Step S2012, with the goal of maximizing the total revenue of the new energy base equipped with a variable speed pumped storage power station in the day-ahead market and the real-time market, establish the first stage optimization objective function based on the basic parameters of the new energy base equipped with a variable speed pumped storage power station.

[0074] Specifically, the expression of the first-stage optimization objective function is as follows:

[0075]

[0076] Among them, F a is the total revenue of the new energy base equipped with a variable-speed pumped storage power station in the day-ahead market and the real-time market; t is the time index, indicating time t; T represents the last real-time market time, t≤T; Δt is the market clearing time interval; They are the day-ahead market electricity price data and the real-time market electricity price data at time t respectively; is the electricity bid amount of the new energy base in the day-ahead market at time t; is the deviation power of the real-time market at time t; is the deviation assessment power, i.e., output deviation; α is the deviation assessment coefficient; c o The operation and maintenance costs of variable speed pumped storage power stations; are the charging power and discharging power of the variable-speed pumped storage power station at time t respectively; λ is the market deviation assessment margin.

[0077] Step S2013, obtaining the output constraint of the new energy base and the state constraint of the pumped storage reservoir capacity in the current period, taking the output constraint of the new energy base and the state constraint of the pumped storage reservoir capacity in the current period as the constraint conditions of the first-stage optimization objective function, and constructing a day-ahead market bidding model.

[0078] Specifically, the expression of the output constraint of the new energy base at time t in the current period is as follows:

[0079]

[0080]

[0081]

[0082]

[0083]

[0084]

[0085] in, To provide actual output for wind power; Actual output for photovoltaics; are the predicted outputs of wind power and photovoltaic power, respectively; i is the index of the variable-speed pumped storage unit; P pump,min , P pump,max are the minimum and maximum charging power of the variable speed pumped storage power station respectively; P gen,min , P gen,max are the minimum and maximum values ​​of the power generation of the variable speed pumped storage power station respectively; are state variables, which are the charging and discharging states of the variable speed pumped storage power station. If it is 1, it means that the variable speed pumped storage power station is in the pumping (charging) state. If it is 1, it means that the variable speed pumped storage power station is in the power generation (discharge) state. If it is 0, it means that the variable speed pumped storage is not in the pumping state. If it is 0, it means that the variable-speed pumped storage is not in the power generation (discharge) state.

[0086] Furthermore, the expression of the pumped storage capacity state constraint at time t in the current period is as follows:

[0087]

[0088] E min ≤E t ≤E max (10)

[0089] Among them, E t is the energy storage state of the pumped storage power station at time t; η pump , η gen are the charging and discharging efficiencies of the variable-speed pumped storage power station; E min 、E max are the minimum and maximum energy storage limits of variable speed pumped storage power stations; E t-1 is the energy storage state of the pumped storage power station at time t-1.

[0090] Step S202, establishing a real-time market self-dispatching model; the real-time market self-dispatching model aims to maximize the total revenue of the new energy base equipped with a variable-speed pumped storage power station in the real-time market.

[0091] Specifically, the above step S202 includes:

[0092] Step S2021, with the goal of maximizing the total revenue of the new energy base equipped with a variable speed pumped storage power station in the real-time market, establish a second-stage optimization objective function based on the basic parameters of the new energy base equipped with a variable speed pumped storage power station.

[0093] Specifically, the expression of the second-stage optimization objective function is as follows:

[0094]

[0095] Among them, R t It is the total revenue of the new energy base equipped with a variable-speed pumped-storage power station in the real-time market from the tth moment to the Tth moment.

[0096] Step S2022, obtain the output constraints of the new energy base and the pumped storage reservoir capacity status constraints in multiple time periods, use the output constraints of the new energy base and the pumped storage reservoir capacity status constraints in multiple time periods as the constraints of the second-stage optimization objective function, and build a real-time market self-dispatching model.

[0097] Specifically, the constraints in the day-ahead market bidding model are similar to those in the real-time market self-dispatching model, with only a reduction in the constraint time periods, that is, the day-ahead market bidding model will gradually reduce the time periods to be considered during the calculation process; for example, the initial calculation range of "t" in the constraints of the day-ahead market bidding model is 1 to T, and the "t" in the constraints of the real-time market self-dispatching model will be selected from 1 to T, 2 to T, ..., t to T, ... T in sequence (the calculation time period gradually decreases, and the values ​​of the time periods that are no longer calculated are fixed, so the time periods that are still to be calculated are constrained), thereby reflecting the impact of the variable-speed pumped-storage power station at the tth moment on the subsequent real-time market revenue.

[0098] Step S203, iteratively solve the two-stage operation optimization model including the day-ahead market bidding model and the real-time market self-dispatching model to obtain the optimal operation plan of the variable speed pumped storage power station. Figure 1 Step S203 of the illustrated embodiment will not be described in detail here.

[0099] The present embodiment provides an optimized operation method for a variable-speed pumped-storage power station. By taking the day-ahead market bidding model as the model for the first stage, the mutual influence between the charging and discharging activities of the variable-speed pumped-storage power station and the electricity market is considered. By taking the real-time market self-dispatching model as the model for the second stage, the influence of the charging and discharging plan of the variable-speed pumped-storage power station in the real-time market on the initial state of the variable-speed pumped-storage in the subsequent real-time market is fully considered. The method can effectively solve the problem of balancing multiple factors such as the operating cost of the variable-speed pumped-storage power station, the system peak-shaving demand and the consumption of new energy in the electricity market environment, thereby improving the operating efficiency and benefits of the variable-speed pumped-storage power station.

[0100] In this embodiment, a method for optimizing operation of a variable speed pumped storage power station is provided, which can be used for the above-mentioned electronic equipment. Figure 3 is a flow chart of an optimized operation method of a variable speed pumped storage power station according to an embodiment of the present invention. Figure 3 As shown, the process includes the following steps:

[0101] Step S301, establish a day-ahead market bidding model; the day-ahead market bidding model aims to maximize the total revenue of a new energy base equipped with a variable-speed pumped storage power station in the day-ahead market and the real-time market. Figure 2 Step S201 of the illustrated embodiment will not be described in detail here.

[0102] Step S302: Establish a real-time market self-dispatching model; the real-time market self-dispatching model aims to maximize the total revenue of the new energy base equipped with a variable-speed pumped storage power station in the real-time market. Figure 2 Step S202 of the illustrated embodiment will not be described in detail here.

[0103] Step S303, iteratively solving the two-stage operation optimization model including the day-ahead market bidding model and the real-time market self-dispatching model to obtain an optimized operation plan for the variable-speed pumped-storage power station.

[0104] Specifically, the day-ahead market bidding model uses the market clearing time interval as the time scale to generate the power bidding amount of the new energy base in the day-ahead market, obtain the lower bound of the two-stage operation optimization model, and transfer the power bidding amount of the new energy base in the day-ahead market to the real-time market self-dispatching model; the real-time market self-dispatching model optimizes the real-time output levels of the new energy power sources and pumped-storage power stations from the tth moment to the Tth moment, determines the deviation power and output deviation (i.e., deviation assessment power) in the real-time market at the tth moment, and then determines the dispatching plan of the pumped storage and new energy units on the operation day (i.e., the real-time market opening day), thereby obtaining the upper bound of the two-stage operation optimization model; the benefits of different variable-speed pumped-storage power station operation plans are calculated through alternating iterations of the two-stage model, and the optimized operation plan of the variable-speed pumped-storage power station with the best market economy is obtained.

[0105] Furthermore, the above step S303 includes:

[0106] Step S3031, obtain the predicted power data of new energy and the market electricity price forecast data, process the predicted power data of new energy and the market electricity price forecast data respectively, and obtain the set of new energy severe output scenarios, the day-ahead market electricity price data and the real-time market electricity price data.

[0107] Specifically, based on historical data or external forecasting tools, the predicted power data of new energy sources and the market electricity price forecast data within the market simulation cycle are obtained.

[0108] In some optional implementations, the above step S3031 includes:

[0109] Step a1, based on the predicted power data of renewable energy and the predicted market electricity price data, a plurality of random scenarios are generated using the Monte Carlo method.

[0110] Specifically, a large number of random samplings are performed in the form of normal distribution based on the predicted power generation data of renewable energy and the predicted market electricity price data to generate multiple scenarios, each of which represents a possible renewable energy power generation or market electricity price situation on the day before.

[0111] Step a2, using synchronous back-substitution method to perform scenario reduction on multiple random scenarios, obtain a set of new energy poor output scenarios and a set of day-ahead market electricity price scenarios, and determine the day-ahead market electricity price data based on the probability of occurrence of the day-ahead market electricity price scenario set.

[0112] Specifically, similar scenarios in multiple random scenarios are aggregated into a cluster, and the most central scene in each cluster is selected as the typical scene of the corresponding cluster, so that a large number of scenarios generated by the Monte Carlo method are reduced to a small number of typical scenarios and the corresponding occurrence probabilities are obtained, forming a day-ahead market electricity price scenario set and a new energy output scenario set, and then the scenes with lower new energy output in the typical scenarios are selected to form a new energy poor output scenario set.

[0113] Furthermore, the scenario with the highest probability of occurrence of the day-ahead market electricity price scenario is selected as the day-ahead market electricity price scenario, and then the day-ahead market electricity price data is determined based on the day-ahead market electricity price situation corresponding to the day-ahead market electricity price scenario.

[0114] Step a3, obtaining real-time electricity price historical data, based on which, an autoregressive sliding average model is used to obtain real-time market electricity price data.

[0115] Specifically, based on the continuously updated real-time electricity price historical data, the price forecast value for each time period of the real-time market is recursively obtained through the autoregressive moving average model, which serves as the real-time market electricity price data updated at any time period in the real-time market self-dispatching model.

[0116] Step S3032, input the set of new energy poor output scenarios, the day-ahead market electricity price data and the real-time market electricity price data into the day-ahead market bidding model to obtain the power bidding amount of the new energy base in the day-ahead market and the lower bound of the two-stage operation optimization model.

[0117] Specifically, the day-ahead market electricity price data and real-time market electricity price data Input the above formula (1) to obtain the power bidding amount of the new energy base in the day-ahead market: Deviation power from real-time market Then and Substituting into the above formula (2), the deviation assessment power is calculated And calculate the power bidding amount in the day-ahead market Deviation power of real-time market and deviation assessment power Corresponding F a , the total revenue of the new energy base equipped with a variable-speed pumped storage power station at time t in the day-ahead market and the real-time market is taken as the lower bound of the two-stage operation optimization model.

[0118] Step S3033, input the power bid amount of the new energy base in the day-ahead market into the real-time market self-dispatching model to obtain the upper bound of the two-stage operation optimization model.

[0119] Specifically, the day-ahead market power bidding amount calculated above is Input the above formula (11), combine formula (2) and the real-time electricity price data from time t to time T predicted at time t-1, solve to obtain the deviation power of the real-time market from time t to time T and the deviation assessment power from time t to time T, and then update the deviation power and deviation assessment power of the real-time market at time t to the real-time market self-dispatching model, combine formula (2) and the real-time electricity price data from time t+1 to time T predicted at time t, solve to obtain the deviation power of the real-time market from time t+1 to time T and the deviation assessment power from time t+1 to time T, update and determine the deviation power and deviation assessment power of the real-time market at each moment in turn, input the deviation power and deviation assessment power after all moments of the real-time market are updated into the above formula (1), and obtain the total revenue of the new energy base equipped with a variable speed pumped storage power station in the day-ahead market and the real-time market, and use the total revenue as the upper bound of the two-stage operation optimization model.

[0120] Step S3034, determine whether the lower bound of the two-stage operation optimization model and the two-stage operation optimization model meet the convergence conditions, and based on the judgment result, perform alternating iterative calculations on the day-ahead market bidding model and the real-time market self-dispatching model to obtain the optimal operation plan of the variable-speed pumped-storage power station.

[0121] In some optional implementations, the above step S3034 includes:

[0122] Step b1, if the lower bound of the two-stage operation optimization model and the upper bound of the two-stage operation optimization model do not meet the convergence conditions, the scenarios in the new energy poor output scenario concentration are updated to obtain updated new energy poor output scenarios, day-ahead market electricity price data, and real-time market electricity price data.

[0123] Specifically, the coefficient matrix of the day-ahead market bidding model is updated, that is, the lth scenario data in the new energy severe output scenario set is updated, and then the day-ahead market electricity price data and the real-time market electricity price data are updated.

[0124] Step b2, based on the updated new energy severe output scenario, day-ahead market electricity price data and real-time market electricity price data, the day-ahead market bidding model and the real-time market self-dispatching model are used to update the lower bound of the two-stage operation optimization model and the upper bound of the two-stage operation optimization model.

[0125] Specifically, the day-ahead market bidding model is solved based on the updated day-ahead market electricity price data and the real-time market electricity price data to obtain the lower bound of the updated two-stage operation optimization model, and the solving steps are the same as the above step S3032.

[0126] Furthermore, the day-ahead market power bidding quantity in the real-time market self-dispatching model is updated according to the solution of the day-ahead market bidding model. The real-time market electricity price data updated at any time period is then substituted into the real-time market self-dispatching model for solution, the dispatch decision results are output, and the upper bound of the two-stage operation optimization model is updated.

[0127] Step b4, compare the lower bound of the updated two-stage operation optimization model and the upper bound of the two-stage operation optimization model with the convergence condition. If the lower bound of the updated two-stage operation optimization model and the upper bound of the two-stage operation optimization model meet the convergence condition, stop the iteration and obtain the optimized operation plan of the variable-speed pumped-storage power station.

[0128] Specifically, the convergence condition is that the difference between the upper and lower bounds of the two-stage optimization model is within the preset allowable range; if the difference between the upper and lower bounds of the two-stage optimization model is within the preset allowable range, then the deviation power of the real-time market in the current iteration process is and the amount of electricity bid by new energy bases in the day-ahead market As an optimized operation plan for variable-speed pumped storage power stations.

[0129] Furthermore, if the upper and lower bounds of the two-stage optimization model do not meet the preset allowable range, return to the above steps b1 to b4 to repeat the calculation until the upper and lower bounds of the two-stage optimization model meet the preset allowable range, and stop the iteration.

[0130] The present embodiment provides an optimized operation method for a variable speed pumped storage power station, which processes the predicted power data of renewable energy and the predicted market electricity price data respectively to obtain the day-ahead market electricity price data and the real-time market electricity price data, and then calculates the lower bound and the upper bound of the two-stage operation optimization model, and judges whether the lower bound of the two-stage operation optimization model and the upper bound of the two-stage operation optimization model meet the convergence conditions, fully considers the interaction and influence of the variable speed pumped storage power station on the power market and the output of renewable energy power sources, fills the gap in the optimization method for pumped storage operation of supporting renewable energy bases in the area where the power market reform is implemented, and can effectively solve the problem of balancing multiple factors such as the operating cost of the variable speed pumped storage power station, the system peak load demand and the new energy consumption under the power market environment, which can not only effectively smooth out the fluctuation of renewable energy, but also reflect the synergistic effect of the variable speed pumped storage power station with the renewable energy power source and the power market, and improve the operating efficiency and benefits of the variable speed pumped storage, and provide a scientific basis and reference for the dispatching and operation of the variable speed pumped storage power station supporting the new energy base.

[0131] The effectiveness of the optimized operation method of a variable speed pumped storage power station is verified by a specific embodiment below.

[0132] Embodiment 1:

[0133] In order to verify the effectiveness of the optimized operation method of variable-speed pumped-storage power stations, an analysis was conducted on a new energy base equipped with a 1000MW wind farm, an 800MW photovoltaic power station, and a 100MW / 600MW·h double-fed variable-speed pumped-storage power station.

[0134] The electricity spot market in which the new energy base participates includes 1 day-ahead market and 24 real-time markets; the market deviation assessment margin is 3%, the deviation assessment coefficient is set to 2, and the new energy day-ahead forecast processing selects typical new energy data in a certain region. The predicted output of photovoltaic and wind farms (i.e., the predicted power data of new energy) is as follows: Figure 4 As shown in Figure 2, the forecast error of renewable energy output is set within 10%. Figure 5 As shown, the price of a certain area's electricity market is selected as the market electricity price forecast value, and the standard deviation of the day-ahead electricity price forecast error is set to 2%, and the standard deviation of the real-time electricity price forecast error is set to 4%.

[0135] Through simulation, the estimated total revenue of the new energy base is 6,830,575.7582 yuan when equipped with a pumped storage power station, and the estimated revenue is 6,753,286.1805 yuan when not equipped with a pumped storage power station. This result is consistent with the actual situation. Equipping a pumped storage power station can significantly improve the economic benefits of the new energy base.

[0136] The power bidding amount of the new energy base in the two cases is as follows: Figure 6As shown in the figure, after the variable speed pumped storage power station is equipped, the new energy base can store the new energy power generated during the low electricity price period and send it out during the high electricity price period, so that the new energy base can participate in the electricity market more flexibly and efficiently, and improve the base efficiency. The change of the storage capacity of the variable speed pumped storage power station shows the dispatching of the variable speed pumped storage power station, as shown in the figure. Figure 7 shown.

[0137] In this embodiment, a variable speed pumped storage power station optimization operation device is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware of a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0138] This embodiment provides an optimized operation device for a variable speed pumped storage power station, such as Figure 8 As shown, including:

[0139] The first establishment module 801 is used to establish a day-ahead market bidding model; the day-ahead market bidding model aims to maximize the total revenue of a new energy base equipped with a variable-speed pumped storage power station in the day-ahead market and the real-time market;

[0140] The second establishment module 802 is used to establish a real-time market self-dispatching model; the real-time market self-dispatching model aims to maximize the total revenue of the new energy base equipped with a variable-speed pumped storage power station in the real-time market;

[0141] The iterative solution module 803 is used to iteratively solve the two-stage operation optimization model including the day-ahead market bidding model and the real-time market self-dispatching model to obtain the optimal operation plan of the variable-speed pumped-storage power station.

[0142] In some optional implementations, the iterative solution module 803 includes:

[0143] A data processing unit is used to obtain the predicted power data of renewable energy and the market electricity price forecast data, and respectively process the predicted power data of renewable energy and the market electricity price forecast data to obtain a set of severe output scenarios of renewable energy, day-ahead market electricity price data, and real-time market electricity price data;

[0144] The first calculation unit is used to input the set of new energy poor output scenarios, the day-ahead market electricity price data and the real-time market electricity price data into the day-ahead market bidding model to obtain the power bidding amount of the new energy base in the day-ahead market and the lower bound of the two-stage operation optimization model;

[0145] The second calculation unit is used to input the power bidding amount of the new energy base in the day-ahead market into the real-time market self-dispatching model to obtain the upper bound of the two-stage operation optimization model;

[0146] The judgment unit is used to judge whether the lower bound of the two-stage operation optimization model and the two-stage operation optimization model meet the convergence conditions. Based on the judgment result, the day-ahead market bidding model and the real-time market self-dispatching model are alternately iterated to obtain the optimal operation plan of the variable-speed pumped storage power station.

[0147] In some optional implementations, the data processing unit includes:

[0148] A generation subunit is used to generate multiple random scenarios using the Monte Carlo method based on the power data that can be generated by new energy and the market electricity price forecast data;

[0149] The reduction subunit is used to reduce multiple random scenarios by using the synchronous back-substitution method, obtain a set of new energy poor output scenarios and a set of day-ahead market electricity price scenarios, and determine the day-ahead market electricity price data based on the probability of occurrence of the day-ahead market electricity price scenario set;

[0150] The acquisition subunit is used to acquire real-time electricity price historical data. Based on the real-time electricity price historical data, the real-time market electricity price data is obtained using an autoregressive sliding average model.

[0151] In some optional implementations, the judging unit includes:

[0152] The first updating subunit is used for updating the scenarios in the new energy bad output scenario concentration if the lower bound of the two-stage operation optimization model and the upper bound of the two-stage operation optimization model do not meet the convergence condition, and obtaining the updated new energy bad output scenario, the day-ahead market electricity price data and the real-time market electricity price data;

[0153] The second updating subunit is used to update the lower bound of the two-stage operation optimization model and the upper bound of the two-stage operation optimization model by using the day-ahead market bidding model and the real-time market self-dispatching model based on the updated new energy poor output scenario, the day-ahead market electricity price data and the real-time market electricity price data;

[0154] The iterative subunit is used to compare the lower bound of the updated two-stage operation optimization model and the upper bound of the two-stage operation optimization model with the convergence condition. If the lower bound of the updated two-stage operation optimization model and the upper bound of the two-stage operation optimization model meet the convergence condition, the iteration is stopped to obtain the optimal operation plan of the variable-speed pumped storage power station.

[0155] In some optional implementations, the first establishing module 801 includes:

[0156] An acquisition unit, used to acquire basic parameters of a new energy base equipped with a variable-speed pumped-storage power station;

[0157] The first establishing unit is used to establish a first-stage optimization objective function based on basic parameters of the new energy base equipped with a variable-speed pumped-storage power station, with the goal of maximizing the total revenue of the new energy base equipped with a variable-speed pumped-storage power station in the day-ahead market and the real-time market;

[0158] The first construction unit is used to obtain the output constraints of the new energy base and the state constraints of the pumped storage reservoir capacity in the current period, and use the output constraints of the new energy base and the state constraints of the pumped storage reservoir capacity in the current period as the constraints of the first-stage optimization objective function to construct a day-ahead market bidding model.

[0159] In some optional implementations, the second establishing module 802 includes:

[0160] The second establishing unit is used to establish a second-stage optimization objective function based on basic parameters of the new energy base equipped with a variable-speed pumped-storage power station, with the goal of maximizing the total revenue of the new energy base equipped with a variable-speed pumped-storage power station in the real-time market;

[0161] The second construction unit is used to obtain the output constraints of new energy bases and the state constraints of pumped storage reservoir capacity in multiple time periods, and use the output constraints of new energy bases and the state constraints of pumped storage reservoir capacity in multiple time periods as the constraints of the second-stage optimization objective function to construct a real-time market self-dispatching model.

[0162] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0163] In this embodiment, an optimized operation device of a variable-speed pumped-storage power station is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above-mentioned functions.

[0164] The embodiment of the present invention also provides a computer device having the above Figure 8 An optimized operation device for a variable speed pumped storage power station is shown.

[0165] See also Fig. 9 , Fig. 9 is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention, such as Fig. 9As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Fig. 9 A processor 10 is taken as an example.

[0166] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0167] The memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0168] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0169] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0170] The computer device also includes an input device 30 and an output device 40. The processor 10, the memory 20, the input device 30 and the output device 40 may be connected via a bus or other means. Fig. 9 The example of connecting through bus is taken in the following.

[0171] The input device 30 can receive input digital or character information, and generate key signal input related to the user settings and function control of the computer device, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, an indicator bar, one or more mouse buttons, a trackball, a joystick, etc. The output device 40 may include a display device, an auxiliary lighting device (e.g., an LED) and a tactile feedback device (e.g., a vibration motor), etc. The above-mentioned display device includes but is not limited to a liquid crystal display, a light emitting diode, a display and a plasma display. In some optional embodiments, the display device can be a touch screen.

[0172] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0173] A part of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the existence of the computer program instruction in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc., and accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium accessible to the computer.

[0174] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A method for optimizing the operation of a variable speed pumped storage power station, characterized in that: The method comprises: Establishing a day-ahead market bidding model; the day-ahead market bidding model aims to maximize the total revenue of a new energy base equipped with a variable-speed pumped storage power station in the day-ahead market and the real-time market; Establishing a real-time market self-dispatching model; the real-time market self-dispatching model aims to maximize the total revenue of a new energy base equipped with a variable-speed pumped storage power station in the real-time market; The two-stage operation optimization model including the day-ahead market bidding model and the real-time market self-dispatching model is iteratively solved to obtain an optimized operation plan for the variable-speed pumped-storage power station.

2. The method according to claim 1, characterized in that: The iterative solution of the two-stage operation optimization model including the day-ahead market bidding model and the real-time market self-dispatching model to obtain the optimal operation plan of the variable-speed pumped storage power station includes: Obtaining new energy predicted power data and market electricity price forecast data, and performing data processing on the new energy predicted power data and market electricity price forecast data respectively to obtain a new energy poor output scenario set, day-ahead market electricity price data, and real-time market electricity price data; Input the set of new energy adverse output scenarios, the day-ahead market electricity price data and the real-time market electricity price data into the day-ahead market bidding model to obtain the power bidding amount of the new energy base in the day-ahead market and the lower bound of the two-stage operation optimization model; Inputting the power bid amount of the new energy base in the day-ahead market into the real-time market self-dispatching model to obtain the upper bound of the two-stage operation optimization model; Determine whether the lower bound of the two-stage operation optimization model and the upper bound of the two-stage operation optimization model meet the convergence conditions, and based on the judgment result, perform alternating iterative calculations on the day-ahead market bidding model and the real-time market self-dispatching model to obtain an optimized operation plan for the variable-speed pumped-storage power station.

3. The method according to claim 2, characterized in that The data of the predicted power generation data of the new energy and the predicted market electricity price data are processed respectively to obtain a set of severe power output scenarios of new energy, day-ahead market electricity price data and real-time market electricity price data, including: Based on the predicted power data of the renewable energy and the predicted market electricity price data, a plurality of random scenarios are generated using the Monte Carlo method; Using synchronous back-substitution method to reduce the multiple random scenarios, obtain the new energy poor output scenario set and the day-ahead market electricity price scenario set, and determine the day-ahead market electricity price data based on the occurrence probability of the day-ahead market electricity price scenario set; Real-time electricity price historical data is acquired, and based on the real-time electricity price historical data, the real-time market electricity price data is obtained using an autoregressive sliding average model.

4. The method according to claim 3, characterized in that The step of judging whether the lower bound of the two-stage operation optimization model and the upper bound of the two-stage operation optimization model meet the convergence condition, and performing alternating iterative calculations on the day-ahead market bidding model and the real-time market self-dispatching model based on the judgment result to obtain the optimal operation plan of the variable-speed pumped-storage power station includes: If the lower bound of the two-stage operation optimization model and the upper bound of the two-stage operation optimization model do not meet the convergence condition, the scenarios in the new energy poor output scenario set are updated to obtain updated new energy poor output scenarios, day-ahead market electricity price data, and real-time market electricity price data; Based on the updated new energy severe output scenario, day-ahead market electricity price data and real-time market electricity price data, the day-ahead market bidding model and the real-time market self-dispatching model are used to update the lower bound of the two-stage operation optimization model and the upper bound of the two-stage operation optimization model; The updated lower bound of the two-stage operation optimization model and the upper bound of the two-stage operation optimization model are compared with the convergence condition. If the updated lower bound of the two-stage operation optimization model and the upper bound of the two-stage operation optimization model meet the convergence condition, the iteration is stopped to obtain the optimized operation plan of the variable speed pumped storage power station.

5. The method according to claim 1, characterized in that The establishing of the day-ahead market bidding model comprises: Obtain basic parameters of a new energy base equipped with a variable-speed pumped storage power station; Taking the total revenue of the new energy base equipped with a variable speed pumped storage power station in the day-ahead market and the real-time market as the goal, a first-stage optimization objective function is established based on the basic parameters of the new energy base equipped with a variable speed pumped storage power station; The output constraint of the new energy base and the state constraint of the pumped storage reservoir capacity in the current period are obtained, and the output constraint of the new energy base and the state constraint of the pumped storage reservoir capacity in the current period are used as the constraint conditions of the optimization objective function of the first stage to construct the day-ahead market bidding model.

6. The method according to claim 5, characterized in that The establishment of a real-time market self-dispatching model includes: Taking the total revenue of the new energy base equipped with a variable speed pumped storage power station in the real-time market as the goal, a second-stage optimization objective function is established based on the basic parameters of the new energy base equipped with a variable speed pumped storage power station; The output constraints of new energy bases and the state constraints of pumped storage reservoir capacity in multiple time periods are obtained, and the output constraints of new energy bases and the state constraints of pumped storage reservoir capacity in the multiple time periods are used as the constraints of the second-stage optimization objective function to construct the real-time market self-dispatching model.

7. An optimized operation device for a variable speed pumped storage power station, characterized in that: The device comprises: The first establishment module is used to establish a day-ahead market bidding model; the day-ahead market bidding model aims to maximize the total revenue of a new energy base equipped with a variable-speed pumped storage power station in the day-ahead market and the real-time market; The second establishment module is used to establish a real-time market self-dispatching model; the real-time market self-dispatching model aims to maximize the total revenue of the new energy base equipped with a variable-speed pumped storage power station in the real-time market; The iterative solution module is used to iteratively solve the two-stage operation optimization model including the day-ahead market bidding model and the real-time market self-dispatching model to obtain an optimized operation plan for the variable-speed pumped-storage power station.

8. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the optimized operation method of the variable speed pumped storage power station according to any one of claims 1 to 6 by executing the computer instructions.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the method for optimizing operation of a variable-speed pumped-storage power station according to any one of claims 1 to 6.

10. A computer program product, characterized in that The method comprises computer instructions for causing a computer to execute the method for optimizing operation of a variable-speed pumped-storage power station according to any one of claims 1 to 6.

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