Hybrid optimization method for new energy and energy storage dispatching operation mode

Abstract

The present invention discloses a hybrid optimization method for new energy and energy storage scheduling operation modes, which includes: S1, regarding the target time period as a long time period, and dividing the long time period into multiple continuous sub-time periods; S2, calculating the equivalent load demand, equivalent maximum power of wind turbines, equivalent maximum power of photovoltaic arrays, and equivalent time-of-use electricity price of each sub-time period; S3, reorganizing into corresponding sequences of long time periods in time sequence; S4, obtaining a comprehensive wind power, photovoltaic, and energy storage operation sequence and a comprehensive load power sequence based on a coordinated operation strategy; S5, using all comprehensive sequences as boundary conditions of all sub-time periods, setting optimization functions and constraints for each sub-time period, and obtaining actual wind power, photovoltaic, energy storage, and load demand sequences for different sub-time periods through optimization calculation; S6, adopting a continuous transmission mechanism to form a long-term scheduling operation mode, thereby realizing efficient scheduling optimization of new energy and energy storage.

Description

Technical Field

[0001] The present invention relates to the field of new energy scheduling technology, and in particular to a hybrid optimization method for new energy and energy storage scheduling operation modes. Background Art

[0002] The proportion of wind power, photovoltaic power, and other sources of power generation in new power systems continues to increase. The introduction of electric vehicles and virtual power plants on the load side has further increased the randomness and complexity of the power system's source-load balance, accelerating the participation of green energy sources such as energy storage in the power market and power operation regulation. At the same time, the diverse regulatory resources integrated into the power system exhibit multi-timescale coupling characteristics, increasing the computational complexity of power dispatch and operation, which directly affects the economic efficiency, low carbon nature, and reliability of energy. Balancing long-term coordinated operation economics with short-term power instantaneous balance reliability has become a key issue.

[0003] Existing new energy storage scheduling and operation modes mostly adopt single time scale optimization, lacking consideration of short-term power balancing capabilities. Due to the limitations of computing power and computing scale, they have the limitations of dimensionality disaster, nonlinearity, and multi-constraint problems, and mainly provide specific methods for short time ranges, or provide relatively rough operating benchmarks. When calculating long time ranges, huge computing resources are required. At the same time, they face situations such as long calculation time, difficulty in convergence, or low solution quality, and cannot be solved. Especially in the operation calculation of long time scales, it is difficult to effectively solve. Moreover, these conditions lead to only static simplification of factors such as energy storage attenuation, and cannot consider the dynamic relationship between attenuation cost and charging and discharging strategy, which greatly reduces the calculation accuracy of comprehensive benefits. Summary of the Invention

[0004] In response to the above-mentioned prior art, the present invention provides a hybrid optimization method for new energy and energy storage scheduling operation modes, mainly to solve the technical problems existing in the above-mentioned background technology.

[0005] To achieve the above objectives, the technical solution of the embodiment of the present invention is implemented as follows: a hybrid optimization method for new energy and energy storage scheduling operation mode, the hybrid optimization method is applied to a power system including new energy and energy storage, the hybrid optimization method includes:

[0006] S1. Consider the target time period as a long period and divide the long period into multiple consecutive sub-periods;

[0007] S2. Calculate the equivalent load demand, the equivalent maximum power generated by the wind turbine, the equivalent maximum power generated by the photovoltaic array, and the equivalent time-of-use electricity price for each sub-time period;

[0008] S3. Reorganize the equivalent load demand, equivalent maximum power generated by wind turbines, equivalent maximum power generated by photovoltaic arrays, and equivalent time-of-use electricity prices of multiple sub-time periods into a corresponding sequence of long-term periods according to time sequence;

[0009] S4. Based on the coordinated operation strategy and the equivalent load demand sequence over a long period of time, the equivalent wind turbine maximum power sequence, the equivalent photovoltaic array maximum power sequence, and the equivalent time-of-use electricity price sequence, calculate and obtain the comprehensive wind power operation sequence, the comprehensive photovoltaic operation sequence, the comprehensive energy storage operation sequence, and the comprehensive load power sequence;

[0010] S5. Using the comprehensive wind power operation sequence, comprehensive photovoltaic operation sequence, comprehensive energy storage system operation sequence, and comprehensive load power sequence as boundary conditions for all sub-time periods, setting optimization functions and constraints for different sub-time periods, optimizing each sub-time period individually, and optimizing and calculating the actual wind power operation sequence, actual photovoltaic operation sequence, actual energy storage system operation sequence, and actual load power sequence for each sub-time period;

[0011] S6. Use a continuous transmission mechanism to process the actual wind power operation sequence, actual photovoltaic operation sequence, actual energy storage system operation sequence, and actual load power sequence in different sub-time periods, and ultimately form a complete long-term period scheduling operation mode based on the processing results.

[0012] Optionally, step S2 specifically includes:

[0013] The target data is regarded as a long-term period data set, and the long-term period data set is divided into multiple one-to-one corresponding sub-time period data sets according to the sub-time periods;

[0014] For the sub-time period data set, the average values ​​of the load demand, the maximum power that the wind turbine can generate, and the maximum power that the photovoltaic array can generate for each sub-time period are calculated. The electricity price for each period is then weighted according to the corresponding time to obtain the equivalent time-of-use electricity price.

[0015] The average value of the load demand in each sub-time period, the average value of the maximum power that can be generated by the wind turbine, the average value of the maximum power that can be generated by the photovoltaic array, and the equivalent time-of-use electricity price are used as the equivalent load demand, equivalent maximum power that can be generated by the wind turbine, equivalent maximum power that can be generated by the photovoltaic array, and equivalent time-of-use electricity price in the sub-time period.

[0016] Optionally, the step S4 specifically includes: combining the equivalent load demands in all sub-time periods in chronological order to form an equivalent load demand sequence for a long time period;

[0017] The maximum power generation capacity of the equivalent wind turbines in all sub-time periods is combined in chronological order to form a maximum power generation capacity sequence of the equivalent wind turbines in a long time period;

[0018] The maximum power that can be generated by the equivalent photovoltaic array in all sub-time periods is combined in chronological order to form a maximum power sequence that can be generated by the equivalent photovoltaic array in the long time period;

[0019] The equivalent time-of-use electricity prices in all sub-time periods are combined in chronological order to form an equivalent time-of-use electricity price sequence for a long time period.

[0020] Optionally, the coordinated operation strategy includes a first coordination rule and a second coordinated operation rule. The first coordination rule is first used to calculate the comprehensive wind power operation sequence, comprehensive photovoltaic operation sequence, comprehensive energy storage operation sequence, and comprehensive load power sequence for a long period of time, and calculate the total load abandonment rate. If the total load abandonment rate exceeds a preset load abandonment rate threshold, the second coordination rule is used to recalculate the above four comprehensive sequences.

[0021] Optionally, the first coordination rule specifically includes:

[0022] For each sub-period within a long period, if the sum of the equivalent maximum wind power generation and the equivalent maximum photovoltaic power generation in the sub-period is greater than the equivalent load demand, the energy storage system absorbs the excess power consumed by the load. If the energy storage system cannot absorb the excess power, it will be fed back to the external power grid. If the external power grid cannot absorb the excess power, the excess renewable energy will be discarded.

[0023] If the sum of the equivalent maximum wind power generation and the equivalent maximum photovoltaic power generation in the sub-time period is less than the equivalent load demand, the equivalent time-of-use electricity price period is further determined. If it is during the peak / peak electricity price period, the energy storage system will supplement the insufficient power, and the external grid will not provide power. If the energy storage is insufficient, the load will be abandoned;

[0024] During the flat electricity price period, the external grid will first supplement the insufficient power. If the power is insufficient, the energy storage system will provide power. If neither the energy storage system nor the external grid can meet the load, the load will be shelved.

[0025] During off-peak electricity price periods, the external power grid will supplement the insufficient electricity; otherwise, the load will be abandoned and the energy storage system will not provide power.

[0026] Optionally, when the sum of the equivalent maximum wind power generation and the equivalent maximum photovoltaic power generation in the sub-time period is greater than the equivalent load demand, calculate the difference between the renewable energy generation and the equivalent load demand :

[0027]

[0028] According to the difference , calculate the comprehensive energy storage output of the current sub-time period :

[0029]

[0030] The comprehensive energy storage output of multiple sub-time periods constitutes a long-term comprehensive energy storage operation sequence :

[0031] Calculate the load demand for the current sub-time period:

[0032]

[0033] The load demands of multiple sub-time periods constitute the comprehensive load power sequence of the long-term period ;

[0034] Calculate the comprehensive wind power output of the current sub-time period:

[0035]

[0036] The comprehensive wind power output of multiple sub-time periods constitutes a comprehensive wind power operation sequence for a long period of time ;

[0037] Calculate the comprehensive photovoltaic output of the current sub-time period:

[0038]

[0039] The comprehensive photovoltaic output of multiple sub-time periods constitutes a comprehensive photovoltaic operation sequence for a long period of time ;

[0040] When the sum of the equivalent maximum wind power generation power and the equivalent maximum photovoltaic power generation power in a sub-time period is less than the equivalent load demand, calculate the comprehensive wind power output of the current sub-time period:

[0041]

[0042] The comprehensive wind power output of multiple sub-time periods constitutes a comprehensive wind power operation sequence for a long period of time ;

[0043] Calculate the comprehensive photovoltaic output of the current sub-time period:

[0044]

[0045] The comprehensive photovoltaic output of multiple sub-time periods constitutes a comprehensive photovoltaic operation sequence for a long period of time ;

[0046] If the current sub-time period is during the peak / peak electricity price period, the following formula is used to calculate the comprehensive energy storage output: :

[0047]

[0048]

[0049] If the current sub-time period is in the flat electricity price period, the following formula is used to calculate the comprehensive energy storage output: :

[0050]

[0051] If the previous sub-period is during the valley electricity price period, the following formula is used to calculate the comprehensive energy storage output: :

[0052]

[0053] The comprehensive energy storage output of multiple sub-time periods constitutes a long-term comprehensive energy storage operation sequence ;

[0054] Calculate the load demand for the current sub-time period:

[0055]

[0056] The load demands of multiple sub-time periods constitute the comprehensive load power sequence of the long-term period ;

[0057] in, is the jth element of the maximum power generation sequence of the equivalent wind turbine, is the jth element of the maximum power sequence of the equivalent photovoltaic array, Represents the difference between renewable energy generation and equivalent load demand, is the maximum charging power of the energy storage system, is the maximum discharge power of the energy storage system, is the jth element of the equivalent load demand sequence, Indicates the maximum value of the external grid interaction power, represents the external grid interaction power, n is a constant, representing the nth element in the above four comprehensive sequences, represents the amount of wind power curtailed, Indicates the amount of photovoltaic power wasted.

[0058] Optionally, the second coordination rule differs from the first coordination rule only in that, during peak / peak electricity price periods, the energy storage system first supplements the insufficient electricity, and when there is a shortage, the external power grid provides power, and the energy storage system and the external power grid are unable to meet the load and abandon the load.

[0059] Optionally, the optimization function of the sub-time period includes a new energy power generation income item, a system configuration cost item, a system operation cost item, an energy storage loss item, a green power curtailment loss item, and a load loss item;

[0060] The constraints of the sub-time period include: power constraints of new energy power generation units, SOC constraints of electrochemical energy storage, maximum charge and discharge power constraints of energy storage, load power constraints, grid power constraints, green power curtailment rate constraints, and load supply rate constraints.

[0061] Optionally, the constraint condition further includes a coupling constraint condition, and the coupling constraint condition includes:

[0062] The average value of the actual wind power operation sequence in a sub-time period is equal to the corresponding value of the comprehensive wind power operation sequence in the sub-time period;

[0063] The average value of the actual PV operation sequence of a sub-time period is equal to the corresponding value of the comprehensive PV operation sequence in the sub-time period;

[0064] The algebraic sum of the charge and discharge powers of the actual energy storage operation sequence in a sub-time period is equal to the corresponding value of the comprehensive energy storage operation sequence in that sub-time period;

[0065] The average value of the actual load power sequence of a sub-time period is equal to the corresponding value of the comprehensive load power sequence in the sub-time period.

[0066] Optionally, the continuous transmission mechanism includes: calculating the final state of the wind power array, photovoltaic array, energy storage system, load power and the final SOC of the energy storage system in the sub-time period based on the actual wind power operation sequence, actual photovoltaic operation sequence, actual energy storage system operation sequence and actual load power sequence of the sub-time period, and using the calculation results as the initial power of the wind power array, photovoltaic array, energy storage system, load power and the initial SOC of the energy storage system in the next sub-time period.

[0067] The beneficial effects of the present invention are: by decomposing a long period into sub-periods and generating equivalent load demand, equivalent maximum power that can be generated by wind turbines, equivalent maximum power that can be generated by photovoltaic arrays, and equivalent time-of-use electricity prices, the dimension of the optimization problem is significantly reduced, and the dimensionality disaster problem caused by the computational complexity of the traditional method is solved. The equivalent load demand, equivalent maximum power that can be generated by wind turbines, equivalent maximum power that can be generated by photovoltaic arrays, and equivalent time-of-use electricity prices are calculated through a coordinated operation strategy to calculate the comprehensive wind power operation sequence, comprehensive photovoltaic operation sequence, comprehensive energy storage operation sequence, and comprehensive load power sequence of the long period, and the four The comprehensive sequence is set as the common boundary of different sub-time periods to ensure that the statistical characteristics of the actual operating sequence of each sub-period are consistent with the characteristics of the long-term period, avoid information loss or deviation caused by data dimensionality reduction, and ensure that key parameters such as the mean value of wind power and photovoltaic output and the algebraic sum of energy storage charge and discharge remain consistent at different time scales. Compared with traditional solutions, the method protected by this application effectively eliminates the isolation in the sub-time period optimization calculation, so that the optimization results of each sub-time period can not only meet its own constraints but also conform to the overall operating laws of the long-term period, thereby improving the consistency and accuracy of the entire system scheduling strategy;

[0068] At the same time, based on the setting of boundary conditions and combined with the optimization algorithm, the global optimal solution can be quickly searched under the premise of meeting complex constraints such as energy storage SOC recursion and power balance, ensuring that the energy flow and equipment status changes in each sub-cycle of the actual operation sequence are consistent with the long-term cycle planning goals, significantly enhancing the refinement of energy system scheduling and operational stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 Schematic diagram of the steps of the hybrid optimization method for new energy and energy storage scheduling operation mode in the embodiment of the present application. DETAILED DESCRIPTION

[0070] The technical solution of the present invention is further elaborated in detail below in conjunction with the drawings and specific embodiments of the specification. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present invention belongs. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention. In the following description, reference is made to "some embodiments", which describes a subset of all possible embodiments, but it should be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments, and can be combined with each other without conflict.

[0071] In the following description, numerous specific details are provided to provide a more thorough understanding of the present invention. However, it will be apparent to those skilled in the art that the present invention may be practiced without one or more of these details. In other instances, certain technical features well known in the art are not described to avoid confusion with the present invention.

[0072] It should be understood that the present invention can be implemented in different forms and should not be interpreted as being limited to the embodiments proposed herein. On the contrary, providing these embodiments will make the disclosure thorough and complete, and will fully convey the scope of the present invention to those skilled in the art. And the purpose of the terms used herein is only to describe specific embodiments and is not intended to limit the present invention. When used herein, the singular forms "one", "an" and "said / the" are also intended to include plural forms, unless the context clearly indicates another way. It should also be understood that the terms "comprising" and / or "comprising" when used in this specification determine the presence of the features, integers, steps, operations, elements and / or parts, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, parts and / or groups. When used herein, the term "and / or" includes any and all combinations of the relevant listed items.

[0073] It should also be noted that when an element is referred to as being "fixed to" another element, it may be directly on the other element or there may be an intermediate element. When an element is referred to as being "connected to" another element, it may be directly connected to the other element or there may be an intermediate element. The terms "vertical," "horizontal," "inner," "outer," "left," "right," and similar expressions used herein are for illustrative purposes only and do not represent the only implementation methods.

[0074] In order to fully understand the present invention, a detailed structure will be provided in the following description to illustrate the technical solution proposed by the present invention. Optional embodiments of the present invention are described in detail below. However, in addition to these detailed descriptions, the present invention may also have other implementations.

[0075] Please refer to the attached Figure 1 The present application provides a hybrid optimization method for new energy and energy storage scheduling operation mode, which is applied to a power system including new energy and energy storage, and the hybrid optimization method includes:

[0076] S1. Consider the target time period as a long period and divide the long period into multiple consecutive sub-periods;

[0077] S2. Calculate the equivalent load demand, the equivalent maximum power generated by the wind turbine, the equivalent maximum power generated by the photovoltaic array, and the equivalent time-of-use electricity price for each sub-time period;

[0078] S3. Reorganize the equivalent load demand, equivalent maximum power generated by wind turbines, equivalent maximum power generated by photovoltaic arrays, and equivalent time-of-use electricity prices of multiple sub-time periods into a corresponding sequence of long-term periods according to time sequence;

[0079] S4. Based on the coordinated operation strategy and the corresponding sequence of the long period, calculate and obtain the comprehensive wind power operation sequence, the comprehensive photovoltaic operation sequence, the comprehensive energy storage operation sequence, and the comprehensive load power sequence;

[0080] S5. Using the comprehensive wind power operation sequence, comprehensive photovoltaic operation sequence, comprehensive energy storage system operation sequence, and comprehensive load power sequence as boundary conditions for all sub-time periods, while setting optimization functions and constraints for different sub-time periods, and optimizing and calculating the actual wind power operation sequence, actual photovoltaic operation sequence, actual energy storage system operation sequence, and actual load power sequence for different sub-time periods;

[0081] S6. Use a continuous transmission mechanism to process the actual wind power operation sequence, actual photovoltaic operation sequence, actual energy storage system operation sequence, and actual load power sequence in different sub-time periods, and ultimately form a complete long-term period scheduling operation mode based on the processing results.

[0082] Specifically, the present application regards the target time period as a long time period and divides it into multiple continuous sub-time periods. The long time period can be year / month / day, and the level of the sub-time period is lower than the long time period. For example, when the long time period is a year, the sub-time period is a week. By calculating the equivalent load demand, the equivalent maximum power that can be generated by the wind turbine, the equivalent maximum power that can be generated by the photovoltaic array and the equivalent time-of-use electricity price of each sub-time period, these equivalent data are reorganized into the corresponding sequence of the long time period in time sequence. Based on the coordinated operation strategy and the long time period equivalent sequence, the comprehensive wind power operation sequence, the comprehensive photovoltaic operation sequence, the comprehensive energy storage operation sequence, and the comprehensive load power sequence are calculated;

[0083] The calculated comprehensive wind power operation sequence, comprehensive photovoltaic operation sequence, comprehensive energy storage operation sequence, and comprehensive load power sequence are used as the boundary conditions of all sub-time periods. At the same time, optimization functions and constraints are set for different sub-time periods. The actual wind power operation sequence, actual photovoltaic operation sequence, actual energy storage system operation sequence, and actual load power sequence of each sub-time period are optimized and calculated. A continuous transfer mechanism is used to process the actual wind power operation sequence, actual photovoltaic operation sequence, actual energy storage system operation sequence, and actual load power sequence of different sub-time periods. Based on the processing results, a complete long-term period scheduling operation mode is finally formed.

[0084] This method decomposes long time periods into sub-time periods, realizes phased processing of complex scheduling problems and reduces computational complexity; by calculating equivalent parameters and reorganizing sequences, data dimensionality reduction and feature extraction are achieved, providing a basis for the overall optimization of long time periods; based on the coordination strategy to calculate the comprehensive sequence, the operation of new energy and energy storage can be coordinated from a global level, improving the overall scheduling efficiency of the system; using the comprehensive sequence as the boundary condition and combining the sub-period optimization function and constraint conditions, it not only ensures the integrity and continuity of the long-term cycle operation, but also can carry out fine-grained regulation according to the specific conditions of each sub-period, realizing the organic combination of long-term cycle optimization and sub-time period dynamic adjustment, which can effectively deal with the randomness and volatility of new energy and loads in the power system and improve the economy, reliability and flexibility of system operation.

[0085] As a possible implementation, step S2 specifically includes:

[0086] The target data is regarded as a long-term period data set, which is divided into multiple one-to-one corresponding sub-time period data sets according to the sub-time period. The target data refers to the load demand data, the maximum power data of the wind turbine, the maximum power data of the photovoltaic array, and the time-of-use electricity price data of the power grid within all time ranges. Before obtaining the target data, it is necessary to further obtain the capacity configuration of the system's wind turbines, photovoltaic arrays, and energy storage systems.

[0087] For the sub-time period data set, the average values ​​of the load demand, the maximum power that the wind turbine can generate, and the maximum power that the photovoltaic array can generate for each sub-time period are calculated. The electricity price for each period is then weighted according to the corresponding time to obtain the equivalent time-of-use electricity price.

[0088] The average value of the load demand in each sub-time period, the average value of the maximum power that can be generated by the wind turbine, the average value of the maximum power that can be generated by the photovoltaic array, and the equivalent time-of-use electricity price are used as the equivalent load demand, equivalent maximum power that can be generated by the wind turbine, equivalent maximum power that can be generated by the photovoltaic array, and equivalent time-of-use electricity price in the sub-time period.

[0089] For example, assuming that the target data set considered as a long-term period data set contains load demand, maximum power that can be generated by wind turbines, maximum power that can be generated by photovoltaic arrays, and time-of-use electricity price data sampled at the minute level within a year, the long-term period data set is divided into 365 consecutive sub-time period data sets, each of which contains load demand, maximum power that can be generated by wind turbines, maximum power that can be generated by photovoltaic arrays, and time-of-use electricity price data at the minute level within a day.

[0090] Taking the calculation of the maximum power that can be generated by an equivalent wind turbine as an example, each sub-time period data set includes the maximum power that can be generated by the wind turbine {}, then the calculated value of the maximum power that can be generated by the equivalent wind turbine in this sub-time period is:

[0091]

[0092] For example, there are 96 sampling points in the sub-time period data set. The maximum power that the wind turbine can generate at each data sampling point is recorded. The values ​​of each point are summed up and the average value is taken. Assuming the total is: 182400kW, the average value is 182400 ÷ 96 = 1900kW.

[0093] Similarly, the average value of the load demand and the average value of the maximum power that can be generated by the photovoltaic array are calculated in sequence.

[0094] As for the calculation of equivalent time-of-use electricity price, the electricity price is calculated according to the time weighting to obtain the equivalent time-of-use electricity price. The calculation formula is:

[0095]

[0096] in, represents the equivalent time-of-use electricity price of the t-th sub-time period, Indicates the time-of-use electricity price for each period within the time period. represents the time period of the electricity price, and N represents all time-of-use electricity price periods.

[0097] The above method ultimately calculates the average load demand, the average maximum power output of wind turbines, the average maximum power output of photovoltaic arrays, and the equivalent time-of-use electricity price. These values ​​are then considered the equivalent load demand, equivalent maximum power output of wind turbines, equivalent maximum power output of photovoltaic arrays, and equivalent time-of-use electricity price for the sub-time period. It is understandable that if each sub-time period contains 96 sampling points, the original data dimension is 96. After dimensionality reduction, each sub-time range is aggregated into a single-dimensional average value, reducing the data volume to 1.04% of the original data. This dimensionality reduction effectively eliminates redundant information caused by short-term fluctuations and highlights the typical operating characteristics within the sub-time period. Furthermore, dimensionality reduction significantly reduces the complexity of subsequent optimization calculations. Instead of processing N × 96 dimensions of data for long-term ranges, only N dimensions are required. The computational complexity decreases exponentially with the reduction in dimensionality, while preserving the statistical characteristics of the data, ensuring that the equivalent parameters after dimensionality reduction accurately reflect the overall trend of the original data.

[0098] As a possible implementation, step S4 specifically includes: combining the equivalent load demands in all sub-time periods in chronological order to form an equivalent load demand sequence for a long time period;

[0099] The maximum power generation capacity of the equivalent wind turbines in all sub-time periods is combined in chronological order to form a maximum power generation capacity sequence of the equivalent wind turbines in a long time period;

[0100] The maximum power that can be generated by the equivalent photovoltaic array in all sub-time periods is combined in chronological order to form a maximum power sequence that can be generated by the equivalent photovoltaic array in the long time period;

[0101] The equivalent time-of-use electricity prices in all sub-time periods are combined in chronological order to form an equivalent time-of-use electricity price sequence for a long time period.

[0102] For example, for the sub-time period A1, the equivalent sequence is , for the sub-time period A2, the equivalent sequence is Similarly, we obtain the equivalent sequence of sub-time periods A2-A50, and reorganize the equivalent sequence of sub-time periods A1-A50 in chronological order to obtain the equivalent sequence of the following long-term period:

[0103] Long-term equivalent load demand series, , ,... ;

[0104] The maximum power sequence of equivalent wind turbines over a long period of time, , ,... ;

[0105] The maximum power sequence of the equivalent photovoltaic array over a long period of time, , ,... ;

[0106] The equivalent time-of-use electricity price series over a long period of time, , ,... .

[0107] Through the orderly arrangement of the time dimension, the equivalent parameters of discrete sub-periods are integrated into a continuous feature sequence, so that the load fluctuation law, new energy output trend and electricity price change pattern within a long period can be systematically presented. In addition, the reorganized sequence further compresses the information dimension on the basis of data dimensionality reduction, so that the original multi-dimensional data of each sub-time is directly reduced to 1 dimension. The current layer of calculation only performs calculations on the data after dimensionality reduction, which greatly reduces the complexity of the optimization calculation. At the same time, the statistical characteristics of the original data are retained, ensuring that the optimization calculation of a long period can not only cover the global operating characteristics, but also capture the correlation between different sub-periods through the sequence time relationship. The subsequent optimization algorithm uses the comprehensive sequence calculated by this layer as the boundary condition, and then performs a detailed solution for each sub-time period.

[0108] As a possible implementation method, the coordinated operation strategy includes a first coordination rule and a second coordinated operation rule. The first coordination rule is first used to calculate the comprehensive wind power operation sequence, comprehensive photovoltaic operation sequence, comprehensive energy storage operation sequence, and comprehensive load power sequence for a long period of time, and calculate the total load abandonment rate. If the total load abandonment rate exceeds the preset load abandonment rate threshold, the second coordination rule is used to recalculate the above four comprehensive sequences.

[0109] It should be noted that the specific calculation process of the total load abandonment rate is common knowledge to those skilled in the art and will not be described in detail in this embodiment.

[0110] As a further implementation manner, the first coordination rule specifically includes:

[0111] For each sub-period within a long period, if the sum of the equivalent maximum wind power generation and the equivalent maximum photovoltaic power generation in the sub-period is greater than the equivalent load demand, the energy storage system absorbs the excess power consumed by the load. If the energy storage system cannot absorb the excess power, it will be fed back to the external power grid. If the external power grid cannot absorb the excess power, the excess renewable energy will be discarded.

[0112] In the specific calculation, when the sum of the equivalent maximum wind power generation power and the equivalent maximum photovoltaic power generation power in the sub-time period is greater than the equivalent load demand, the difference between the sum of the equivalent maximum wind power generation power and the equivalent maximum photovoltaic power generation power in the sub-time period and the equivalent load demand value is calculated. :

[0113]

[0114] When the load is greater than the equivalent load demand, the excess load is charged into the energy storage system, and the energy storage system absorbs the excess electricity consumed by the load. If the energy storage system cannot absorb it, it will be fed back to the external power grid. When the external power grid cannot absorb it, the excess new energy will be discarded.

[0115] Therefore, when calculating, the comprehensive energy storage output of the current sub-time period is calculated by the following formula: :

[0116]

[0117] The comprehensive energy storage output of multiple sub-time periods constitutes a long-term comprehensive energy storage operation sequence :

[0118] It should be noted that the energy storage output It includes both the energy storage discharge and energy storage charging conditions. When the power charged into the energy storage system does not reach the maximum charging power of the energy storage system, the energy storage output = When the power charged into the energy storage system reaches the maximum charging power of the energy storage system, the energy storage output = , the remaining power is fed into the grid. When the value fed into the grid reaches the maximum value of the grid interaction power When the power is fully utilized, the remaining new energy power is discarded.

[0119] Furthermore, the load demand of the current sub-time period is:

[0120]

[0121] The load demands of multiple sub-time periods constitute the comprehensive load power sequence of the long-term period .

[0122] Furthermore, the calculated wind power output of the current sub-time period is:

[0123]

[0124] The comprehensive wind power output of multiple sub-time periods constitutes a comprehensive wind power operation sequence for a long period of time ;

[0125] Furthermore, the comprehensive photovoltaic output of the current sub-time period is calculated as:

[0126]

[0127] The comprehensive photovoltaic output of multiple sub-time periods constitutes a comprehensive photovoltaic operation sequence for a long period of time ;

[0128] For example, when the sum of the equivalent maximum wind power generation power and the equivalent maximum photovoltaic power generation power in sub-period A1 is greater than the equivalent load demand, and the energy storage system can absorb the excess power, the energy storage output in sub-period A1 is , its load demand If there is excess renewable energy output and wind power output is abandoned, the wind power output , photovoltaic output If you choose to abandon photovoltaic power output, wind power output , photovoltaic output .

[0129] For example, assuming that the equivalent maximum wind power generation power of the sub-time period A1 is 100MW, equivalent maximum photovoltaic power generation is 80MW, its equivalent load demand is 150MW, the difference =100MW+80MW-150MW=30MW, if the maximum charge of energy storage It is 20MW, the maximum grid interaction power 5MW, there is still 5MW of excess renewable energy output. At this time, according to the preset rules, choose to abandon wind power output or photovoltaic output. For example, when choosing to abandon wind power output, the wind power output of the current sub-time period 100MW-5MW=95MW, the photovoltaic output of the current sub-time period The wind power output in the current sub-period is 80MW. When the photovoltaic output is abandoned, the wind power output in the current sub-period is The photovoltaic output of the current sub-period is 100MW. It is 80MW-5MW=75MW.

[0130] As another optional implementation, if the sum of the equivalent maximum wind power generation power and the equivalent maximum photovoltaic power generation power in the sub-time period is less than the equivalent load demand, the time period of the equivalent time-of-use electricity price is further determined. If it is during the peak / peak electricity price period, the energy storage system will supplement the insufficient power, and the external grid will not provide power. If the energy storage is insufficient, the load will be abandoned;

[0131] During the flat electricity price period, the external grid will first supplement the insufficient power. If the power is insufficient, the energy storage system will provide power. If neither the energy storage system nor the external grid can meet the load, the load will be shelved.

[0132] During off-peak electricity price periods, the external power grid will supplement the insufficient electricity; otherwise, the load will be abandoned and the energy storage system will not provide power.

[0133] In the specific calculation, when the sum of the equivalent maximum wind power generation power and the equivalent maximum photovoltaic power generation power in the sub-time period is less than the equivalent load demand, the difference between the sum of the equivalent maximum wind power generation power and the equivalent maximum photovoltaic power generation power in the sub-time period and the equivalent load demand value is calculated. :

[0134]

[0135] Since the sum of the equivalent maximum wind power generation power and the equivalent maximum photovoltaic power generation power still does not meet the equivalent load demand, the wind power output is fully generated. Therefore, the calculated wind power output of the current sub-time period is:

[0136]

[0137] The comprehensive wind power output of multiple sub-time periods constitutes a comprehensive wind power operation sequence for a long period of time ;

[0138] Similarly, when the photovoltaic power output is fully generated, the comprehensive photovoltaic power output of the current sub-time period is calculated as:

[0139]

[0140] The comprehensive photovoltaic output of multiple sub-time periods constitutes a comprehensive photovoltaic operation sequence for a long period of time ;

[0141] Determine the current time-of-use electricity price. If the current sub-time period is in the peak / peak electricity price period, use the following formula to calculate the comprehensive energy storage output: :

[0142]

[0143] It should be noted that when the output power of the energy storage system does not reach the maximum discharge power of the energy storage system, the energy storage output = When the output power of the energy storage system reaches the maximum discharge power of the energy storage system, the energy storage output = , the external grid does not provide power and the load is abandoned when the energy storage is insufficient;

[0144] If the current sub-time period is in the flat electricity price period, the following formula is used to calculate the comprehensive energy storage output: :

[0145]

[0146] It should be noted that when the external power grid can meet the difference When the energy storage output , when the external grid cannot meet the difference , when the power supplied by the energy storage system does not reach the maximum discharge power of the energy storage system, the energy storage output If the power supplied by the energy storage system reaches the maximum discharge power of the energy storage system, the energy storage output = ;

[0147] If the previous sub-period is during the valley electricity price period, the following formula is used to calculate the comprehensive energy storage output: :

[0148]

[0149] The comprehensive energy storage output of multiple sub-time periods constitutes a long-term comprehensive energy storage operation sequence ;

[0150] If the energy storage output can meet the load requirements, the load demand for the current sub-time period is the sum of wind power output, photovoltaic output, and energy storage output. If the energy storage output cannot meet the load requirements and external grid supplementary power is required, the load demand for the current sub-time period is the sum of wind power output, photovoltaic output, energy storage output, and grid supplementary power. In summary, the load demand for the current sub-time period is calculated using the following formula:

[0151]

[0152] The load demands of multiple sub-time periods constitute the comprehensive load power sequence of the long-term period ;

[0153] For example, assuming that the equivalent maximum wind power generation power of the sub-time period A1 is 100MW, equivalent maximum photovoltaic power generation is 20MW, its equivalent load demand is 150MW, the difference =100MW+20MW-150MW=-30MW, if the energy storage output A maximum of 30MW of input can be provided to meet the load requirements, and no grid interaction power input is required. At this time, the grid interaction power is 0. If the energy storage output Can provide up to 10MW input, requiring grid interaction power In addition, if the grid interaction power , then the load demand of the sub-time period at this time is the sum of wind power output, photovoltaic output, energy storage discharge power, and grid interaction power, that is, .

[0154] Among them, among them, is the jth element of the maximum power generation sequence of the equivalent wind turbine, is the jth element of the maximum power sequence of the equivalent photovoltaic array, Represents the difference between renewable energy generation and equivalent load demand, is the maximum charging power of the energy storage system, is the maximum discharge power of the energy storage system, is the jth element of the equivalent load demand sequence, Indicates the maximum value of the external grid interaction power, represents the external grid interaction power, n is a constant, representing the nth element in the above four comprehensive sequences, represents the amount of wind power curtailed, Indicates the amount of photovoltaic power wasted.

[0155] As a possible implementation method, the only difference between the second coordination rule and the first coordination rule is that during the peak / peak electricity price period, the energy storage system will first supplement the insufficient electricity, and when there is a shortage, the external power grid will provide power. When the energy storage system and the external power grid cannot meet the load, the load will be shed.

[0156] Specifically, after calculating the comprehensive wind power operation sequence, comprehensive photovoltaic operation sequence, comprehensive energy storage operation sequence, and comprehensive load power sequence for a long period of time through the first coordination rule, the total load abandonment rate under the long period of time is calculated. If the total load abandonment rate exceeds the preset load abandonment rate threshold, the second coordination rule is used to recalculate the above four comprehensive sequences. The calculation process is the same as that of the first coordination rule. Only during the peak / peak electricity price period, the energy storage system is used to supplement the insufficient electricity first, and the external power grid provides power when it is insufficient. The energy storage system and the external power grid cannot meet the load and abandon the load. The comprehensive wind power operation sequence, comprehensive photovoltaic operation sequence, comprehensive energy storage operation sequence, and comprehensive load power sequence output by the second coordination rule are the final comprehensive wind power operation sequence, comprehensive photovoltaic operation sequence, comprehensive energy storage operation sequence, and comprehensive load power sequence.

[0157] As a possible implementation method, the optimization function of the sub-time period includes a new energy power generation income item, a system configuration cost item, a system operation cost item, an energy storage loss item, a green power abandonment loss item, and a load loss item.

[0158] For example, in this embodiment, the following optimization function is constructed based on the new energy power generation income item, system configuration cost item, system operation cost item, energy storage loss item, green power curtailment loss item, and load loss item. :

[0159]

[0160]

[0161] in, Revenue from new energy power generation, System investment cost, For system operating costs, The cost of purchasing electricity from the grid, Green power curtailment loss, is the load loss, is the income weight of new energy power generation, is the system investment weight, Weights for system operation, is the power purchase weight of the grid, is the loss weight of green power curtailment, is the load loss weight, is the serial number of each wind turbine, photovoltaic array, and energy storage. For time, is the power generation of new energy at a certain moment, is the electricity price of renewable energy at a certain moment, Selling power to the grid, is the electricity price sold by the power grid at a certain moment, The green power curtailment power at a certain moment, is the cost of green power curtailment at a certain moment, is the load loss power at a certain moment, is the load loss cost at a certain moment, is the operating power of each device at a certain moment, is the operating cost of each device under the operating conditions at a certain moment, is the energy storage decay cost, is the energy storage operation cost function, is the energy storage operating power, is the energy storage charge state, is the energy storage temperature.

[0162] Furthermore, the energy storage operation cost function Select as a function related to energy storage power, energy storage operation cost function It is expressed as follows:

[0163]

[0164] in, is the energy storage attenuation coefficient.

[0165] It should be noted that the optimization function set for each sub-time period can be the same or different. When the operating scenarios of each sub-time period have similar characteristics, using the same optimization function can reduce the complexity of parameter adjustment, improve computational efficiency, and avoid the waste of resources caused by repeatedly setting optimization targets. This is especially suitable for periods of continuous flat electricity prices or stable renewable energy power generation, and can quickly solve the optimal operating strategy with a unified standard. When different sub-time periods face differentiated operating conditions, setting different optimization functions can achieve precise regulation. For example, during peak electricity price periods, the optimization function can focus on balancing the benefits of energy storage discharge with load costs, giving priority to supplementing electricity through energy storage to reduce electricity purchase costs; during valley electricity price periods, the optimization function can focus on energy storage charging strategies, using low-priced electricity to increase energy storage capacity; during periods of high renewable energy output, the optimization function can focus on reducing power abandonment losses and maximizing the absorption of wind and solar power generation. This flexibility enables the system to adjust optimization targets in a targeted manner based on dynamic factors such as time-of-use electricity price fluctuations, changes in renewable energy output, and the SOC status of energy storage, thereby achieving a better balance between economy, reliability, and low carbon. It not only meets the specific operating needs of different time periods, but also improves the overall operating benefits of renewable energy and energy storage systems, effectively solves the complexity of source-load balance at multiple time scales, and enhances the adaptability and robustness of power system scheduling.

[0166] As a possible implementation, the constraints of the sub-time period include: new energy power generation unit power constraint, electrochemical energy storage SOC constraint, energy storage maximum charge and discharge power constraint, load power constraint, grid power constraint, green power curtailment rate constraint, and load supply rate constraint. Each constraint is expressed as follows:

[0167]

[0168] in, For each new energy power generation, is the minimum power generation capacity of the new energy power generation equipment, is the maximum power generation capacity of the new energy power generation equipment, The real-time SOC of the energy storage system, is the lower limit of SOC of the energy storage system, is the upper limit of SOC of energy storage system, is the grid interaction power, is the minimum value of grid interaction power, is the maximum value of grid interaction power, is the operating power of the energy storage system, is the maximum discharge power of the energy storage system, is the maximum charging power of the energy storage system, is the load power, is the minimum load power, is the maximum load power, is the green power curtailment rate in the sub-time range, is the maximum green power curtailment rate, is the load shedding rate in the sub-time range, is the maximum load shedding rate.

[0169] Similarly, in this embodiment, the constraints of the sub-time periods of each time period may be the same or different. Similarly, when the operating scenarios of each sub-time period have similar characteristics, using the same constraints can simplify the calculation process, avoid repeated parameter setting, improve optimization efficiency, and ensure the consistency of the system operation strategy. When different sub-time periods face differentiated operating conditions, setting different constraints can achieve precise regulation. For example, during peak electricity price periods, the load supply rate constraint can be lowered, and at the same time, the restrictions on the power abandonment rate can be relaxed to give priority to meeting the load demand during high electricity price periods; during valley electricity price periods, the load supply rate constraint can be tightened to ensure power supply reliability, and the energy storage charging power constraint can be adjusted to allow a higher charging rate to utilize low-priced electricity to increase energy storage capacity, while imposing stricter constraints on the grid interaction power; during periods when new energy output is large, the green electricity abandonment rate constraint can be increased, and some energy storage charging and discharging constraints can be relaxed to adapt to new energy fluctuations. This flexibility enables the system to dynamically adjust constraint boundaries based on dynamic factors such as time-of-use electricity price fluctuations, changes in renewable energy output, and energy storage SOC status. While ensuring the specific operational safety needs of each time period, it also optimizes resource allocation overall and balances economy and reliability.

[0170] In a further possible implementation, the constraint condition further includes a coupling constraint condition, and the coupling constraint condition includes:

[0171] The average value of the actual wind power operation sequence in a sub-time period is equal to the corresponding value of the comprehensive wind power operation sequence in the sub-time period. The constraint condition is in the form of:

[0172]

[0173] is the corresponding value of the comprehensive wind power operation sequence in t sub-time periods, is the actual wind power operation sequence of the sub-time period that needs to be solved.

[0174] The average value of the actual PV operation sequence of a sub-time period is equal to the corresponding value of the comprehensive PV operation sequence in the sub-time period;

[0175] The algebraic sum of the charge and discharge powers of the actual energy storage operation sequence in a sub-time period is equal to the corresponding value of the comprehensive energy storage operation sequence in that sub-time period;

[0176] The average value of the actual load power sequence of a sub-time period is equal to the corresponding value of the comprehensive load power sequence in the sub-time period.

[0177] Specifically, the actual operating sequence and the comprehensive sequence must meet the consistency of operating characteristics. Within each sub-time period, the average value of the actual wind power operating sequence must be equal to the corresponding value of the comprehensive wind power operating sequence of that sub-time period, ensuring that the actual wind power operating power is consistent with the corresponding value of the comprehensive wind power represented after dimensionality reduction. Similarly, the average value of the actual photovoltaic operating sequence in each sub-time period must be consistent with the corresponding value of the comprehensive photovoltaic sequence to maintain the integrity of the statistical characteristics of the photovoltaic output throughout the entire time period. For energy storage systems, the algebraic sum of the charging and discharging power in the actual energy storage operating sequence, with charging being positive and discharging being negative, must be equal to the corresponding value of the comprehensive energy storage sequence in that sub-time period. This constraint ensures the conservation of the total amount of energy storage energy flow. In terms of load demand, the average value of the actual load sequence must be consistent with the corresponding value of the comprehensive load power sequence of that sub-time period, ensuring that the load characteristics can still truly reflect the actual power consumption level after dimensionality reduction.

[0178] Taking the average value of the actual wind power operation sequence of a sub-time period as an example, which is equal to the corresponding value of the comprehensive wind power operation sequence in the sub-time period, if the calculated actual wind power operation sequence of the sub-time period contains 15 power data points, the corresponding value of the comprehensive wind power operation sequence calculated above in the sub-time period is When the average value of the 15 power data points is equal to , ensuring that the actual wind power operating power is consistent with the comprehensive situation characterizing the wind power output after dimensionality reduction.

[0179] In a further embodiment, the continuous transmission mechanism in step S6 includes: calculating the final state of the wind power array, photovoltaic array, energy storage system, load power, and the final SOC of the energy storage system in the sub-time period based on the actual wind power operation sequence, actual photovoltaic operation sequence, actual energy storage system operation sequence, and actual load power sequence of the sub-time period, and using the calculation results as the initial power of the wind power array, photovoltaic array, energy storage system, load power, and the initial SOC of the energy storage system in the next sub-time period.

[0180] Specifically, taking a sub-period (such as a day) as an example, the actual wind power operation sequence records the wind power output at each time point within that period. The final state is the wind power output power at the last time point of the period. This value is used directly as the initial power for the next period for optimization and solution to reflect the continuity of wind turbine operation. Similarly, for photovoltaic arrays, the final state is the photovoltaic power at the last moment of the period, which is used as the initial power for optimization and solution for the next period. The final SOC calculation of the energy storage system requires the accumulation of the charge and discharge energy during the period: the initial SOC is added to the product of the charge and discharge power at each time point and the time interval, and then divided by the energy storage capacity. The final SOC is calculated and used as the initial SOC for the next period. The final state of load power is the actual load value at the last moment of the period, and the initial load power of the next period is used as the benchmark, ensuring the time series continuity of load demand. In this way, the operating state of each component forms a closed loop within the time period, ensuring the timing consistency of the scheduling strategy and the accuracy of energy calculation.

[0181] In summary, when performing optimization calculations, for each sub-time period, corresponding reference values ​​are extracted from the integrated wind power operation sequence, integrated photovoltaic operation sequence, integrated energy storage operation sequence, and integrated load power sequence as optimization boundaries, and constraints and optimization functions for different sub-time periods are set in the CPLEX solver.

[0182] In summary, in step S6, for each sub-time period, the objective function and its corresponding constraints are converted into a mixed integer linear programming model, where the energy storage charging and discharging state is modeled by binary variables. The boundary reference value of the sub-time period, the initial state of the wind, solar and storage loads, and the time-of-use electricity price parameters are loaded during initialization. CPLEX searches for the optimal solution through the branch and bound method, outputs the actual wind power operation sequence, actual photovoltaic operation sequence, actual energy storage system operation sequence, and actual load power sequence that meet all constraints, and calculates the final state of the wind power array, photovoltaic array, energy storage system, load power, and the final SOC of the energy storage system in the sub-time period as the initial state input for the next sub-time period. All sub-time periods are calculated in sequence, and finally the actual sequences of each sub-period are combined in chronological order to form a complete long-term period scheduling operation mode.

[0183] It should be noted that the specific process of using the CPLEX solver for optimization calculation is common knowledge to those skilled in the art and will not be described in detail in this embodiment.

[0184] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. The scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A hybrid optimization method for new energy and energy storage scheduling operation modes, wherein the hybrid optimization method is applied to a power system including new energy and energy storage, and is characterized in that: The hybrid optimization method comprises: S1. Consider the target time period as a long period and divide the long period into multiple consecutive sub-periods; S2. Calculate the equivalent load demand, the equivalent maximum power generated by the wind turbine, the equivalent maximum power generated by the photovoltaic array, and the equivalent time-of-use electricity price for each sub-time period; S3. Reorganize the equivalent load demand, equivalent maximum power generated by wind turbines, equivalent maximum power generated by photovoltaic arrays, and equivalent time-of-use electricity prices of multiple sub-time periods into a corresponding equivalent sequence of a long period according to the time sequence; S4. Based on the coordinated operation strategy and the equivalent load demand sequence over a long period of time, the equivalent wind turbine maximum power sequence, the equivalent photovoltaic array maximum power sequence, and the equivalent time-of-use electricity price sequence, calculate and obtain the comprehensive wind power operation sequence, the comprehensive photovoltaic operation sequence, the comprehensive energy storage operation sequence, and the comprehensive load power sequence; S5. Using the comprehensive wind power operation sequence, comprehensive photovoltaic operation sequence, comprehensive energy storage system operation sequence, and comprehensive load power sequence as boundary conditions for all sub-time periods, setting optimization functions and constraints for different sub-time periods, optimizing each sub-time period individually, and optimizing and calculating the actual wind power operation sequence, actual photovoltaic operation sequence, actual energy storage system operation sequence, and actual load power sequence for each sub-time period; S6. Use a continuous transmission mechanism to process the actual wind power operation sequence, actual photovoltaic operation sequence, actual energy storage system operation sequence, and actual load power sequence in different sub-time periods, and ultimately form a complete long-term period scheduling operation mode based on the processing results.

2. The hybrid optimization method for new energy and energy storage scheduling operation mode according to claim 1 is characterized in that: The step S2 specifically includes: The target data is regarded as a long-term period data set, and the long-term period data set is divided into multiple one-to-one corresponding sub-time period data sets according to the sub-time periods; For the sub-time period data set, the average values ​​of the load demand, the maximum power that the wind turbine can generate, and the maximum power that the photovoltaic array can generate for each sub-time period are calculated. The electricity price for each period is then weighted according to the corresponding time to obtain the equivalent time-of-use electricity price. The average value of the load demand in each sub-time period, the average value of the maximum power that can be generated by the wind turbine, the average value of the maximum power that can be generated by the photovoltaic array, and the equivalent time-of-use electricity price are used as the equivalent load demand, equivalent maximum power that can be generated by the wind turbine, equivalent maximum power that can be generated by the photovoltaic array, and equivalent time-of-use electricity price corresponding to the sub-time period.

3. The hybrid optimization method for new energy and energy storage scheduling operation mode according to claim 2 is characterized in that: Said step S3 specifically comprises: combining the equivalent load demands in all sub-time periods in chronological order to form an equivalent load demand sequence of a long-time period; The maximum power generation capacity of the equivalent wind turbines in all sub-time periods is combined in chronological order to form a maximum power generation capacity sequence of the equivalent wind turbines in a long time period; The maximum power that can be generated by the equivalent photovoltaic array in all sub-time periods is combined in chronological order to form a maximum power sequence that can be generated by the equivalent photovoltaic array in the long time period; The equivalent time-of-use electricity prices in all sub-time periods are combined in chronological order to form an equivalent time-of-use electricity price sequence for a long time period.

4. The hybrid optimization method for new energy and energy storage scheduling operation mode according to claim 3 is characterized in that: The coordinated operation strategy includes a first coordination rule and a second coordinated operation rule. The first coordination rule is first used to calculate the comprehensive wind power operation sequence, comprehensive photovoltaic operation sequence, comprehensive energy storage operation sequence, and comprehensive load power sequence for a long period of time, and calculate the total load abandonment rate. If the total load abandonment rate exceeds the preset load abandonment rate threshold, the second coordination rule is used to recalculate the above four comprehensive sequences.

5. The hybrid optimization method for new energy and energy storage scheduling operation mode according to claim 4 is characterized in that: The first coordination rule specifically includes: For each sub-period within a long period, if the sum of the equivalent maximum wind power generation and the equivalent maximum photovoltaic power generation in the sub-period is greater than the equivalent load demand, the energy storage system absorbs the excess power consumed by the load. If the energy storage system cannot absorb the excess power, it will be fed back to the external power grid. If the external power grid cannot absorb the excess power, the excess renewable energy will be discarded. If the sum of the equivalent maximum wind power generation and the equivalent maximum photovoltaic power generation in the sub-time period is less than the equivalent load demand, the equivalent time-of-use electricity price period is further determined. If it is during the peak / peak electricity price period, the energy storage system will supplement the insufficient power, and the external grid will not provide power. If the energy storage is insufficient, the load will be abandoned; During the flat electricity price period, the external grid will first supplement the insufficient power. If the power is insufficient, the energy storage system will provide power. If neither the energy storage system nor the external grid can meet the load, the load will be shelved. During off-peak electricity price periods, the external power grid will supplement the insufficient electricity; otherwise, the load will be abandoned and the energy storage system will not provide power.

6. The hybrid optimization method for new energy and energy storage scheduling operation mode according to claim 5 is characterized in that: When the sum of the equivalent maximum wind power generation and the equivalent maximum photovoltaic power generation in the sub-time period is greater than the equivalent load demand, the difference between the renewable energy generation and the equivalent load demand is calculated. : According to the difference , calculate the comprehensive energy storage output of the current sub-time period : The comprehensive energy storage output of multiple sub-time periods constitutes a long-term comprehensive energy storage operation sequence : Calculate the load demand for the current sub-time period: The load demands of multiple sub-time periods constitute the comprehensive load power sequence of the long-term period ; Calculate the comprehensive wind power output of the current sub-time period: The comprehensive wind power output of multiple sub-time periods constitutes a comprehensive wind power operation sequence for a long period of time ; Calculate the comprehensive photovoltaic output of the current sub-time period: The comprehensive photovoltaic output of multiple sub-time periods constitutes a comprehensive photovoltaic operation sequence for a long period of time ; When the sum of the equivalent maximum wind power generation power and the equivalent maximum photovoltaic power generation power in a sub-time period is less than the equivalent load demand, calculate the comprehensive wind power output of the current sub-time period: The comprehensive wind power output of multiple sub-time periods constitutes a comprehensive wind power operation sequence for a long period of time ; Calculate the comprehensive photovoltaic output of the current sub-time period: The comprehensive photovoltaic output of multiple sub-time periods constitutes a comprehensive photovoltaic operation sequence for a long period of time ; If the current sub-time period is during the peak / peak electricity price period, the following formula is used to calculate the comprehensive energy storage output: : If the current sub-time period is in the flat electricity price period, the following formula is used to calculate the comprehensive energy storage output: : If the previous sub-period is during the valley electricity price period, the following formula is used to calculate the comprehensive energy storage output: : The comprehensive energy storage output of multiple sub-time periods constitutes a long-term comprehensive energy storage operation sequence ; Calculate the load demand for the current sub-time period: The load demands of multiple sub-time periods constitute the comprehensive load power sequence of the long-term period ; in, is the jth element of the maximum power generation sequence of the equivalent wind turbine, is the jth element of the maximum power sequence of the equivalent photovoltaic array, Represents the difference between renewable energy generation and equivalent load demand, is the maximum charging power of the energy storage system, is the maximum discharge power of the energy storage system, is the jth element of the equivalent load demand sequence, Indicates the maximum value of the external grid interaction power, represents the external grid interaction power, n is a constant, representing the nth element in the above four comprehensive sequences, represents the amount of wind power curtailed, Indicates the amount of photovoltaic power wasted.

7. The hybrid optimization method for new energy and energy storage scheduling operation mode according to claim 6 is characterized in that: The second coordination rule differs from the first coordination rule only in that, during peak / peak electricity price periods, the energy storage system first supplements the insufficient power, and when the power is insufficient, the external power grid provides power. When the energy storage system and the external power grid cannot meet the load, the load is shelved.

8. The hybrid optimization method for new energy and energy storage scheduling operation mode according to claim 7 is characterized in that: The optimization function of the sub-time period includes a new energy power generation income item, a system configuration cost item, a system operation cost item, an energy storage loss item, a green power curtailment loss item, and a load loss item; The constraints of the sub-time period include: power constraints of new energy power generation units, SOC constraints of electrochemical energy storage, maximum charge and discharge power constraints of energy storage, load power constraints, grid power constraints, green power curtailment rate constraints, and load supply rate constraints.

9. The hybrid optimization method for new energy and energy storage scheduling operation mode according to claim 8 is characterized in that: The constraint conditions also include coupling constraint conditions, and the coupling constraint conditions include: The average value of the actual wind power operation sequence in a sub-time period is equal to the corresponding value of the comprehensive wind power operation sequence in the sub-time period; The average value of the actual PV operation sequence of a sub-time period is equal to the corresponding value of the comprehensive PV operation sequence in the sub-time period; The algebraic sum of the charge and discharge powers of the actual energy storage operation sequence in a sub-time period is equal to the corresponding value of the comprehensive energy storage operation sequence in that sub-time period; The average value of the actual load power sequence of a sub-time period is equal to the corresponding value of the comprehensive load power sequence in the sub-time period.

10. The hybrid optimization method for new energy and energy storage scheduling operation mode according to claim 9 is characterized in that: The continuous transmission mechanism specifically includes: calculating the final state of the wind power array, photovoltaic array, energy storage system, load power, and the final SOC of the energy storage system in the sub-time period based on the actual wind power operation sequence, actual photovoltaic operation sequence, actual energy storage system operation sequence, and actual load power sequence of the sub-time period, and using the calculation results as the initial power of the wind power array, photovoltaic array, energy storage system, load power, and the initial SOC of the energy storage system in the next sub-time period.

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