A method and device for coordinated operation optimization of energy storage and new energy station

Abstract

The application relates to the technical field of new energy station power generation output optimization configured with energy storage, and particularly provides a coordinated operation optimization method and device for energy storage and a new energy station, which comprises the following steps: obtaining relevant data of the new energy station; taking the relevant data of the new energy station as input, solving a pre-constructed day-ahead coordinated optimization operation model by using a robust optimization method to obtain a day-ahead optimization operation scheme; taking the day-ahead optimization operation scheme as input, solving a pre-constructed day-ahead optimization operation model by using a mixed integer linear programming method to obtain an optimization operation result of a next scheduling period; and performing new energy station operation optimization according to the optimization operation result of the next scheduling period. The technical scheme provided by the application fully utilizes the value of energy storage resources, reduces wind or light abandonment of the new energy station while tracking AGC instructions or predicting power, increases power selling income of the new energy station, reduces power station assessment fees, and improves the overall benefit of the new energy station.

Description

Technical Field

[0001] This invention relates to the field of power generation technology for new energy power plants equipped with energy storage, and specifically to a method and device for optimizing the coordinated operation of energy storage and new energy power plants. Background Technology

[0002] In recent years, the installed capacity and proportion of new energy sources in the power system have been continuously increasing. Due to the randomness of new energy sources, they have brought enormous challenges to the dispatch and operation of the power grid. The new power system with new energy sources as the main body urgently needs to develop frequency regulation resources other than thermal power and hydropower. At present, both new energy sources and energy storage can participate in frequency regulation ancillary services.

[0003] Coordinated optimization of energy storage and renewable energy power plants refers to the process of rationally formulating output plans for both renewable energy and energy storage, based on known short-term or ultra-short-term output forecasts, to mitigate fluctuations in renewable energy output and track AGC (Automatic Generation Control) commands. Current optimization of renewable energy power plant operation control is primarily from the perspective of the power grid, or within independent microgrids or active distribution networks that include renewable energy generation. When optimizing the control of energy storage and renewable energy power plants from the perspective of the power grid, the optimization objectives are often to mitigate fluctuations in renewable energy output and maximize renewable energy consumption. When optimizing the control of independent microgrids or active distribution networks, the focus is on minimizing the overall regional electricity cost.

[0004] As a high-quality and controllable resource in the power system, energy storage has become a standard feature of new energy power plants and an essential component of the new power system. However, after energy storage is deployed, its operation mode is limited, its utilization rate is low, and it has not significantly increased the revenue of the power plants. Energy storage, as a flexible and controllable resource, possesses multiple functions such as smoothing out fluctuations in new energy output, peak shaving and valley filling, tracking AGC commands, tracking and predicting output, primary frequency regulation, and inertia control. However, these functions can conflict in practice, and some functions, such as fluctuation smoothing and inertia control, have no revenue source. Summary of the Invention

[0005] To at least partially overcome the problems existing in related technologies, this application provides a method and apparatus for optimizing the coordinated operation of energy storage and new energy power stations.

[0006] According to a first aspect of the embodiments of this application, a method for optimizing the coordinated operation of energy storage and new energy power stations is provided, the method comprising:

[0007] Obtain relevant data from new energy power plants;

[0008] Using the relevant data of the new energy power station as input, the robust optimization method is used to solve the pre-constructed day-ahead collaborative optimization operation model to obtain the day-ahead optimization operation scheme;

[0009] Using the aforementioned daytime optimized operation plan as input, the pre-constructed intraday optimized operation model is solved using a mixed integer linear programming method to obtain the optimized operation results for the next scheduling period;

[0010] Based on the optimized operation results of the next scheduling period, the operation of new energy power stations will be optimized;

[0011] The pre-built day-ahead collaborative optimization operation model is constructed with the goal of maximizing the total electricity sales revenue of new energy power plants.

[0012] The pre-built intraday optimized operation model is constructed with the goal of minimizing the deviation of AGC commands or minimizing the predicted output deviation of new energy power plants.

[0013] Preferably, the relevant data for the new energy power stations include: new energy day-ahead short-term power forecast data and day-ahead electricity price clearing data;

[0014] The aforementioned new energy day-ahead short-term power forecast data includes: wind power output and photovoltaic power output for each time period;

[0015] The aforementioned day-ahead electricity price clearing data includes: electricity sales prices for each time period.

[0016] Preferably, the current-day optimized operating model includes:

[0017] The objective function of the current-day optimization model is determined by the following formula:

[0018]

[0019] The total electricity sales revenue F of the new energy power station is determined by the following formula. D :

[0020]

[0021] The electricity sales revenue f of the renewable energy power station in time period t is determined by the following formula. i (t):

[0022]

[0023] The operation and maintenance cost f of the new energy power station in time period t is determined by the following formula. e (t):

[0024]

[0025] In the above formula, t∈[1,N], N is the total number of time periods; x and u are control variables, x is a continuous variable, x is the energy storage charging / discharging power, X is a set of continuous variables; u is a 0-1 variable, u is the charging / discharging state of the energy storage system, U is a set of 0-1 variables; w is a random variable, w is the output of wind power / photovoltaic power generation, W is a set of random variables; D is the day-ahead, c sell (t) represents the electricity price for the t-th time period. Let t be the output of wind power generation in time period t. For the photovoltaic power output in time period t, The charging power of the energy storage system in time period t. Let be the discharge power of the energy storage system in time period t, and Δt be the duration of a unit time period; c Wind For the maintenance costs of wind power generation, c PV For the maintenance costs of photovoltaic power generation, c ES This refers to the maintenance costs of the energy storage system.

[0026] Preferably, the current-day optimized operating model further includes:

[0027] The constraints of the day-ahead optimized operating model are determined by the following formula:

[0028]

[0029]

[0030]

[0031]

[0032]

[0033]

[0034] In the above formula, t∈[1,N], and N is the total number of time periods; This represents the minimum state of charge of the energy storage system. This represents the maximum state of charge of the energy storage system. Let be the energy stored in the energy storage system during time period t. The rated capacity of the energy storage system, The charging power of the energy storage system in time period t. Let be the discharge power of the energy storage system in time period t. The charging status of the energy storage system. This represents the discharge state of the energy storage system. This represents the maximum charge / discharge power of the energy storage system. This refers to the energy stored in the energy storage system before the start of the day. The energy stored in the energy storage system during the Nth time period; Let t be the output of wind power generation in time period t. For the photovoltaic power output in time period t, The maximum available power output of wind power generation in time period t. t represents the maximum available power generated by photovoltaic power generation during the t-th time period.

[0035] Preferably, the maximum state of charge of the energy storage system is determined by the following formula.

[0036]

[0037] The energy storage capacity of the energy storage system in time period t is determined by the following formula.

[0038]

[0039] The maximum capacity required for the energy storage system to assist new energy power plants in tracking AGC commands is determined by the following formula.

[0040]

[0041] In the above formula, Let δ be the energy stored in the energy storage system during time period t-1. ES The self-dissipation rate of the energy storage system. To improve the charging efficiency of energy storage systems. For the discharge efficiency of the energy storage system Let be the energy stored by the energy storage system in the t-th time period of the previous day.

[0042] Preferably, the step of using relevant data from the new energy power station as input and solving the pre-constructed day-ahead collaborative optimization operation model using a robust optimization method to obtain the day-ahead optimized operation scheme includes:

[0043] Based on the aforementioned new energy day-ahead short-term power forecast data, the limit constraints of wind power output and photovoltaic power output are obtained by using the scenario generation method and scenario reduction method.

[0044] Using the day-ahead short-term power forecast data and the day-ahead electricity price clearing data as input, and based on the limit constraints of the wind power output, the limit constraints of the photovoltaic power output, and the constraints of the day-ahead collaborative optimization operation model, the pre-constructed day-ahead collaborative optimization operation model is solved using a robust optimization method to obtain the charging / discharging power of the energy storage system at each time period.

[0045] The charging / discharging power of the energy storage system at each time period is the day-ahead optimized operation scheme.

[0046] Preferably, the step of obtaining the boundary constraints for wind power output and photovoltaic power output based on the day-ahead short-term power forecast data of the new energy sources, using a scenario generation method and a scenario reduction method, includes:

[0047] Based on the aforementioned new energy day-ahead short-term power forecast data, a preset number of wind power output scenarios and photovoltaic power output scenarios are generated using a scenario generation method.

[0048] Using the scenario reduction method, typical scenarios are selected from the wind power output scenarios and photovoltaic power output scenarios respectively to obtain the upper / lower limits of wind power output and photovoltaic power output for each time period.

[0049] By utilizing the upper / lower limits of wind power output and photovoltaic power output for each time period, the limit constraints of wind power output and photovoltaic power output are obtained respectively.

[0050] Preferably, the limit constraint condition for the wind power output is determined by the following formula:

[0051]

[0052] The limit constraints on the photovoltaic power generation output are determined by the following formula:

[0053]

[0054] In the above formula, and These represent the upper and lower limits of wind power output in time period t. and These represent the upper and lower limits of photovoltaic power generation output in time period t. The maximum available power output of wind power generation in time period t. t represents the maximum available power generated by photovoltaic power generation during the t-th time period.

[0055] Preferably, the intraday optimized operation model includes:

[0056] The objective function of the intraday optimization operation model is determined by the following formula:

[0057]

[0058] The AGC command deviation or the predicted output deviation of the new energy power station for the g-th time period of the i-th intraday optimized operation is determined by the following formula.

[0059]

[0060] In the above formula, i∈[1,M], where M is the total number of optimization runs per day; g∈[1,N] O ], N O This represents the total number of segments optimized each day. For the AGC instruction in the g-th time period of the i-th day's optimized operation, The predicted power output of the renewable energy power station for the g-th time period of the i-th day's optimized operation. and It is a variable between 0 and 1, and cannot be 1 at the same time; when tracking AGC instructions during the g-th time period of the i-th intraday optimization run, Let g be the AGC command deviation during the g-th period of the i-th day's optimized operation; when tracking the predicted output of the renewable energy power station during the g-th period of the i-th day's optimized operation, The predicted output deviation of the new energy power station during the g-th time period of the i-th day of optimized operation; Let g be the AGC instruction value for the g-th time period of the i-th day's optimized operation. The predicted output value of the renewable energy power station during the g-th time period of the i-th day of optimized operation; The actual output of wind power generation during the g-th time period of the i-th day's optimized operation. The actual output of photovoltaic power generation during the g-th period of the i-th day's optimized operation. The charging power of the energy storage system during the i+g-1th time period of the i-th day's optimized operation. Let the discharge power of the energy storage system be the discharge power of the energy storage system during the (i+g-1)th time period of the (i+g-1)th optimized operation within the day. Let be the change in the charging / discharging power of the energy storage system during the g-th period of the i-th day of optimized operation, relative to the day-ahead optimization results.

[0061] Preferably, the intraday optimized operation model further includes:

[0062] The constraints of the intraday optimized operation model are determined by the following formula:

[0063]

[0064]

[0065]

[0066]

[0067] In the above formula, Let g be the ultra-short-term predicted power value of wind power generation during the g-th period of the i-th intraday optimized operation. Let g be the ultra-short-term predicted power value of photovoltaic power generation during the g-th period of the i-th day's optimized operation. For the state of charge of the energy storage system during the (i+g-1)th time period of the (i+g-1)th day of the (i+g-1)th day of optimized operation, For the state of charge of the energy storage system during the t+g-2 period of the i-th day of optimized operation, Let g be the change in the amount of energy stored in the energy storage system relative to the day-ahead time period during the i-th day of optimized operation. The rated capacity of the energy storage system, This represents the maximum charging and discharging power of the energy storage system.

[0068] Preferably, the change in the amount of stored energy relative to the day-ahead energy storage system during the g-th time period of the i-th day-ahead optimized operation is determined by the following formula.

[0069]

[0070] In the above formula, δ represents the energy stored in the energy storage system during the (g-1)th time period. ES Let be the self-dissipation rate of the energy storage system, and Δg be the duration of a unit time period. To improve the charging efficiency of energy storage systems. Let be the change in the charging / discharging power of the energy storage system during the g-th period of the i-th day's optimized operation, relative to the day-ahead optimization results. The charging / discharging / electrical power of the energy storage system during the g-th period of the i-th day's optimized operation.

[0071] Preferably, the step of using the day-ahead optimized operation plan as input and solving the pre-built intraday optimized operation model using a mixed-integer linear programming method to obtain the optimized operation result for the next scheduling period includes:

[0072] Using the day-ahead optimized operation scheme as input, and based on the constraints of the day-intraday optimized operation model, the pre-constructed day-intraday optimized operation model is solved using a mixed integer linear programming method to obtain the change in the charging / discharging power of the energy storage system relative to the day-ahead optimized result for the next scheduling period;

[0073] The charging / discharging power of the energy storage system corresponding to the next scheduling period in the day-ahead optimized operation plan is added to the change in the charging / discharging power of the energy storage system in the next scheduling period relative to the day-ahead optimization result to obtain the final charging / discharging power of the energy storage system in the next scheduling period.

[0074] The final charging / discharging power of the energy storage system in the next scheduling period is the optimized operation result of the next scheduling period.

[0075] According to a second aspect of the embodiments of this application, a device for the coordinated operation optimization of energy storage and new energy power stations is provided, the device comprising:

[0076] The first acquisition module is used to acquire relevant data from new energy power stations;

[0077] The second acquisition module is used to take the relevant data of the new energy power station as input, and use the robust optimization method to solve the pre-constructed day-ahead collaborative optimization operation model to obtain the day-ahead optimization operation scheme.

[0078] The third acquisition module is used to take the day-ahead optimized operation plan as input, and use the mixed integer linear programming method to solve the pre-built intraday optimized operation model to obtain the optimized operation result of the next scheduling period.

[0079] The optimization module is used to optimize the operation of new energy power stations based on the optimization results of the next scheduling period.

[0080] The pre-built day-ahead collaborative optimization operation model is constructed with the goal of maximizing the total electricity sales revenue of new energy power plants.

[0081] The pre-built intraday optimized operation model is constructed with the goal of minimizing the deviation of AGC commands or minimizing the predicted output deviation of new energy power plants.

[0082] Preferably, the relevant data for the new energy power stations include: new energy day-ahead short-term power forecast data and day-ahead electricity price clearing data;

[0083] The aforementioned new energy day-ahead short-term power forecast data includes: wind power output and photovoltaic power output for each time period;

[0084] The aforementioned day-ahead electricity price clearing data includes: electricity sales prices for each time period.

[0085] Preferably, the current-day optimized operating model includes:

[0086] The objective function of the current-day optimization model is determined by the following formula:

[0087]

[0088] The total electricity sales revenue F of the new energy power station is determined by the following formula. D :

[0089]

[0090] The electricity sales revenue f of the renewable energy power station in time period t is determined by the following formula. i (t):

[0091]

[0092] The operation and maintenance cost f of the new energy power station in time period t is determined by the following formula.e (t):

[0093]

[0094] In the above formula, t∈[1,N], N is the total number of time periods; x and u are control variables, x is a continuous variable, x is the energy storage charging / discharging power, X is a set of continuous variables; u is a 0-1 variable, u is the charging / discharging state of the energy storage system, U is a set of 0-1 variables; w is a random variable, w is the output of wind power / photovoltaic power generation, W is a set of random variables; D is the day-ahead, c sell (t) represents the electricity price for the t-th time period. Let t be the output of wind power generation in time period t. For the photovoltaic power output in time period t, The charging power of the energy storage system in time period t. Let be the discharge power of the energy storage system in time period t, and Δt be the duration of a unit time period; c Wind For the maintenance costs of wind power generation, c PV For the maintenance costs of photovoltaic power generation, c ES This refers to the maintenance costs of the energy storage system.

[0095] Preferably, the current-day optimized operating model further includes:

[0096] The constraints of the day-ahead optimized operating model are determined by the following formula:

[0097]

[0098]

[0099]

[0100]

[0101]

[0102]

[0103] In the above formula, t∈[1,N], and N is the total number of time periods; This represents the minimum state of charge of the energy storage system. This represents the maximum state of charge of the energy storage system. Let be the energy stored in the energy storage system during time period t. The rated capacity of the energy storage system, The charging power of the energy storage system in time period t. Let be the discharge power of the energy storage system in time period t. The charging status of the energy storage system. This represents the discharge state of the energy storage system. This represents the maximum charge / discharge power of the energy storage system. This refers to the energy stored in the energy storage system before the start of the day. The energy stored in the energy storage system during the Nth time period; Let t be the output of wind power generation in time period t. For the photovoltaic power output in time period t, The maximum available power output of wind power generation in time period t. t represents the maximum available power generated by photovoltaic power generation during the t-th time period.

[0104] Preferably, the maximum state of charge of the energy storage system is determined by the following formula.

[0105]

[0106] The energy storage capacity of the energy storage system in time period t is determined by the following formula.

[0107]

[0108] The maximum capacity required for the energy storage system to assist new energy power plants in tracking AGC commands is determined by the following formula.

[0109]

[0110] In the above formula, Let δ be the energy stored in the energy storage system during time period t-1. ES The self-dissipation rate of the energy storage system. To improve the charging efficiency of energy storage systems. For the discharge efficiency of the energy storage system Let be the energy stored by the energy storage system in the t-th time period of the previous day.

[0111] Preferably, the second acquisition module includes:

[0112] The first acquisition unit is used to acquire the limit constraints of wind power output and photovoltaic power output based on the new energy day-ahead short-term power prediction data and using the scenario generation method and scenario reduction method.

[0113] The second acquisition unit is used to take the day-ahead short-term power forecast data and the day-ahead electricity price clearing data as input, and based on the limit constraints of the wind power output, the limit constraints of the photovoltaic power output and the constraints of the day-ahead collaborative optimization operation model, use a robust optimization method to solve the pre-constructed day-ahead collaborative optimization operation model to obtain the charging / discharging power of the energy storage system at each time period.

[0114] The third acquisition unit is used to obtain the charging / discharging power of the energy storage system at each time period for the day-ahead optimized operation scheme.

[0115] Preferably, the first acquisition unit includes:

[0116] A generation subunit is used to generate a preset number of wind power output scenarios and photovoltaic power output scenarios based on the new energy day-ahead short-term power forecast data using a scenario generation method.

[0117] The first acquisition subunit is used to select typical scenarios from the wind power output scenarios and photovoltaic power output scenarios respectively using the scenario reduction method, and obtain the upper / lower limits of wind power output and photovoltaic power output for each time period.

[0118] The second acquisition subunit is used to acquire the limit constraints of wind power output and photovoltaic power output by using the upper / lower limits of wind power output and photovoltaic power output in each time period, respectively.

[0119] Preferably, the second acquisition subunit is specifically used for:

[0120] The limit constraint condition for the wind power output is determined by the following formula:

[0121]

[0122] The limit constraints on the photovoltaic power generation output are determined by the following formula:

[0123]

[0124] In the above formula, and These represent the upper and lower limits of wind power output in time period t. and These represent the upper and lower limits of photovoltaic power generation output in time period t. The maximum available power output of wind power generation in time period t. t represents the maximum available power generated by photovoltaic power generation during the t-th time period.

[0125] Preferably, the intraday optimized operation model includes:

[0126] The objective function of the intraday optimization operation model is determined by the following formula:

[0127]

[0128] The AGC command deviation or the predicted output deviation of the new energy power station for the g-th time period of the i-th intraday optimized operation is determined by the following formula.

[0129]

[0130] In the above formula, i∈[1,M], where M is the total number of optimization runs per day; g∈[1,N] O ], N O This represents the total number of segments optimized each day. For the AGC instruction in the g-th time period of the i-th day's optimized operation, The predicted power output of the renewable energy power station for the g-th time period of the i-th day's optimized operation. and It is a variable between 0 and 1, and cannot be 1 at the same time; when tracking AGC instructions during the g-th time period of the i-th intraday optimization run, Let g be the AGC command deviation during the g-th period of the i-th day's optimized operation; when tracking the predicted output of the renewable energy power station during the g-th period of the i-th day's optimized operation, The predicted output deviation of the new energy power station during the g-th time period of the i-th day of optimized operation; Let g be the AGC instruction value for the g-th time period of the i-th day's optimized operation. The predicted output value of the renewable energy power station during the g-th time period of the i-th day of optimized operation; The actual output of wind power generation during the g-th time period of the i-th day's optimized operation. The actual output of photovoltaic power generation during the g-th period of the i-th day's optimized operation. The charging power of the energy storage system during the i+g-1th time period of the i-th day's optimized operation. Let the discharge power of the energy storage system be the discharge power of the energy storage system during the (i+g-1)th time period of the (i+g-1)th optimized operation within the day. Let be the change in the charging / discharging power of the energy storage system during the g-th period of the i-th day of optimized operation, relative to the day-ahead optimization results.

[0131] Preferably, the intraday optimized operation model further includes:

[0132] The constraints of the intraday optimized operation model are determined by the following formula:

[0133]

[0134]

[0135]

[0136]

[0137] In the above formula, Let g be the ultra-short-term predicted power value of wind power generation during the g-th period of the i-th intraday optimized operation. Let g be the ultra-short-term predicted power value of photovoltaic power generation during the g-th period of the i-th day's optimized operation. For the state of charge of the energy storage system during the (i+g-1)th time period of the (i+g-1)th day of the (i+g-1)th day of optimized operation, For the state of charge of the energy storage system during the t+g-2 period of the i-th day of optimized operation, Let g be the change in the amount of energy stored in the energy storage system relative to the day-ahead time period during the i-th day of optimized operation. The rated capacity of the energy storage system, This represents the maximum charging and discharging power of the energy storage system.

[0138] Preferably, the change in the amount of stored energy relative to the day-ahead energy storage system during the g-th time period of the i-th day-ahead optimized operation is determined by the following formula.

[0139]

[0140] In the above formula, δ represents the energy stored in the energy storage system during the (g-1)th time period. ES Let be the self-dissipation rate of the energy storage system, and Δg be the duration of a unit time period. To improve the charging efficiency of energy storage systems. Let be the change in the charging / discharging power of the energy storage system during the g-th period of the i-th day's optimized operation, relative to the day-ahead optimization results. The charging / discharging / electrical power of the energy storage system during the g-th period of the i-th day's optimized operation.

[0141] Preferably, the third acquisition module includes:

[0142] The fourth acquisition unit is used to take the day-ahead optimized operation scheme as input, and based on the constraints of the day-ahead optimized operation model, use a mixed integer linear programming method to solve the pre-constructed day-ahead optimized operation model to obtain the change in the charging / discharging power of the energy storage system relative to the day-ahead optimized result in the next scheduling period.

[0143] The fifth acquisition unit is used to add the charging / discharging power of the energy storage system corresponding to the next scheduling period in the day-ahead optimized operation plan to the change in the charging / discharging power of the energy storage system in the next scheduling period relative to the day-ahead optimization result, so as to obtain the final charging / discharging power of the energy storage system in the next scheduling period.

[0144] The sixth acquisition unit is used to obtain the charging / discharging power of the energy storage system for the next scheduling period as the optimized operation result of the next scheduling period.

[0145] According to a third aspect of the embodiments of this application, a computer device is provided, comprising: one or more processors;

[0146] The processor is used to store one or more programs;

[0147] When the one or more programs are executed by the one or more processors, the steps in the above-described method for optimizing the coordinated operation of energy storage and new energy power stations are implemented.

[0148] According to a fourth aspect of the embodiments of this application, a computer-readable storage medium is provided, on which a computer program is stored, wherein when the computer program is executed, the steps in the above-described method for optimizing the coordinated operation of energy storage and new energy power stations are implemented.

[0149] The above-described technical solutions of the present invention have at least one or more of the following beneficial effects:

[0150] By acquiring relevant data from renewable energy power plants and using this data as input, a robust optimization method is used to solve a pre-constructed day-ahead collaborative optimization operation model to obtain a day-ahead optimized operation scheme. Then, using this scheme as input, a mixed-integer linear programming method is used to solve a pre-constructed intraday optimized operation model to obtain the optimized operation results for the next scheduling period. Based on these results, the operation of renewable energy power plants is optimized. The pre-constructed day-ahead collaborative optimization operation model aims to maximize the total electricity sales revenue of renewable energy power plants, while the pre-constructed intraday optimized operation model aims to minimize the deviation of AGC commands or the predicted output deviation of renewable energy power plants. This approach fully leverages the value of energy storage resources, reduces wind or solar curtailment while tracking AGC commands or predicted output, increases electricity sales revenue, reduces performance-based costs, responds to electricity market prices, and improves the overall efficiency of renewable energy power plants. Attached Figure Description

[0151] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0152] Figure 1 This is a flowchart illustrating an optimized method for the coordinated operation of energy storage and new energy power stations according to an exemplary embodiment;

[0153] Figure 2 This is a structural block diagram illustrating a collaborative operation optimization device for energy storage and new energy power stations, according to an exemplary embodiment. Detailed Implementation

[0154] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0155] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0156] As disclosed in the background section, in recent years, the installed capacity and proportion of new energy sources in the power system have been continuously increasing. Due to the randomness of new energy sources, they have brought enormous challenges to the dispatch and operation of the power grid. The new power system with new energy sources as the main body urgently needs to develop frequency regulation resources other than thermal power and hydropower. Currently, both new energy sources and energy storage can participate in frequency regulation ancillary services.

[0157] Coordinated optimization of energy storage and renewable energy power plants refers to the process of rationally formulating output plans for both renewable energy and energy storage, based on known short-term or ultra-short-term output forecasts, to mitigate fluctuations in renewable energy output and track AGC (Automatic Generation Control) commands. Current optimization of renewable energy power plant operation control is primarily from the perspective of the power grid, or within independent microgrids or active distribution networks that include renewable energy generation. When optimizing the control of energy storage and renewable energy power plants from the perspective of the power grid, the optimization objectives are often to mitigate fluctuations in renewable energy output and maximize renewable energy consumption. When optimizing the control of independent microgrids or active distribution networks, the focus is on minimizing the overall regional electricity cost.

[0158] As a high-quality and controllable resource in the power system, energy storage has become a standard feature of new energy power plants and an essential component of the new power system. However, after energy storage is deployed, its operation mode is limited, its utilization rate is low, and it has not significantly increased the revenue of the power plants. Energy storage, as a flexible and controllable resource, possesses multiple functions such as smoothing out fluctuations in new energy output, peak shaving and valley filling, tracking AGC commands, tracking and predicting output, primary frequency regulation, and inertia control. However, these functions can conflict in practice, and some functions, such as fluctuation smoothing and inertia control, have no revenue source.

[0159] To address the aforementioned issues, while tracking and dispatching AGC commands or predicting power output, efforts should be made to reduce wind or solar curtailment at renewable energy power plants, increase revenue from electricity sales, reduce performance-based costs, respond to electricity market prices, and improve the overall efficiency of renewable energy power plants.

[0160] The above plan will be explained in detail below.

[0161] Example 1

[0162] Figure 1 This is a flowchart illustrating an optimized method for the coordinated operation of energy storage and new energy power stations, according to an exemplary embodiment. Figure 1 As shown, this method can be used in a terminal, but is not limited to, and includes the following steps:

[0163] Step 101: Obtain relevant data from new energy power stations;

[0164] Step 102: Using relevant data from new energy power plants as input, solve the pre-constructed day-ahead collaborative optimization operation model using robust optimization methods to obtain the day-ahead optimized operation scheme;

[0165] Step 103: Using the day-ahead optimized operation plan as input, solve the pre-built intraday optimized operation model using the mixed integer linear programming method to obtain the optimized operation results for the next scheduling period;

[0166] Step 104: Optimize the operation of new energy power stations based on the optimized operation results of the next scheduling period;

[0167] Among them, the pre-built day-ahead collaborative optimization operation model is constructed with the goal of maximizing the total electricity sales revenue of new energy power plants;

[0168] The pre-built intraday optimized operation model is constructed with the goal of minimizing the deviation of AGC commands or the deviation of the predicted output of new energy power plants.

[0169] It should be noted that the energy storage and renewable energy power station collaborative operation optimization method provided in this embodiment of the invention fully leverages the utilization value of energy storage resources through day-ahead and intraday collaborative optimization of the energy storage system and renewable energy power station. While tracking and scheduling AGC commands or the predicted output of renewable energy power stations, it reduces wind or solar curtailment at renewable energy power stations, responds to electricity market prices, and increases power station revenue.

[0170] Furthermore, relevant data for renewable energy power plants include: short-term daytime power forecast data for renewable energy and daytime electricity price clearing data;

[0171] The current-day short-term power forecast data for new energy sources includes: the output of wind power generation and the output of photovoltaic power generation in each time period;

[0172] The electricity price clearing data includes: electricity sales prices for each time period.

[0173] In some embodiments, renewable energy power plants may, but are not limited to, obtain renewable energy day-ahead short-term power forecast data by utilizing a pre-set renewable energy day-ahead short-term forecasting system. The method of "obtaining day-ahead short-term power forecast data using a day-ahead short-term forecasting system" involved in the embodiments of this invention is well known to those skilled in the art; therefore, its specific implementation will not be described in detail.

[0174] It is understandable that the electricity price for each time period refers to the electricity price of new energy power plants that operate in conjunction with energy storage systems, meaning that the electricity prices of new energy power plants and energy storage systems are consistent.

[0175] It should be noted that by utilizing day-ahead electricity price clearing data and new energy day-ahead short-term power forecast data, it is possible to reduce wind and solar curtailment while increasing power plant sales revenue, taking into account changes in electricity prices.

[0176] Furthermore, the operating model has been optimized recently, including:

[0177] The objective function of the current-day optimization model is determined by the following formula:

[0178]

[0179] The total electricity sales revenue F of the new energy power station is determined by the following formula. D :

[0180]

[0181] The electricity sales revenue f of the renewable energy power station in time period t is determined by the following formula. i (t):

[0182]

[0183] The operation and maintenance cost f of the new energy power station in time period t is determined by the following formula. e (t):

[0184]

[0185] In the above formula, t∈[1,N], N is the total number of time periods; x and u are control variables, x is a continuous variable, x is the energy storage charging / discharging power, X is a set of continuous variables; u is a 0-1 variable, u is the charging / discharging state of the energy storage system, U is a set of 0-1 variables; w is a random variable, w is the output of wind power / photovoltaic power generation, W is a set of random variables; D is the day-ahead, c sell (t) represents the electricity price for the t-th time period. Let t be the output of wind power generation in time period t. For the photovoltaic power output in time period t, The charging power of the energy storage system in time period t. Let be the discharge power of the energy storage system in time period t, and Δt be the duration of a unit time period; c Wind For the maintenance costs of wind power generation, c PV For the maintenance costs of photovoltaic power generation, c ES This refers to the maintenance costs of the energy storage system.

[0186] In some embodiments, Δt can be, but is not limited to, 15 minutes. When Δt is 15 minutes, a day is divided into 96 time periods.

[0187] It should be noted that the expression of formula (1) is the conventional expression in robust optimization methods, therefore the expression in formula (1) is... This can be understood as: maximizing the total electricity sales revenue of new energy power plants while minimizing the output of wind power and / or photovoltaic power generation. Since x and u are control variables and solutions to the day-ahead optimization model, their specific meanings in formula (1) need not be further explained here.

[0188] Furthermore, recent optimizations to the operating model also include:

[0189] The constraints of the day-ahead optimization model are determined by the following formula:

[0190]

[0191]

[0192]

[0193]

[0194]

[0195]

[0196] In the above formula, t∈[1,N], and N is the total number of time periods; This represents the minimum state of charge of the energy storage system. This represents the maximum state of charge of the energy storage system. Let be the energy stored in the energy storage system during time period t. The rated capacity of the energy storage system, The charging power of the energy storage system in time period t. Let be the discharge power of the energy storage system in time period t. The charging status of the energy storage system. This represents the discharge state of the energy storage system. This represents the maximum charge / discharge power of the energy storage system. This refers to the energy stored in the energy storage system before the start of the day. The energy stored in the energy storage system during the Nth time period; Let t be the output of wind power generation in time period t. For the photovoltaic power output in time period t, The maximum available power output of wind power generation in time period t. t represents the maximum available power generated by photovoltaic power generation during the t-th time period.

[0197] Specifically, the maximum state of charge of the energy storage system is determined by the following formula.

[0198]

[0199] The energy storage capacity of the energy storage system in time period t is determined by the following formula.

[0200]

[0201] The maximum capacity required for the energy storage system to assist new energy power plants in tracking AGC commands is determined by the following formula.

[0202]

[0203] In the above formula, Let δ be the energy stored in the energy storage system during time period t-1. ES The self-dissipation rate of the energy storage system. To improve the charging efficiency of energy storage systems. For the discharge efficiency of the energy storage system Let be the energy stored by the energy storage system in the t-th time period of the previous day.

[0204] It is understandable that the energy stored by the energy storage system in time period t of the previous day... It can be obtained through log records, but the previous day's... The method of obtaining is also through calculation using formula (12).

[0205] In some embodiments, when calculating the maximum capacity required for the energy storage system to assist the new energy power station in tracking AGC commands and calculating the maximum state of charge of the energy storage system, the energy storage system needs to operate in a manner that charges during periods of output and discharges at maximum power during periods of no output.

[0206] It should be noted that the minimum state of charge (SBC) of an energy storage system is generally set by those skilled in the art based on experimental data to ensure the safe use of the energy storage system. For example, the minimum SBC of an energy storage system may be, but is not limited to, 0.1.

[0207] Furthermore, step 102 can be achieved, but is not limited to, through the following process:

[0208] Step 1021: Based on the day-ahead short-term power forecast data of new energy sources, use the scenario generation method and scenario reduction method to obtain the boundary constraints of wind power output and photovoltaic power output.

[0209] Step 1022: Using the day-ahead short-term power forecast data and day-ahead electricity price clearing data as input, based on the limit constraints of wind power output, the limit constraints of photovoltaic power output and the constraints of the day-ahead collaborative optimization operation model, the pre-constructed day-ahead collaborative optimization operation model is solved using the robust optimization method to obtain the charging / discharging power of the energy storage system at each time period.

[0210] Step 1023: The charging / discharging power of the energy storage system at each time period is the day-ahead optimized operation scheme.

[0211] Understandably, by adopting steps 1021-1023 to obtain the day-ahead optimized operation plan, a foundation has been laid for increasing the electricity sales revenue of new energy power plants, reducing the assessment costs of new energy power plants, and improving the overall efficiency of new energy power plants.

[0212] It should be noted that the "robust optimization method" involved in the embodiments of the present invention is well known to those skilled in the art, therefore, its specific implementation will not be described in detail.

[0213] Further, step 1021 includes:

[0214] Step 1021a: Based on the day-ahead short-term power forecast data of new energy sources, generate a preset number of wind power output scenarios and photovoltaic power output scenarios using the scenario generation method;

[0215] In some embodiments, the scene generation method may be, but is not limited to, Monte Carlo or Latin hypercube;

[0216] Step 1021b: Using the scenario reduction method, select typical scenarios from the wind power output scenarios and the photovoltaic power output scenarios respectively to obtain the upper / lower limits of wind power output and photovoltaic power output for each time period.

[0217] In some embodiments, the scene reduction method may be, but is not limited to, K-means;

[0218] Step 1021c: Obtain the limit constraints on wind power output and photovoltaic power output by using the upper / lower limits of wind power output and photovoltaic power output for each time period, respectively.

[0219] Specifically, the limit constraints on wind power output are determined by the following formula:

[0220]

[0221] The limit constraints for photovoltaic power generation output are determined by the following formula:

[0222]

[0223] In the above formula, and These represent the upper and lower limits of wind power output in time period t. and These represent the upper and lower limits of photovoltaic power generation output in time period t. The maximum available power output of wind power generation in time period t. t represents the maximum available power generated by photovoltaic power generation during the t-th time period.

[0224] It should be noted that the "scene generation method" and "scene reduction method" involved in the embodiments of the present invention are well known to those skilled in the art, therefore, their specific implementation methods will not be described in detail.

[0225] Further, the intraday optimized operating model includes:

[0226] The objective function of the intraday optimization model is determined by the following formula:

[0227]

[0228] The AGC command deviation or the predicted output deviation of the new energy power station for the g-th time period of the i-th intraday optimized operation is determined by the following formula.

[0229]

[0230] In the above formula, i∈[1,M], where M is the total number of optimization runs per day; g∈[1,N] O ], N O This represents the total number of segments optimized each day. For the AGC instruction in the g-th time period of the i-th day's optimized operation, The predicted power output of the renewable energy power station for the g-th time period of the i-th day's optimized operation. and It is a variable between 0 and 1, and cannot be 1 at the same time; when tracking AGC instructions during the g-th time period of the i-th intraday optimization run, Let g be the AGC command deviation during the g-th period of the i-th day's optimized operation; when tracking the predicted output of the renewable energy power station during the g-th period of the i-th day's optimized operation, The predicted output deviation of the new energy power station during the g-th time period of the i-th day of optimized operation; Let g be the AGC instruction value for the g-th time period of the i-th day's optimized operation. The predicted output value of the renewable energy power station during the g-th time period of the i-th day of optimized operation; The actual output of wind power generation during the g-th time period of the i-th day's optimized operation. The actual output of photovoltaic power generation during the g-th period of the i-th day's optimized operation. The charging power of the energy storage system during the i+g-1th time period of the i-th day's optimized operation. Let the discharge power of the energy storage system be the discharge power of the energy storage system during the (i+g-1)th time period of the (i+g-1)th optimized operation within the day. Let be the change in the charging / discharging power of the energy storage system during the g-th period of the i-th day of optimized operation, relative to the day-ahead optimization results.

[0231] It should be noted that the AGC command is issued by the power grid dispatching agency, and the predicted output of the renewable energy power plants is obtained using the renewable energy day-ahead short-term forecasting system pre-installed at the renewable energy power plants.

[0232] It should also be noted that on a typical operation day, optimization is generally performed every 15 minutes, with each optimization lasting 2 hours. Each 15-minute optimization period ends after the current period's optimization results are applied. During each optimization run, feedback corrections are required based on measured renewable energy output data, energy storage capacity, and State of Charge (SOC) to ensure the accuracy of the daily optimization operation. Therefore, multiple daily optimization runs are necessary each day.

[0233] Furthermore, the intraday optimized operating model also includes:

[0234] The constraints of the intraday optimization operation model are determined by the following formula:

[0235]

[0236]

[0237]

[0238]

[0239] In the above formula, Let g be the ultra-short-term predicted power value of wind power generation during the g-th period of the i-th intraday optimized operation. Let g be the ultra-short-term predicted power value of photovoltaic power generation during the g-th period of the i-th day's optimized operation. For the state of charge of the energy storage system during the (i+g-1)th time period of the (i+g-1)th day of the (i+g-1)th day of optimized operation, For the state of charge of the energy storage system during the t+g-2 period of the i-th day of optimized operation, Let g be the change in the amount of energy stored in the energy storage system relative to the day-ahead time period during the i-th day of optimized operation. The rated capacity of the energy storage system, This represents the maximum charging and discharging power of the energy storage system.

[0240] The change in the energy storage capacity of the energy storage system relative to the day-ahead energy storage system during the g-th time period of the i-th intraday optimization operation is determined by the following formula.

[0241]

[0242] In the above formula, δ represents the energy stored in the energy storage system during the (g-1)th time period. ES Let be the self-dissipation rate of the energy storage system, and Δg be the duration of a unit time period. To improve the charging efficiency of energy storage systems. Let be the change in the charging / discharging power of the energy storage system during the g-th period of the i-th day's optimized operation, relative to the day-ahead optimization results. The charging / discharging / electrical power of the energy storage system during the g-th period of the i-th day's optimized operation.

[0243] It should be noted that the ultra-short-term predicted power values ​​of wind power generation during the g-th period of the i-th day-on-day optimization and the ultra-short-term predicted power values ​​of photovoltaic power generation during the g-th period of the i-th day-on-day optimization are obtained using the new energy day-on-day short-term prediction system pre-set at the new energy power station.

[0244] In some embodiments, but not limited to, the real-time state of charge of the energy storage system, the ultra-short-term predicted output of the new energy source, and AGC commands can be obtained through the energy management system of the new energy power station. When the optimized operation results for the next scheduling period are obtained, execution commands can be issued to the new energy power station to achieve the optimization of the operation of the new energy power station.

[0245] Furthermore, step 103 can be implemented, but is not limited to, through the following process:

[0246] Step 1031: Taking the day-ahead optimized operation scheme as input, and based on the constraints of the intraday optimized operation model, use the mixed integer linear programming method to solve the pre-constructed intraday optimized operation model to obtain the change in the charging / discharging power of the energy storage system relative to the day-ahead optimized result in the next scheduling period;

[0247] Step 1032: Add the charging / discharging power of the energy storage system corresponding to the next scheduling period in the day-ahead optimized operation plan to the change in the charging / discharging power of the energy storage system in the next scheduling period relative to the day-ahead optimization results to obtain the final charging / discharging power of the energy storage system in the next scheduling period;

[0248] Step 1033: The final charging / discharging power of the energy storage system in the next scheduling period is the optimized operation result of the next scheduling period.

[0249] It should be noted that the "mixed integer linear programming method" involved in the embodiments of the present invention is well known to those skilled in the art, therefore, its specific implementation will not be described in detail.

[0250] This invention provides a method for the coordinated operation optimization of energy storage and renewable energy power plants, proposing a method for coordinated optimization operation that responds to the day-ahead electricity spot market and tracks AGC output and output prediction curves intraday. Intraday, energy storage is charged when AGC output is limited; when AGC output is unlimited, energy storage discharges considering predicted output deviations. Day-ahead, based on the energy storage utilization of the previous day or several days, and reserving the energy storage capacity required for intraday operation, coordinated optimization operation of renewable energy and energy storage is performed in response to market electricity prices. This achieves the overall goal of increasing the revenue of renewable energy power plants by participating in the day-ahead electricity spot market and tracking AGC commands and output prediction curves intraday, thereby improving renewable energy consumption, reducing wind and solar curtailment, increasing electricity sales revenue of renewable energy power plants, and reducing assessment costs. However, the capacity and power of energy storage are limited. Therefore, considering intraday tracking of AGC commands and output prediction curves to reduce wind or solar curtailment, the remaining capacity of energy storage is utilized to participate in the day-ahead spot market, charging during low-price periods and selling electricity during high-price periods to maximize electricity sales revenue.

[0251] This invention provides a method for optimizing the coordinated operation of energy storage and new energy power plants. By acquiring relevant data from the new energy power plants and using this data as input, a robust optimization method is used to solve a pre-constructed day-ahead coordinated optimization operation model to obtain a day-ahead optimized operation scheme. Then, using this scheme as input, a mixed-integer linear programming method is used to solve a pre-constructed intraday optimized operation model to obtain the optimized operation result for the next scheduling period. Based on the optimized operation result for the next scheduling period, the operation of the new energy power plants is optimized. The pre-constructed day-ahead coordinated optimization operation model is built with the goal of maximizing the total electricity sales revenue of the new energy power plants, while the pre-constructed intraday optimized operation model is built with the goal of minimizing the deviation of AGC commands or the deviation of the predicted output of the new energy power plants. This method can fully utilize the value of energy storage resources, reduce wind or solar curtailment at new energy power plants while tracking scheduling AGC commands or predicted output, increase electricity sales revenue, reduce power plant assessment costs, respond to electricity market prices, and improve the overall efficiency of new energy power plants.

[0252] Example 2

[0253] To support the aforementioned method for optimizing the coordinated operation of energy storage and new energy power plants, this invention provides a device for optimizing the coordinated operation of energy storage and new energy power plants, referring to... Figure 2 The device includes:

[0254] The first acquisition module is used to acquire relevant data from new energy power stations;

[0255] The second acquisition module is used to take relevant data from new energy power plants as input, and use robust optimization methods to solve the pre-constructed day-ahead collaborative optimization operation model to obtain the day-ahead optimization operation scheme.

[0256] The third acquisition module is used to take the day-ahead optimized operation plan as input, and use mixed integer linear programming method to solve the pre-built intraday optimized operation model to obtain the optimized operation results for the next scheduling period.

[0257] The optimization module is used to optimize the operation of new energy power stations based on the optimization results of the next scheduling period.

[0258] Among them, the pre-built day-ahead collaborative optimization operation model is constructed with the goal of maximizing the total electricity sales revenue of new energy power plants;

[0259] The pre-built intraday optimized operation model is constructed with the goal of minimizing the deviation of AGC commands or the deviation of the predicted output of new energy power plants.

[0260] Furthermore, relevant data for renewable energy power plants include: short-term daytime power forecast data for renewable energy and daytime electricity price clearing data;

[0261] The current-day short-term power forecast data for new energy sources includes: the output of wind power generation and the output of photovoltaic power generation in each time period;

[0262] The electricity price clearing data includes: electricity sales prices for each time period.

[0263] Furthermore, the operating model has been optimized recently, including:

[0264] The objective function of the current-day optimization model is determined by the following formula:

[0265]

[0266] The total electricity sales revenue F of the new energy power station is determined by the following formula. D :

[0267]

[0268] The electricity sales revenue f of the renewable energy power station in time period t is determined by the following formula. i (t):

[0269]

[0270] The operation and maintenance cost f of the new energy power station in time period t is determined by the following formula. e (t):

[0271]

[0272] In the above formula, t∈[1,N], N is the total number of time periods; x and u are control variables, x is a continuous variable, x is the energy storage charging / discharging power, X is a set of continuous variables; u is a 0-1 variable, u is the charging / discharging state of the energy storage system, U is a set of 0-1 variables; w is a random variable, w is the output of wind power / photovoltaic power generation, W is a set of random variables; D is the day-ahead, c sell (t) represents the electricity price for the t-th time period. Let t be the output of wind power generation in time period t. For the photovoltaic power output in time period t, The charging power of the energy storage system in time period t. Let be the discharge power of the energy storage system in time period t, and Δt be the duration of a unit time period; c Wind For the maintenance costs of wind power generation, c PV For the maintenance costs of photovoltaic power generation, c ES This refers to the maintenance costs of the energy storage system.

[0273] Furthermore, recent optimizations to the operating model also include:

[0274] The constraints of the day-ahead optimization model are determined by the following formula:

[0275]

[0276]

[0277]

[0278]

[0279]

[0280]

[0281] In the above formula, t∈[1,N], and N is the total number of time periods; This represents the minimum state of charge of the energy storage system. This represents the maximum state of charge of the energy storage system. Let be the energy stored in the energy storage system during time period t. The rated capacity of the energy storage system, The charging power of the energy storage system in time period t. Let be the discharge power of the energy storage system in time period t. The charging status of the energy storage system. This represents the discharge state of the energy storage system. This represents the maximum charge / discharge power of the energy storage system. This refers to the energy stored in the energy storage system before the start of the day. The energy stored in the energy storage system during the Nth time period; Let t be the output of wind power generation in time period t. For the photovoltaic power output in time period t, The maximum available power output of wind power generation in time period t. t represents the maximum available power generated by photovoltaic power generation during the t-th time period.

[0282] Specifically, the maximum state of charge of the energy storage system is determined by the following formula.

[0283]

[0284] The energy storage capacity of the energy storage system in time period t is determined by the following formula.

[0285]

[0286] The maximum capacity required for the energy storage system to assist new energy power plants in tracking AGC commands is determined by the following formula.

[0287]

[0288] In the above formula, Let δ be the energy stored in the energy storage system during time period t-1. ES The self-dissipation rate of the energy storage system. To improve the charging efficiency of energy storage systems. For the discharge efficiency of the energy storage system Let be the energy stored by the energy storage system in the t-th time period of the previous day.

[0289] Furthermore, the second acquisition module includes:

[0290] The first acquisition unit is used to acquire the limit constraints of wind power output and photovoltaic power output based on the day-ahead short-term power forecast data of new energy sources, using the scenario generation method and the scenario reduction method.

[0291] The second acquisition unit is used to take the day-ahead short-term power forecast data and day-ahead electricity price clearing data as input, and based on the limit constraints of wind power output, the limit constraints of photovoltaic power output and the constraints of the day-ahead collaborative optimization operation model, use the robust optimization method to solve the pre-constructed day-ahead collaborative optimization operation model to obtain the charging / discharging power of the energy storage system at each time period.

[0292] The third acquisition unit is used to obtain the charging / discharging power of the energy storage system at different times for day-ahead optimized operation.

[0293] Furthermore, the first acquisition unit includes:

[0294] The generation sub-unit is used to generate a preset number of wind power output scenarios and photovoltaic power output scenarios based on the day-ahead short-term power forecast data of new energy sources and using the scenario generation method.

[0295] The first acquisition subunit is used to select typical scenarios from wind power output scenarios and photovoltaic power output scenarios respectively using the scenario reduction method, and obtain the upper / lower limits of wind power output and photovoltaic power output for each time period.

[0296] The second acquisition subunit is used to obtain the limit constraints of wind power output and photovoltaic power output by utilizing the upper / lower limits of wind power output and photovoltaic power output for each time period, respectively.

[0297] Specifically, the second acquisition subunit is used for:

[0298] The limit constraints for wind power output are determined by the following formula:

[0299]

[0300] The limit constraints for photovoltaic power generation output are determined by the following formula:

[0301]

[0302] In the above formula, and These represent the upper and lower limits of wind power output in time period t. and These represent the upper and lower limits of photovoltaic power generation output in time period t. The maximum available power output of wind power generation in time period t. t represents the maximum available power generated by photovoltaic power generation during the t-th time period.

[0303] Further, the intraday optimized operating model includes:

[0304] The objective function of the intraday optimization model is determined by the following formula:

[0305]

[0306] The AGC command deviation or the predicted output deviation of the new energy power station for the g-th time period of the i-th intraday optimized operation is determined by the following formula.

[0307]

[0308] In the above formula, i∈[1,M], where M is the total number of optimization runs per day; g∈[1,N] O ], N O This represents the total number of segments optimized each day. For the AGC instruction in the g-th time period of the i-th day's optimized operation, The predicted power output of the renewable energy power station for the g-th time period of the i-th day's optimized operation. and It is a variable between 0 and 1, and cannot be 1 at the same time; when tracking AGC instructions during the g-th time period of the i-th intraday optimization run. Let g be the AGC command deviation during the g-th period of the i-th day's optimized operation; when tracking the predicted output of the renewable energy power station during the g-th period of the i-th day's optimized operation, The predicted output deviation of the new energy power station during the g-th time period of the i-th day of optimized operation; Let g be the AGC instruction value for the g-th time period of the i-th day's optimized operation. The predicted output value of the renewable energy power station during the g-th time period of the i-th day of optimized operation; The actual output of wind power generation during the g-th time period of the i-th day's optimized operation. The actual output of photovoltaic power generation during the g-th period of the i-th day's optimized operation. The charging power of the energy storage system during the i+g-1th time period of the i-th day's optimized operation. Let the discharge power of the energy storage system be the discharge power of the energy storage system during the (i+g-1)th time period of the (i+g-1)th optimized operation within the day. Let be the change in the charging / discharging power of the energy storage system during the g-th period of the i-th day of optimized operation, relative to the day-ahead optimization results.

[0309] Furthermore, the intraday optimized operating model also includes:

[0310] The constraints of the intraday optimization operation model are determined by the following formula:

[0311]

[0312]

[0313]

[0314]

[0315] In the above formula, Let g be the ultra-short-term predicted power value of wind power generation during the g-th period of the i-th intraday optimized operation. Let g be the ultra-short-term predicted power value of photovoltaic power generation during the g-th period of the i-th day's optimized operation. For the state of charge of the energy storage system during the (i+g-1)th time period of the (i+g-1)th day of the (i+g-1)th day of optimized operation, For the state of charge of the energy storage system during the t+g-2 period of the i-th day of optimized operation, Let g be the change in the amount of energy stored in the energy storage system relative to the day-ahead time period during the i-th day of optimized operation. The rated capacity of the energy storage system, This represents the maximum charging and discharging power of the energy storage system.

[0316] Specifically, the change in the amount of stored energy relative to the day-ahead energy storage system during the g-th time period of the i-th day-ahead optimized operation is determined by the following formula.

[0317]

[0318] In the above formula, δ represents the energy stored in the energy storage system during the (g-1)th time period. ES Let be the self-dissipation rate of the energy storage system, and Δg be the duration of a unit time period. To improve the charging efficiency of energy storage systems. Let be the change in the charging / discharging power of the energy storage system during the g-th period of the i-th day's optimized operation, relative to the day-ahead optimization results. The charging / discharging / electrical power of the energy storage system during the g-th period of the i-th day's optimized operation.

[0319] Furthermore, the third acquisition module includes:

[0320] The fourth acquisition unit is used to take the day-ahead optimized operation plan as input, and based on the constraints of the day-ahead optimized operation model, use mixed integer linear programming to solve the pre-constructed day-ahead optimized operation model to obtain the change in the charging / discharging power of the energy storage system relative to the day-ahead optimized result in the next scheduling period.

[0321] The fifth acquisition unit is used to add the charging / discharging power of the energy storage system corresponding to the next scheduling period in the day-ahead optimized operation plan to the change in the charging / discharging power of the energy storage system in the next scheduling period relative to the day-ahead optimization result, so as to obtain the final charging / discharging power of the energy storage system in the next scheduling period.

[0322] The sixth acquisition unit is used to obtain the final charging / discharging power of the energy storage system for the next scheduling period as the optimized operation result for the next scheduling period.

[0323] This invention provides a method for optimizing the coordinated operation of energy storage and new energy power plants. A first acquisition module acquires relevant data from the new energy power plants. A second acquisition module uses this data as input and solves a pre-constructed day-ahead coordinated optimization operation model using a robust optimization method to obtain a day-ahead optimized operation scheme. A third acquisition module uses this scheme as input and solves a pre-constructed intraday optimized operation model using a mixed-integer linear programming method to obtain the optimized operation result for the next scheduling period. The optimization module optimizes the operation of the new energy power plants based on the optimized operation result for the next scheduling period. The pre-constructed day-ahead coordinated optimization operation model aims to maximize the total electricity sales revenue of the new energy power plants, while the pre-constructed intraday optimized operation model aims to minimize the deviation of AGC commands or the deviation of the predicted output of the new energy power plants. This method can fully utilize the value of energy storage resources, reduce wind or solar curtailment at new energy power plants while tracking scheduling AGC commands or predicted output, increase electricity sales revenue, reduce power plant assessment costs, respond to electricity market prices, and improve the overall efficiency of new energy power plants.

[0324] It is understood that the device embodiments provided above correspond to the method embodiments described above, and the specific details can be referred to each other, which will not be repeated here.

[0325] It is understood that the same or similar parts in the above embodiments can be referred to each other, and the contents not described in detail in some embodiments can be referred to the same or similar contents in other embodiments.

[0326] Example 3

[0327] Based on the same inventive concept, this invention also provides a computer device, which includes a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the computer storage medium to achieve corresponding method flows or corresponding functions, thereby realizing the steps of the coordinated operation optimization method for energy storage and new energy power stations in the above embodiments.

[0328] Example 4

[0329] Based on the same inventive concept, this invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the steps of the optimized method for the coordinated operation of energy storage and new energy power stations in the above embodiments.

[0330] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0331] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0332] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0333] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0334] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for optimizing the coordinated operation of energy storage and new energy stations, characterized in that, The method comprises: acquiring relevant data of a new energy station; using a robust optimization method to solve a pre-constructed day-ahead collaborative optimization operation model to obtain a day-ahead optimization operation scheme, with the relevant data of the new energy station as input; using a mixed integer linear programming method to solve a pre-constructed day-ahead optimization operation model to obtain an optimization operation result of a next scheduling period, with the day-ahead optimization operation scheme as input; performing new energy station operation optimization according to the optimization operation result of the next scheduling period; wherein the pre-constructed day-ahead collaborative optimization operation model is constructed with the maximum total electricity sales revenue of the new energy station as a target; the pre-constructed day-ahead optimization operation model is constructed with the minimum AGC instruction deviation or the minimum predicted output deviation of the new energy station as a target; the day-ahead optimization operation model comprises: a target function of the day-ahead optimization operation model is determined according to the following formula: AGC command deviation of the gth time period of the ith day or the predicted output deviation of the new energy station is determined according to the following formula : In the above formula, i∈[1,M], where M is the total number of optimization runs per day; g∈[1,N] O ], N O This represents the total number of segments optimized each day. For the AGC instruction in the g-th time period of the i-th day's optimized operation, The predicted power output of the renewable energy power station for the g-th time period of the i-th day's optimized operation. and It is a variable between 0 and 1, and cannot be 1 at the same time; when tracking AGC instructions during the g-th time period of the i-th intraday optimization run, Let g be the AGC command deviation during the g-th period of the i-th day's optimized operation; when tracking the predicted output of the renewable energy power station during the g-th period of the i-th day's optimized operation, The predicted output deviation of the new energy power station during the g-th time period of the i-th day of optimized operation; Let g be the AGC instruction value for the g-th time period of the i-th day's optimized operation. The predicted output value of the renewable energy power station during the g-th time period of the i-th day of optimized operation; The actual output of wind power generation during the g-th time period of the i-th day's optimized operation. The actual output of photovoltaic power generation during the g-th period of the i-th day's optimized operation. The charging power of the energy storage system during the i+g-1th time period of the i-th day's optimized operation. Let the discharge power of the energy storage system be the discharge power of the energy storage system during the (i+g-1)th time period of the (i+g-1)th optimized operation within the day. The change in the charging or discharging power of the energy storage system during the g-th period of the i-th day of optimized operation is relative to the previous day's optimization results.

2. The method of claim 1, wherein, the relevant data of the new energy station comprises new energy day-ahead short-term power prediction data and day-ahead electricity price clearing data; the new energy day-ahead short-term power prediction data comprises wind power output of each period and photovoltaic power output of each period; the day-ahead electricity price clearing data comprises electricity sales price of each period.

3. The method of claim 2, wherein, the day-ahead collaborative optimization operation model comprises: a target function of the day-ahead collaborative optimization operation model is determined according to the following formula: The total electricity selling revenue F of the new energy station is determined according to the following formula D : wherein the electricity sale revenue f of the new energy station in the tth time period is determined according to the following formula i (t): The operation and maintenance cost f of the new energy station in the t period is determined by the following formula e (t): In the above formula, t∈[1, N], N is the total number of time periods; x and u are control variables, x is a continuous variable, x is the charging or discharging power of the energy storage, X is the set of continuous variables; u is a 0-1 variable, u is the charging or discharging state of the energy storage system, U is the set of 0-1 variables; w is a random variable, w is the output of wind power or photovoltaic power, W is the set of random variables; D is day-ahead, c sell (t) is the electricity selling price of the t time period, is the output of wind power in the t time period, is the output of photovoltaic power in the t time period, is the charging power of the energy storage system in the t time period, is the discharging power of the energy storage system in the t time period, Δt is the length of the unit time period; c Wind is the maintenance cost of wind power, c PV is the maintenance cost of photovoltaic power, c ES is the maintenance cost of the energy storage system.

4. The method of claim 3, wherein, the day-ahead collaborative optimization operation model further comprises: a constraint condition of the day-ahead collaborative optimization operation model is determined according to the following formula: In the above formula, t ∈ [1, N], N is the total number of time periods; SoSminis the minimum state of charge of the energy storage system, SoSmaxis the maximum state of charge of the energy storage system, SoE(t) is the energy storage energy of the energy storage system in the tth time period, SoCmaxis the rated capacity of the energy storage system, SoC(t) is the charging power of the energy storage system in the tth time period, SoD(t) is the discharging power of the energy storage system in the tth time period, SoS(t) is the state of charge of the energy storage system, SoD(t) is the state of discharge of the energy storage system, SoPmaxis the maximum charging or discharging power of the energy storage system, SoE0is the energy storage energy of the energy storage system before the beginning of the day, SoE(N) is the energy storage energy of the energy storage system in the Nth time period; Pwind(t) is the output of wind power generation in the tth time period, Ppv(t) is the output of photovoltaic power generation in the tth time period, Pwind,max(t) is the maximum available power of wind power generation in the tth time period, Ppv,max(t) is the maximum available power of photovoltaic power generation in the tth time period.

5. The method of claim 4, wherein, The maximum state of charge of the energy storage system is determined as follows : The energy storage energy of the energy storage system for the t-th period is determined according to the following formula : Wherein the maximum capacity required for the energy storage system to assist the new energy station in tracking the AGC instruction is determined according to the following formula : In the above formula, is the energy storage energy of the energy storage system at the t-1 period, δ ES is the self-dissipation rate of the energy storage system, is the charging efficiency of the energy storage system, is the discharging efficiency of the energy storage system, is the energy storage energy of the energy storage system at the t period of the previous day.

6. The method of claim 5, wherein, the day-ahead optimization operation scheme is obtained by using a robust optimization method to solve the pre-constructed day-ahead collaborative optimization operation model, with the relevant data of the new energy station as input, comprising: limit constraint conditions of wind power output and limit constraint conditions of photovoltaic power output are obtained by using a scenario generation method and a scenario reduction method based on the new energy day-ahead short-term power prediction data; each period of the charging or discharging power of the energy storage system is obtained by using a robust optimization method to solve the pre-constructed day-ahead collaborative optimization operation model, with the day-ahead short-term power prediction data and the day-ahead electricity price clearing data as input, based on the limit constraint conditions of wind power output, the limit constraint conditions of photovoltaic power output and the constraint conditions of the day-ahead collaborative optimization operation model; the charging or discharging power of the energy storage system in each period is the day-ahead optimization operation scheme.

7. The method of claim 6, wherein, the limit constraint conditions of wind power output and the limit constraint conditions of photovoltaic power output are obtained by using a scenario generation method and a scenario reduction method based on the new energy day-ahead short-term power prediction data, comprising: a preset number of wind power output scenarios and photovoltaic power output scenarios are generated by using a scenario generation method based on the new energy day-ahead short-term power prediction data; upper or lower limits of wind power output of each period and upper or lower limits of photovoltaic power output of each period are obtained by using a scenario reduction method to select typical scenarios from the wind power output scenarios and the photovoltaic power output scenarios, respectively; The upper limit or lower limit of the wind power generation output of each time period and the upper limit or lower limit of the photovoltaic power generation output of each time period are used to obtain a wind power generation output limit constraint condition and a photovoltaic power generation output limit constraint condition.

8. The method of claim 7, wherein, The wind power generation output limit constraint condition is determined according to the following formula: The photovoltaic power generation output limit constraint condition is determined according to the following formula: In the above formulae, and are the upper or lower limits of the output of wind power generation in the tth period, respectively, and are the upper or lower limits of the output of photovoltaic power generation in the tth period, respectively, is the maximum available power of wind power generation in the tth period, is the maximum available power of photovoltaic power generation in the tth period.

9. The method of claim 1, wherein, The intra-day optimization operation model further comprises: The constraint condition of the intra-day optimization operation model is determined according to the following formula: in the above formula, is the ultra-short-term predicted power value of the wind power generation of the gth period of the ith day for the optimization operation, is the ultra-short-term predicted power value of the photovoltaic power generation of the gth period of the ith day for the optimization operation, is the state of charge of the energy storage system of the ith+g-1 period of the ith day for the optimization operation, is the state of charge of the energy storage system of the ith+g-2 period of the ith day for the optimization operation, is the change amount of the energy storage capacity of the energy storage system relative to the day-ahead energy storage system at the gth period of the ith day for the optimization operation, is the rated capacity of the energy storage system, is the maximum charge and discharge power of the energy storage system.

10. The method of claim 9, wherein, The change amount of the power storage amount of the day-ahead energy storage system at the gth time phase of the ith day-ahead optimization operation is determined according to the following formula : In the above formula, is the energy storage amount of the energy storage system in the gth time period, and ES is the self-dissipation rate of the energy storage system, and is the charging efficiency of the energy storage system, is the change amount of the charging or discharging power of the energy storage system in the gth time period in the ith day-ahead optimization operation relative to the day-ahead optimization result, is the charging or discharging power of the energy storage system in the gth time period in the ith day-ahead optimization operation.

11. The method of claim 10, wherein, The intra-day optimization operation model is solved by using a mixed integer linear programming method based on the constraint condition of the intra-day optimization operation model and the day-ahead optimization operation scheme, to obtain a change amount of the charging or discharging power of the energy storage system in the next scheduling time period relative to the day-ahead optimization result. The charging or discharging power of the energy storage system in the next scheduling time period in the day-ahead optimization operation scheme is added to the change amount of the charging or discharging power of the energy storage system in the next scheduling time period relative to the day-ahead optimization result, to obtain the final charging or discharging power of the energy storage system in the next scheduling time period. The final charging or discharging power of the energy storage system in the next scheduling time period is the optimization result in the next scheduling time period. The device comprises:

12. A device for optimizing the coordinated operation of energy storage and new energy stations, characterized in that, A first obtaining module is configured to obtain relevant data of a new energy station. A second obtaining module is configured to solve a pre-constructed day-ahead collaborative optimization operation model by using a robust optimization method based on the relevant data of the new energy station, to obtain a day-ahead optimization operation scheme. A third obtaining module is configured to solve a pre-constructed intra-day optimization operation model by using a mixed integer linear programming method based on the day-ahead optimization operation scheme, to obtain an optimization result in a next scheduling time period. An optimization module is configured to perform new energy station operation optimization according to the optimization result in the next scheduling time period. The pre-constructed day-ahead collaborative optimization operation model is constructed with the maximum total new energy station power selling revenue as a target. The pre-constructed intra-day optimization operation model is constructed with the minimum AGC instruction deviation or the minimum new energy station predicted output deviation as a target. The intra-day optimization operation model comprises: A target function of the intra-day optimization operation model is determined according to the following formula: The relevant data of the new energy station comprises new energy day-ahead short-term power prediction data and day-ahead electricity price clearing data. AGC command deviation of the gth time period of the ith day or the predicted output deviation of the new energy station is determined according to the following formula In the above formula, i∈[1,M], M is the total number of daily intra-day optimization operations; g∈[1,N O ], N O is the total number of segments of each daily intra-day optimization operation; is the AGC instruction of the gth segment of the ith daily intra-day optimization operation, is the predicted output of the new energy station of the gth segment of the ith daily intra-day optimization operation, and are 0-1 variables and cannot be 1 at the same time; when the gth segment of the ith daily intra-day optimization operation tracks the AGC instruction, is the AGC instruction deviation of the gth segment of the ith daily intra-day optimization operation; when the gth segment of the ith daily intra-day optimization operation tracks the predicted output of the new energy station, is the predicted output deviation of the new energy station of the gth segment of the ith daily intra-day optimization operation; is the AGC instruction value of the gth segment of the ith daily intra-day optimization operation, is the predicted output value of the new energy station of the gth segment of the ith daily intra-day optimization operation; is the actual output of wind power generation of the gth segment of the ith daily intra-day optimization operation, is the actual output of photovoltaic power generation of the gth segment of the ith daily intra-day optimization operation, is the charging power of the energy storage system of the ith+g-1 segment of the ith daily intra-day optimization operation, is the discharging power of the energy storage system of the ith+g-1 segment of the ith daily intra-day optimization operation, is the change amount of the charging or discharging power of the energy storage system of the gth segment of the ith daily intra-day optimization operation relative to the day-ahead optimization result.

13. The apparatus of claim 12, wherein, The new energy day-ahead short-term power prediction data comprises wind power generation output of each time period and photovoltaic power generation output of each time period. The day-ahead electricity price clearing data comprises a power selling price of each time period. The day-ahead collaborative optimization operation model comprises:

14. The apparatus of claim 13, wherein, A target function of the day-ahead collaborative optimization operation model is determined according to the following formula: The day-ahead collaborative optimization operation model further comprises: The total electricity selling revenue F of the new energy station is determined according to the following formula D : wherein the electricity sale revenue f of the new energy station in the tth time period is determined according to the following formula i (t): The operation and maintenance cost f of the new energy station in the t period is determined according to the following formula e (t): In the above formula, t ∈ [1, N], N is the total number of time periods; x and u are control variables, x is a continuous variable, x is the charging or discharging power of the energy storage, X is the set of continuous variables; u is a 0-1 variable, u is the charging or discharging state of the energy storage system, U is the set of 0-1 variables; w is a random variable, w is the output of wind power or photovoltaic power, W is the set of random variables; D is day-ahead, c sell (t) is the electricity selling price of the t time period, is the output of wind power of the t time period, is the output of photovoltaic power of the t time period, is the charging power of the energy storage system of the t time period, is the discharging power of the energy storage system of the t time period, Δt is the length of the unit time period; c Wind is the maintenance cost of wind power, c PV is the maintenance cost of photovoltaic power, c ES is the maintenance cost of the energy storage system.

15. The apparatus of claim 14, wherein, A constraint condition of the day-ahead collaborative optimization operation model is determined according to the following formula: The second obtaining module comprises: In the above formula, t ∈ [1, N], N is the total number of time periods; SoSminis the minimum state of charge of the energy storage system, SoSmaxis the maximum state of charge of the energy storage system, SoE(t) is the energy storage energy of the energy storage system in the tth time period, SoC is the rated capacity of the energy storage system, SoC(t) is the charging power of the energy storage system in the tth time period, SoD(t) is the discharging power of the energy storage system in the tth time period, SoS is the state of charge of the energy storage system, SoD is the state of discharge of the energy storage system, SoP is the maximum charging or discharging power of the energy storage system, SoE0is the energy storage energy of the energy storage system before the beginning of the day, SoENis the energy storage energy of the energy storage system in the Nth time period; Pwind(t) is the output of wind power generation in the tth time period, Ppv(t) is the output of photovoltaic power generation in the tth time period, Pwind,max(t) is the maximum available power of wind power generation in the tth time period, Ppv,max(t) is the maximum available power of photovoltaic power generation in the tth time period.

16. The apparatus of claim 15, wherein, The maximum state of charge of the energy storage system is determined as follows : The energy storage energy of the energy storage system for the t-th period is determined according to the following formula : In the formula, the maximum capacity required for the energy storage system to assist the new energy station in tracking the AGC instruction is determined according to the following formula In the above formula, is the energy storage energy of the energy storage system at the t-1 period, δ ES is the self-dissipation rate of the energy storage system, is the charging efficiency of the energy storage system, is the discharging efficiency of the energy storage system, is the energy storage energy of the energy storage system at the t period of the previous day.

17. The apparatus of claim 16, wherein, ​ The first obtaining unit is configured to obtain limit constraint conditions of wind power generation output and limit constraint conditions of photovoltaic power generation output based on the new energy day-ahead short-term power prediction data by using a scenario generation method and a scenario reduction method. The second obtaining unit is configured to take the day-ahead short-term power prediction data and the day-ahead electricity price clearing data as input, and obtain charging or discharging power of the energy storage system at each time period by solving a pre-constructed day-ahead collaborative optimization operation model based on the limit constraint conditions of the wind power generation output, the limit constraint conditions of the photovoltaic power generation output, and constraint conditions of the day-ahead collaborative optimization operation model by using a robust optimization method. The third obtaining unit is configured to take the charging or discharging power of the energy storage system at each time period as the day-ahead optimization operation scheme.

18. The apparatus of claim 17, wherein, The first obtaining unit includes: The generating sub-unit is configured to generate a preset number of wind power generation output scenarios and photovoltaic power generation output scenarios by using a scenario generation method based on the new energy day-ahead short-term power prediction data. The first obtaining sub-unit is configured to select typical scenarios from the wind power generation output scenarios and the photovoltaic power generation output scenarios respectively by using a scenario reduction method, and obtain upper or lower limits of wind power generation output at each time period and upper or lower limits of photovoltaic power generation output at each time period. The second obtaining sub-unit is configured to obtain the limit constraint conditions of the wind power generation output and the limit constraint conditions of the photovoltaic power generation output by using the upper or lower limits of the wind power generation output at each time period and the upper or lower limits of the photovoltaic power generation output at each time period respectively.

19. The apparatus of claim 18, wherein, The second obtaining sub-unit is specifically configured to: determine the limit constraint conditions of the wind power generation output according to the following formula: determine the limit constraint conditions of the photovoltaic power generation output according to the following formula: In the above formulae, and are the upper or lower limits of the output of wind power generation in the tth period, respectively, and are the upper or lower limits of the output of photovoltaic power generation in the tth period, respectively, is the maximum available power of wind power generation in the tth period, is the maximum available power of photovoltaic power generation in the tth period.

20. The apparatus of claim 12, wherein, The day-ahead optimization operation model further includes: determine the constraint conditions of the day-ahead optimization operation model according to the following formula: in the above formula, is the ultra-short-term predicted power value of the wind power generation of the gth period of the ith day for the optimization operation, is the ultra-short-term predicted power value of the photovoltaic power generation of the gth period of the ith day for the optimization operation, is the state of charge of the energy storage system of the i+g-1th period of the ith day for the optimization operation, is the state of charge of the energy storage system of the t+g-2th period of the ith day for the optimization operation, is the change amount of the energy storage capacity of the energy storage system relative to the day-ahead energy storage system at the gth period of the ith day for the optimization operation, is the rated capacity of the energy storage system, is the maximum charge and discharge power of the energy storage system.

21. The apparatus of claim 20, wherein, The change amount of the power storage amount of the day-ahead energy storage system at the gth time phase of the ith day-ahead optimization operation is determined according to the following formula : In the above formula, is the energy storage amount of the energy storage system in the gth time period, and ES is the self-dissipation rate of the energy storage system, and is the charging efficiency of the energy storage system, is the change amount of the charging or discharging power of the energy storage system in the gth time period in the ith day-ahead optimization operation relative to the day-ahead optimization result, is the charging or discharging power of the energy storage system in the gth time period in the ith day-ahead optimization operation.

22. The apparatus of claim 21, wherein, The third obtaining module includes: The fourth obtaining unit is configured to take the day-ahead optimization operation scheme as input, and obtain a variation amount of charging or discharging power of the energy storage system at a next dispatching time period relative to a day-ahead optimization result by solving a pre-constructed day-ahead optimization operation model based on the constraint conditions of the day-ahead optimization operation model by using a mixed integer linear programming method. The fifth obtaining unit is configured to add the charging or discharging power of the energy storage system at the next dispatching time period corresponding to the day-ahead optimization operation scheme and the variation amount of the charging or discharging power of the energy storage system at the next dispatching time period relative to the day-ahead optimization result, to obtain final charging or discharging power of the energy storage system at the next dispatching time period. The sixth obtaining unit is configured to take the final charging or discharging power of the energy storage system at the next dispatching time period as an optimization operation result of the next dispatching time period.

23. A computer device, comprising: One or more processors; The processor is configured to store one or more programs; When the one or more programs are executed by the one or more processors, the steps in the collaborative operation optimization method for energy storage and new energy field stations are implemented. ​ 24. A computer-readable storage medium, characterized in that, A computer program product, comprising a computer program, which, when executed, implements the steps of the method for coordinated operation optimization of energy storage and new energy power stations according to any one of claims 1 to 11.

Images