A base power transmission curve formulation method based on frequency analysis and multi-energy complementary simulation operation

Abstract

The application provides a base power transmission curve fitting method based on frequency analysis and multi-energy complementary simulation operation, comprising: establishing a multi-energy complementary operation simulation model of a clean energy base; analyzing original power transmission process curves of the clean energy base in a simulation year to obtain daily power transmission process characteristic curves of the simulation year; and the daily power transmission process characteristic curve is a multi-section characteristic curve fitted by corresponding probability power transmission characteristic values determined based on probability parameters in each time period within a day. Through multi-section correction, standardized processing, time period data set construction, multi-level clustering and mixed probability distribution function construction of the daily power transmission process, the application quantitatively evaluates possible power transmission processes of the base, further fits a typical multi-section power transmission process, can intuitively judge the performance of the power transmission curve, provides a method for determining a typical power transmission plan of the base, provides a basis for the selection of potential receiving ends, and has a significant supporting role in promoting efficient consumption of new energy.

Description

Technical Field

[0001] This application belongs to the field of clean energy base planning and operation scheduling technology, specifically involving a method for determining the power transmission curve of the base based on frequency analysis and multi-energy complementary simulation operation. Background Technology

[0002] Large-scale, centralized new energy bases (such as large-scale wind and solar power bases in deserts, Gobi, and arid lands) have become the main force in energy transformation. However, the inherent intermittency, volatility, and uncertainty of new energy sources pose significant challenges to the safe and stable operation of the power grid. Existing methods for determining power transmission curves are mostly based on historical data or simple planned output, failing to fully consider the synergistic potential of multi-energy complementarity. New energy bases are typically equipped with flexible resources such as hydropower and energy storage to form multi-energy complementary systems. However, how to accurately simulate operations, deeply explore the regulation potential of various resources, improve the fit with the actual needs of the power grid, and reduce wind and solar curtailment are industry problems that need to be solved. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides a method for determining the power transmission curve of a clean energy base based on frequency analysis and multi-energy complementary simulation operation. This method can effectively conduct power supply area analysis of clean energy bases and determine the power transmission mode and process of the base.

[0004] The technical solution adopted in this invention is as follows:

[0005] This invention provides a method for determining the power transmission curve of a power base based on frequency analysis and multi-energy complementary simulation operation, comprising the following steps:

[0006] Step S1: Obtain relevant data on power system sources, grids, loads, and storage for the clean energy base in the simulated year;

[0007] Step S2: Establish a multi-energy complementary operation simulation model for the clean energy base; the multi-energy complementary operation simulation model includes an objective function system and operating constraints.

[0008] Step S3: Solve the multi-energy complementary operation simulation model to obtain the raw power transmission of the clean energy base for each time period t in the simulated year. , , To simulate the number of time periods in a year, the raw power transmission from the clean energy base in each time period t during the simulated year is obtained. The constructed original power transmission process curve;

[0009] Step S4: Analyze the original power transmission process curve of the clean energy base in the simulated year to obtain the daily power transmission process characteristic curve of the clean energy base in the simulated year; the daily power transmission process characteristic curve is a multi-segment characteristic curve fitted to the power transmission characteristic value of the corresponding probability determined by the probability parameter for each time period within the day.

[0010] Furthermore, the power system source, grid, load, and storage related data include:

[0011] Power data: Data on the operating characteristics of power plants and generating units that participate in the power dispatching of the clean energy base, including wind power, photovoltaic power, hydropower, and thermal power.

[0012] Power grid data: characteristics of power transmission channels and internal collection lines of clean energy bases;

[0013] Energy storage power data: Operational characteristic data of energy storage power stations and units configured in the clean energy base plan; wherein, the energy storage power stations include pumped hydro storage and electrochemical energy storage.

[0014] Furthermore, the objective function system includes an objective function for maximizing the power generation efficiency of the clean energy base and an objective function for maximizing the utilization rate of new energy sources in the clean energy base;

[0015] The operational constraints include power output constraints for wind power plants, power output constraints for photovoltaic power plants, constraints for hydropower plants, constraints for thermal power plants, constraints for energy storage power plants, power transmission capacity constraints for power transmission channels, and power load balance conditions for multi-energy complementary systems in clean energy bases.

[0016] Furthermore, step S4 specifically involves:

[0017] Step S41, for the raw power transmission of each time period t in the simulated year The formed time series , To simulate the number of days in a year, where t represents an hourly time period, the data is re-expressed in days as: Daily Power Transmission Time Series , , Representing the day The original power supply during the specified time period;

[0018] Step S42, Daily power transmission time sequence The original power transmission in each The corrected power supply is obtained by making adjustments. Thus, the corrected daily power transmission time series is obtained. The corrected daily power transmission time series This is a multi-stage daily power supply;

[0019] Step S43: For the corrected daily power delivery time series for all days. Represented in time periods as: Time Series of Power Transmission ;

[0020] Step S44: Using an improved frequency analysis algorithm, analyze the power transmission time series for each time period. Frequency analysis was performed to obtain the results for each time period. Based on the power transmission characteristic values ​​corresponding to the probability parameters determined in the simulated year, the power transmission characteristic values ​​for each time period in the simulated year are fitted to obtain the daily power transmission process characteristic curve.

[0021] Furthermore, step S42 specifically includes:

[0022] Step S421, for the daily power transmission time series An algorithm for calculating local standard deviation using a rolling window is employed for daily power transmission time series. Calculations are performed to obtain the result reflecting the first Local standard deviation time series of the fluctuation characteristics of daily original power transmission volume ;

[0023] Step S422, for the first Daily local standard deviation time series The analysis was conducted to determine the time period in which the first extreme point occurred in the local standard deviation time series. And the time period when the second extreme point appears. ;

[0024] Step S423, by time period and time period As the segmentation boundary point, the daily power transmission time series Divided into three intervals, namely: the first interval Second interval Third interval ;

[0025] Step S424: Calculate the average of all original power transmissions in the first interval as the corrected power transmission for each element in the first interval; calculate the average of all original power transmissions in the second interval as the corrected power transmission for each element in the second interval; calculate the average of all original power transmissions in the third interval as the corrected power transmission for each element in the third interval.

[0026] By sequentially concatenating the corrected power transmission amounts from the first interval, the second interval, and the third interval, the corrected daily power transmission time series is obtained. .

[0027] Furthermore, step S421 specifically includes:

[0028] ① Let the length of the scroll window be The scrolling window moves in one step for the daily power transmission time series. The scrolling window movement and data coverage methods are as follows:

[0029] When the scroll window starts aligned When this happens, the data range within the scrolling window is: When the scroll window starts aligned When this happens, the data range within the scrolling window is: And so on; when the starting position of the scrolling window is aligned... When this happens, the data range within the scrolling window is: ;

[0030] ② When the starting position of the scroll window is aligned hour, The data range within the scrolling window is re-represented as follows: , That is The corresponding local standard deviation is calculated using the following formula. : ;

[0031] Scroll window start position aligned sequentially , ,…, , and thus reflect the first Local standard deviation time series of the fluctuation characteristics of daily original power transmission volume .

[0032] Furthermore, step S44 specifically includes:

[0033] Step S441: Based on the maximum power transmission capacity of the clean energy base's external transmission channel. Power transmission time series during the period The revised power transmission rates in the data are as follows: The standardized power transmission power is obtained by standardizing the power using formula (1). Thus, the standardized time series of power transmission during the time period is obtained. :

[0034] (1);

[0035] Step S442: Standardize the power transmission time series for each time period. The elements within the cluster are clustered by density, and noise points are assigned to the nearest cluster based on their density. A cluster, represented as: ;

[0036] Step S443, for each cluster , Then, fine-grained clustering is performed to obtain fine-grained clusters. , For clusters The number of fine-grained clusters obtained;

[0037] Will Cluster All fine-grained clusters are sorted in ascending order of the standardized power transmission value of the center point of each fine-grained cluster, and then re-represented as: a fine-grained cluster sequence. , The number of all fine-grained clusters;

[0038] Step S444: Based on the results of fine-grained clustering, determine each fine-grained cluster. lower boundary point and the upper boundary point , This enables fine-grained clusters lower boundary point and fine-grained clusters upper boundary point Overlap, thus obtaining each fine-grained cluster interval = ;

[0039] Step S445, for fine-grained clusters The corresponding interval is obtained. For each interval Frequency statistical analysis was conducted to obtain the interval Optimal probability distribution function The method is as follows:

[0040] For interval From the candidate distribution set Select distribution , so that:

[0041] (2);

[0042] (3);

[0043] in: It is an interval Data items The empirical distribution function; It is a distribution In parameters The cumulative distribution function under the given conditions, Determined by maximum likelihood estimation It is a distribution In parameters The probability distribution function under;

[0044] Step S446, calculate using the following formula. each interval The probability density function of the resulting mixed distribution represents the standardized time series of power transmission during the period. Standardized probability density function ;

[0045] (4);

[0046] in: Representative interval The weight, , Representative sequence Falling into the range The amount of data; represent For standardized power transmission, , It is an indicator function, when The value is 1 if it is true, and 0 otherwise. yes exist for The value at time;

[0047] Step S447, using formula Standardize the probability density function Perform a standardized reverse process to obtain the results for each time period. Electricity transmission probability distribution function ; It is the standardized and restored power transmission electricity;

[0048] Step S448, for each time period Electricity transmission probability distribution function Analysis was conducted on the power transmission probability distribution function. The characteristic values ​​of the transmitted power are determined based on probability parameters.

[0049] Furthermore, in step S444, each fine-grained cluster interval The specific method for determining this is as follows:

[0050] when At that time, its fine-grained clusters lower boundary point fine-grained clusters The minimum value of all elements in the set;

[0051] when At that time, fine-grained clusters upper boundary point with fine-grained clusters lower boundary point The formula is the same: ,in: Represents fine-grained clusters The maximum value of all elements in the array; Represents fine-grained clusters The minimum value of all elements in the set;

[0052] when At that time, fine-grained clusters upper boundary point fine-grained clusters The maximum value of all elements in the set.

[0053] Furthermore, step S448 specifically includes:

[0054] Determine the time period probability parameters During the period Electricity transmission probability distribution function In the middle, select probability parameters Determine the characteristic values ​​of the transmitted power.

[0055] Furthermore, the selection probability parameter The specific characteristics of the transmitted power are determined as follows:

[0056] ① During the time period Electricity transmission probability distribution function In the corresponding power transmission probability distribution curve, the maximum probability of determining the peak point location. and maximum power transmission ;

[0057] ②If ,but This refers to the characteristic value of the transmitted power.

[0058] ③If ,but:

[0059] Solve the following formula to determine the change. :

[0060] Based on the amount of change Determine probability parameters The corresponding lower bound of the power transmission range and the Upper Realm This allows for the determination of the power transmission range;

[0061] The median of the power transmission range is taken as the characteristic value of the power transmission.

[0062] The method for determining the power transmission curve of a power base based on frequency analysis and multi-energy complementary simulation operation provided by this invention has the following advantages:

[0063] 1. This invention discloses a method for determining the power transmission curve of a clean energy base based on frequency analysis and multi-energy complementary simulation operation. Through multi-energy complementary operation simulation of a clean energy base, the working boundary and actual power generation capacity process and upper and lower limits of the base are reasonably determined under the conditions of considering operational efficiency and safe and stable operation of the units. On the basis of ensuring the economy and safety of the base's power transmission plan, this provides a clear and reliable operating range basis for determining the power transmission curve, effectively improving the overall efficiency of the power system. 2. This invention discloses a method for determining the power transmission curve of a power base based on frequency analysis and multi-energy complementary simulation operation. By modifying it into a multi-segment daily power transmission, standardizing the process, constructing time-period datasets, and using multi-level clustering and mixed probability distribution functions, the method quantifies and evaluates the possible power transmission process of the power base, and then determines a typical multi-segment power transmission process. This method can intuitively judge the performance of the power transmission curve and provides a set of methods for determining the typical power transmission plan of the power base. It provides a basis for the selection of potential receiving ends and plays a significant supporting role in promoting the efficient consumption of new energy. Attached Figure Description

[0064] Figure 1 A flowchart of a method for determining a power transmission curve for a base based on frequency analysis and multi-energy complementary simulation operation, provided by the present invention;

[0065] Figure 2 Provided for embodiments of the present invention Frequency statistics results for the time period;

[0066] Figure 3 Provided for embodiments of the present invention Power transmission probability distribution function during a time period The corresponding curve graph;

[0067] Figure 4 The proposed three-stage daily power transmission process characteristic curve is provided for the embodiments of the present invention. Detailed Implementation

[0068] To make the technical problems solved, the technical solutions, and the beneficial effects of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and are not intended to limit the invention.

[0069] See Figure 1 This invention provides a method for determining the power transmission curve of a power base based on frequency analysis and multi-energy complementary simulation operation, comprising the following steps:

[0070] Step S1: Obtain relevant data on power system sources, grids, loads, and storage for clean energy bases (sending end) in the simulated year;

[0071] The power system source, grid, load, and storage related data include:

[0072] Power data: Data on the operating characteristics of power plants and generating units that participate in the power dispatching of the clean energy base, including wind power, photovoltaic power, hydropower, and thermal power.

[0073] Power grid data: characteristics of power transmission channels in clean energy bases (maximum power transmission capacity, whether they are blocked, blocking capacity, etc.) and characteristics of internal collection lines in the bases (maximum line capacity, etc.).

[0074] Energy storage power data: Operational characteristic data of energy storage power stations and units configured in the clean energy base plan; wherein, the energy storage power stations include pumped hydro storage and electrochemical energy storage.

[0075] Step S2: Establish a multi-energy complementary operation simulation model for the clean energy base; the multi-energy complementary operation simulation model includes an objective function system and operating constraints.

[0076] The objective function system includes the objective function for maximizing the power generation efficiency of the clean energy base and the objective function for maximizing the utilization rate of new energy sources in the clean energy base;

[0077] The operational constraints include power output constraints for wind power plants, power output constraints for photovoltaic power plants, constraints for hydropower plants, constraints for thermal power plants, constraints for energy storage power plants, power transmission capacity constraints for power transmission channels, and power load balance conditions for multi-energy complementary systems in clean energy bases.

[0078] S21: Objective function system, mainly including:

[0079] 1) Maximizing the power generation efficiency of the clean energy base. This objective considers the complex constraints of multiple power sources. By optimizing the energy storage operation mode and time-series output allocation, the overall power generation efficiency of the base is maximized. The objective function can be expressed as:

[0080]

[0081] in: To generate electricity for clean energy bases; , , , and These are wind power stations, photovoltaic power stations, hydropower stations, thermal power stations, and energy storage power stations, respectively, during the specified time periods. The power generation benefits are expressed in ten thousand yuan. , These represent the effective power outputs of the i-th wind power station and the j-th photovoltaic power station during time period t. For the first The effective output of a hydropower station during time period t For the effective output of the r-th thermal power plant in time period t, The effective output of the k-th energy storage power station in time period t is divided into effective power generation output and effective energy storage output, depending on whether the energy storage power station is in the power generation state. The unit is MW. and The feed-in tariffs for wind power and solar power are respectively. For the first The electricity price of a hydroelectric power station, To standardize the benchmark electricity price for thermal power within the specified scope, The electricity price for the k-th energy storage power station (different prices are used depending on the operating conditions), in yuan / MW; Let t be the length of the time period, which is generally taken as 1 hour during the planning stage; , , , , These represent the total number of wind power stations, photovoltaic power stations, hydropower stations, thermal power stations, and energy storage power stations in a multi-energy complementary power generation system, in units of [number]. This refers to the number of time periods t that the planning year has.

[0082] It should be noted that for clean energy bases, wind power and photovoltaic power are generally required, while hydropower or thermal power may not be required. Therefore, the above formula can be simplified according to the actual power source configuration of the base.

[0083] 2) Maximizing the utilization rate of new energy sources: This objective is achieved by fully adjusting and optimizing the operation mode of energy storage power stations in multi-energy complementary power generation systems. While meeting the operational boundary conditions of multiple power sources and balancing the needs of the receiving-end power system and the transmission requirements of power transmission projects, the goal is to maximize the absorption of wind and solar power output and thus maximize the utilization rate of new energy sources. The simulated operation objective is described as follows:

[0084]

[0085] in: The function is the utilization rate of new energy sources; and These represent the installed capacity of the i-th wind power station and the j-th photovoltaic power station, respectively, in ten thousand kW; other symbols have the same meaning as before.

[0086] S22: Operational requirements and constraints for various types of power sources and systems in a multi-energy complementary base, mainly including:

[0087] 1) Wind power plant output constraints

[0088]

[0089] in: , For the i-th wind power station, during the time period Minimum and maximum output limits, in units of 10,000 kW, of which Generally, no more than the i-th wind power station in the time period Design output .

[0090] 2) Output constraints of photovoltaic power plants

[0091]

[0092] in: , Let be the minimum and maximum allowable output of the j-th photovoltaic power station during time period t, respectively, in kW. Generally, the design output of the j-th photovoltaic power station in time period t shall not exceed that of the j-th photovoltaic power station. .

[0093] 3) Constraints of the hydropower station

[0094] 3-1) Water balance constraints of hydropower stations

[0095]

[0096] in: , The first The water storage capacity of a hydropower station at the beginning and end of time period t is expressed in 10,000 m3. For the first The inflow rate of a hydropower station during time period t is expressed in m3 / s. For the first The outflow from a hydropower station during time period t is expressed in m³ / s; other symbols have the same meaning as before.

[0097] 3-2) Upper and lower limits and amplitude constraints of water level at hydropower stations

[0098]

[0099]

[0100] in: For the first The initial water level of a hydropower station at time t, in meters; For the first The lower limit of the permissible water level at the beginning of time period t for each hydropower station, in meters; For the first The upper limit of the allowable water level at the beginning of time period t for each hydropower station is expressed in meters. Generally, the normal high water level is selected during the non-flood season, and the flood limit water level corresponding to the flood control needs is selected during the flood season. For the first The upper limit of water level fluctuation for a hydropower station in time period t is expressed in meters. Due to environmental and navigational needs of the hydropower station, the water level fluctuation needs to be limited to a certain range; other symbols have the same meaning as before.

[0101] 3-3) Initial and final water level constraints during the hydropower station's dispatching period

[0102]

[0103]

[0104] in: , The first Iterative calculation of initial water level and initial water level during the scheduling period of each hydropower station; , The first The lower and upper limits of the water level at the end of the scheduling period for each hydropower station, in meters; other symbols have the same meaning as before.

[0105] 3-4) Constraints on outflow from hydropower stations

[0106]

[0107]

[0108] in: and The first The lower and upper limits of the allowable discharge flow of a hydropower station in time period t, in m3 / s; and The first The power generation flow and water discharge flow of a hydropower station in time period t are expressed in m3 / s; other symbols have the same meaning as above.

[0109] 3-5) Power output constraints of hydropower stations

[0110]

[0111]

[0112] in: , The first The lower and upper limits of the allowable output of a hydropower station in time period t, in units of 10,000 kW, where the maximum output is generally the station’s intended output. This indicates the variation in hydropower output between adjacent time periods, in ten thousand kW. This indicates the upper limit of hydropower output variation, in units of 10,000 kW; other symbols have the same meaning as above.

[0113] 3-6) Head constraints of hydropower stations

[0114]

[0115] in: For the first The head of the hydroelectric power station; , The first The allowable water head and lower limit for each hydropower station.

[0116] In addition, hydropower output is also affected by the requirements for comprehensive utilization of water resources (such as flood control, sediment removal, and ice control). When modeling hydropower stations mathematically, these requirements are mainly converted into constraints such as discharge flow and water level control based on the actual engineering situation. For example, the discharge flow of the hydropower station cannot be lower than its lower limit (depending on ecological and water supply needs), and the reservoir water level cannot be higher than its upper limit (the flood control limit water level during the flood season).

[0117] 4) Thermal power

[0118] 4-1) Output constraints of thermal power plants

[0119]

[0120] in: The effective output of the r-th thermal power plant during time period t; Minimum technical output for the r-th thermal power plant; This represents the maximum output of the r-th thermal power plant.

[0121] 4-2) Climbing Constraints (Peak Shaving Amplitude Limit Constraints):

[0122]

[0123]

[0124] in: The effective output of the r-th thermal power plant during time period t-1; , This is to limit the uphill and downhill climbing of thermal power units.

[0125] 5) Energy storage power station

[0126] 5-1) Energy balance constraint

[0127]

[0128] in: , These represent the total energy storage capacity of the kth energy storage power station at time period t+1 and time period t, respectively, in ten thousand kWh. , , respectively, are the charging and power generation efficiencies of the kth energy storage power station, which are dimensionless; and These represent the energy storage output and power generation output of the kth energy storage power station during time period t, respectively. and These represent the total energy storage capacity of the k-th energy storage power station at the beginning and end of the period, respectively. Let be the rated installed capacity of the k-th energy storage power station; This represents the number of full-capacity utilization hours of the k-th energy storage power station. The total energy storage coefficient at the beginning of the multi-energy complementary operation simulation period for the k-th energy storage power station is a real number between 0 and 1, such as... A value of 0 indicates that the energy storage power station is in a state of no energy storage before the multi-energy complementary operation simulation is carried out; a value of 1 indicates that the energy storage power station is in a state of full energy storage before the multi-energy complementary operation simulation is carried out. Considering energy balance, the energy of the energy storage power station remains balanced on the time scale from the beginning to the end of the multi-power multi-energy complementary operation simulation period. It should return to the state of that energy storage when it starts from a certain energy storage and when it ends. The meanings of other symbols are the same as before.

[0129] 5-2) Output constraints of energy storage power stations

[0130]

[0131]

[0132] in: and Let represent the power generation and energy storage output of the k-th energy storage power station in time period t, respectively; , Let represent the minimum and maximum power generation constraints of the k-th energy storage power station in time period t, respectively, in units of 10,000 kW. The maximum power generation constraint is affected by two factors: the rated power generation capacity of the energy storage power station and the effective power generation that the remaining energy storage capacity of the energy storage power station can provide relative to the length of a single dispatch period. , These represent the minimum and maximum energy storage output constraints of the k-th energy storage power station in time period t, respectively, in units of 10,000 kW. Similarly, the maximum output constraint is affected by two factors: first, the rated energy storage capacity of the energy storage power station; and second, the difference between the maximum energy storage capacity and the current energy storage capacity of the energy storage power station relative to the length of a single dispatch period, which can provide effective energy storage output.

[0133] 5-3) Operating constraints of energy storage units

[0134]

[0135] in: , These represent the indicators of whether the k-th energy storage power station is in energy storage or power generation mode during time period t. They are dimensionless, discrete integer variables, and can only take the values ​​0 or 1, where 0 represents not being in this mode and 1 represents being in this mode. Generally, pumped storage cannot operate in both power generation and pumping modes simultaneously. If one unit of the k-th energy storage power station is operating in power generation mode during time period t, then the other units of that power station cannot be in pumping mode, and vice versa. Although electrochemical energy storage can theoretically operate in both energy storage and power generation modes simultaneously, this should not occur in multi-energy complementary application scenarios. Therefore, electrochemical energy storage or other new types of energy storage are still subject to the constraints of the above formula.

[0136] 5-4) Reservoir capacity constraints and water balance of pumped storage power stations (other energy storage power stations do not have this constraint).

[0137]

[0138]

[0139] in: , These represent the real-time reservoir capacities of the upper and lower reservoirs of the k-th pumped storage power station at time t, respectively, in ten thousand m³. , These represent the maximum and minimum reservoir capacity constraints of the upper reservoir of the k-th pumped storage power station, respectively, in 10,000 m3. Since the water level and reservoir capacity constraints are constrained by the water level-capacity curve of the upper reservoir, satisfying the reservoir capacity constraint means that the water level constraint is also satisfied at the same time. , These represent the maximum and minimum reservoir capacity constraints of the k-th pumped storage power station's reservoir, respectively, in tens of thousands of m³.

[0140] 6) Power transmission capacity constraints of transmission channels

[0141]

[0142] in: and These represent the minimum and maximum capacity that a power transmission project can transmit during time period t, in tens of thousands of kW. This represents the power transmission capacity of the power transmission project during time period t; other symbols have the same meaning as before. It is worth noting that, unless otherwise constrained, It equals the maximum power transmission capacity designed for power transmission projects.

[0143] 7) Power load balancing of multi-energy complementary systems

[0144] If we consider the matching of the power output process of the clean energy base with the demand load characteristics of the receiving end power system, in planning and design practice, a typical power transmission curve is generally formulated in the multi-source operation simulation to constrain the power transmission process of the base. Taking the wind-solar-storage multi-energy complementary integrated base as an example, the specific details are as follows.

[0145]

[0146] in: This represents the power transmitted during time period t in the proposed typical power transmission curve; other symbols have the same meaning as before. It is worth noting that if a typical power transmission curve for a clean energy base is proposed, then when the output of the multi-energy interconnected power generation system exceeds the load during time period t of the curve, the excess power is considered unused, and it is determined that power curtailment occurs at this time. The curtailment method is based on the ratio of wind and solar power station output during the time period, with wind curtailment and solar curtailment respectively.

[0147] Step S3: Solve the multi-energy complementary operation simulation model to obtain the raw power transmission of the clean energy base for each time period t in the simulated year. , , The number of time periods in the simulated year is typically taken as 8760 hours, thus obtaining the raw power transmission from the clean energy base in each time period t during the simulated year. The constructed original power transmission process curve;

[0148] Specifically, based on the multi-energy complementary operation simulation model of the clean energy base, the complementary scheduling operation of each power source in the clean energy base is carried out. According to the set objective function and constraints, the optimized power supply process for 8760 hours is determined.

[0149] In practical applications, different objective functions (objective function S21-1 or objective function S21-2 or a combination of objective functions S21-1 and S21-2) are selected to solve the multi-energy complementary operation simulation model of the clean energy base and carry out an 8760-hour time-series simulation to obtain the simulated power transmission process of the clean energy base. .

[0150] Step S4: Analyze the original power transmission process curve of the clean energy base in the simulated year to obtain the daily power transmission process characteristic curve of the clean energy base in the simulated year; the daily power transmission process characteristic curve is a multi-segment characteristic curve fitted to the power transmission characteristic value of the corresponding probability determined by the probability parameter for each time period within the day.

[0151] This step is a key innovation of the present invention, specifically including:

[0152] Step S41, for the raw power transmission of each time period t in the simulated year The formed time series , To simulate the number of days in a year, where t represents an hourly time period, the data is re-expressed in days as: Daily Power Transmission Time Series , , Representing the day The original power supply during the specified time period;

[0153] Step S42, Daily power transmission time sequence The original power transmission in each The corrected power supply is obtained by making adjustments. Thus, the corrected daily power transmission time series is obtained. The corrected daily power transmission time series This is a multi-stage daily power supply;

[0154] Depending on actual needs, a three-stage or five-stage daily power transmission process can be adopted. A three-stage daily power transmission process refers to: dividing the daily power transmission time sequence... The process is divided into three segments, with equal power transmission within each segment; similarly, for a five-segment daily power transmission process, the daily power transmission time sequence is... The system is divided into five segments, with equal power delivery within each segment. The specific number of segments can be adjusted flexibly according to actual needs.

[0155] The specific correction process is as follows:

[0156] Step S421, for the daily power transmission time series An algorithm for calculating local standard deviation using a rolling window is employed for daily power transmission time series. Calculations are performed to obtain the result reflecting the first Local standard deviation time series of the fluctuation characteristics of daily original power transmission volume ;

[0157] This step is specifically as follows:

[0158] ① Let the length of the scroll window be If it needs to be divided into three sections, then Set to 8; if you need to divide it into five segments, then Set to 5. The scrolling window movement step is 1, for daily power transmission time series. The scrolling window movement and data coverage methods are as follows:

[0159] When the scroll window starts aligned When this happens, the data range within the scrolling window is: When the scroll window starts aligned When this happens, the data range within the scrolling window is: And so on; when the starting position of the scrolling window is aligned... When this happens, the data range within the scrolling window is: ;

[0160] ② When the starting position of the scroll window is aligned hour, The data range within the scrolling window is re-represented as follows: , That is The corresponding local standard deviation is calculated using the following formula. : ;

[0161] Scroll window start position aligned sequentially , ,…, , and thus reflect the first Local standard deviation time series of the fluctuation characteristics of daily original power transmission volume .

[0162] Step S422, for the first Daily local standard deviation time series The analysis was conducted to determine the time period in which the first extreme point occurred in the local standard deviation time series. And the time period when the second extreme point appears. ;

[0163] Step S423, by time period and time period As the segmentation boundary point, the daily power transmission time series Divided into three intervals, namely: the first interval Second interval Third interval ;

[0164] Step S424: Calculate the average of all original power transmissions in the first interval as the corrected power transmission for each element in the first interval; calculate the average of all original power transmissions in the second interval as the corrected power transmission for each element in the second interval; calculate the average of all original power transmissions in the third interval as the corrected power transmission for each element in the third interval.

[0165] By sequentially concatenating the corrected power transmission amounts from the first interval, the second interval, and the third interval, the corrected daily power transmission time series is obtained. .

[0166] Step S43: For the corrected daily power delivery time series for all days. Represented in time periods as: Time Series of Power Transmission ;

[0167] Step S44: Using an improved frequency analysis algorithm, analyze the power transmission time series for each time period. Frequency analysis was performed to obtain the results for each time period. Based on the power transmission characteristic values ​​corresponding to the probability parameters determined in the simulated year, the power transmission characteristic values ​​for each time period in the simulated year are fitted to obtain the daily power transmission process characteristic curve.

[0168] This step is specifically as follows:

[0169] Step S441: Based on the maximum power transmission capacity of the clean energy base's external transmission channel. Power transmission time series during the period The revised power transmission rates in the data are as follows: The standardized power is obtained by using formula (1) to avoid the influence of dimensions. Thus, the standardized time series of power transmission during the time period is obtained. :

[0170] (1);

[0171] Step S442: Standardize the power transmission time series for each time period. The elements within the cluster are clustered by density, and noise points are assigned to the nearest cluster based on their density. A cluster, represented as: ;

[0172] Specifically, in this step, the DBSCAN algorithm can be used to standardize the power transmission time series for each time period. Density clustering of elements within the range yields... A set of clusters and noise points.

[0173] For each noise point in the noise point set, assign it to the nearest cluster based on its density. Specifically, for each noise point... Calculate the minimum distance from the point to all points in each cluster, and assign it to the cluster with the minimum distance.

[0174] Step S443, for each cluster , Then, fine-grained clustering is performed, for example, using the K-means algorithm, to obtain fine-grained clusters. , For clusters The number of fine-grained clusters obtained;

[0175] Will Cluster All fine-grained clusters are sorted in ascending order of the standardized power transmission value of the center point of each fine-grained cluster, and then re-represented as: a fine-grained cluster sequence. , The number of all fine-grained clusters;

[0176] Step S444: Based on the results of fine-grained clustering, determine each fine-grained cluster. lower boundary point and the upper boundary point , This enables fine-grained clusters lower boundary point and fine-grained clusters upper boundary point Overlap, thus obtaining each fine-grained cluster interval = ;

[0177] In this step, each fine-grained cluster interval The specific determination method is as follows:

[0178] when At that time, its fine-grained clusters lower boundary point fine-grained clusters The minimum value of all elements in the set;

[0179] when At that time, fine-grained clusters upper boundary point with fine-grained clusters lower boundary point The formula is the same: ,in: Represents fine-grained clusters The maximum value of all elements in the array; Represents fine-grained clusters The minimum value of all elements in the set;

[0180] when At that time, fine-grained clusters upper boundary point fine-grained clusters The maximum value of all elements in the set.

[0181] Step S445, for fine-grained clusters The corresponding interval is obtained. For each interval Frequency statistical analysis was conducted to obtain the interval Optimal probability distribution function The method is as follows:

[0182] For interval From the candidate distribution set Select distribution , so that:

[0183] (2);

[0184] (3);

[0185] in: It is an interval Data items The empirical distribution function; It is a distribution In parameters The cumulative distribution function under the given conditions, Determined by maximum likelihood estimation It is a distribution In parameters The probability distribution function; candidate distribution set This includes the gamma distribution, beta distribution, and Weibull distribution.

[0186] Step S446, calculate using the following formula. each interval The probability density function of the resulting mixed distribution represents the standardized time series of power transmission during the period. Standardized probability density function ;

[0187] (4);

[0188] in: Representative interval The weight, , Representative sequence Falling into the range The amount of data; represent For standardized power transmission, , It is an indicator function, when The value is 1 if it is true, and 0 otherwise. yes exist for The value at time;

[0189] Step S447, using formula Standardize the probability density function Perform a standardized reverse process to obtain the results for each time period. Electricity transmission probability distribution function ; It is the standardized and restored power transmission electricity;

[0190] Step S448, for each time period Electricity transmission probability distribution function Analysis was conducted on the power transmission probability distribution function. The characteristic values ​​of the transmitted power are determined based on probability parameters.

[0191] This step specifically involves: determining the time period. probability parameters During the period Electricity transmission probability distribution function In the middle, select probability parameters Determine the characteristic values ​​and probability parameters of the transmitted power. The value ranges from 0.9 to 1.

[0192] In this step, the selection probability parameter The specific characteristics of the transmitted power are determined as follows:

[0193] ① During the time period Electricity transmission probability distribution function In the corresponding power transmission probability distribution curve, the maximum probability of determining the peak point location. and maximum power transmission ;

[0194] ②If ,but This refers to the characteristic value of the transmitted power.

[0195] ③If ,but:

[0196] Solve the following formula to determine the change. :

[0197] Based on the amount of change Determine probability parameters The corresponding lower bound of the power transmission range and the Upper Realm This allows for the determination of the power transmission range;

[0198] The median of the power transmission range is taken as the characteristic value of the power transmission.

[0199] It should be noted that the probability parameter This needs to be determined by analyzing the power system and electricity consumption characteristics of the potential power supply area. Furthermore, probability parameters for different time periods are also required. They can be different; for example, the probability parameter for time period 1 can be 0.95, and the probability parameter for time period 2 can be 0.97.

[0200] It is important to emphasize that the above description of this invention provides a method for probabilistically analyzing daily (24h) power transmission processes on an annual scale. However, this method can be extended to different time scales. For example, on a monthly scale, the 8760h process is first divided into months. For instance, January has 31 days, so the process length is 31*24h. Using this as a data sample, a time-by-time power transmission set for January is constructed, ultimately yielding the daily time-by-time power transmission process for January. Then, the process is iterated through February to December to obtain the daily time-by-time power transmission process for each month. Furthermore, the power transmission process used for frequency analysis can also be simulated over many years (8760h*N).

[0201] Taking a power transmission channel with a maximum external transmission capacity of 1000MW as an example for verification, the method of this invention was used to obtain the following results: Figure 2 shown The frequency statistics results for the time period are then obtained. Figure 3 shown Power transmission probability distribution function during a time period And then get Figure 4 The three-segment daily power transmission process characteristic curves are shown.

[0202] The present invention provides a method for determining the power transmission curve of a power base based on frequency analysis and multi-energy complementary simulation operation, which has the following characteristics:

[0203] Traditional methods for determining power transmission curves are generally based on the analysis of potential receiving-end load characteristics or historical experience. These methods fail to fully leverage the multi-energy complementarity benefits of clean energy bases, do not adequately reflect the actual operating conditions of various power sources and units within the base, and cannot quantitatively assess the matching degree between the base's power generation capacity and power transmission demand. In contrast, this invention performs characteristic analysis on the power transmission over each time period of the simulated year, ultimately obtaining a daily power transmission curve that is identical throughout the simulated year. It then conducts probabilistic characteristic analysis of the base's power transmission process over a long timescale, quantitatively assessing the base's power transmission capacity. This improves the simulation efficiency of multi-energy complementarity and allows for the development of power transmission curves that better align with the base's operational patterns and enhance its efficiency.

[0204] The present invention provides a method for determining the power transmission curve of a power base based on frequency analysis and multi-energy complementary simulation operation, which has the following advantages: 1. The present invention discloses a method for determining the power transmission curve of a power base based on frequency analysis and multi-energy complementary simulation operation. Through multi-energy complementary operation simulation of clean energy bases, it reasonably determines the working boundary and actual power generation capacity process and upper and lower limits of the base under the conditions of considering operational efficiency and safe and stable operation of units. On the basis of ensuring the economy and safety of the power transmission plan of the base, it provides a clear and reliable operational range basis for determining the power transmission curve, effectively improving the overall efficiency of the power system. 2. The present invention discloses a method for determining the power transmission curve of a power base based on frequency analysis and multi-energy complementary simulation operation. By modifying it to multi-segment daily power transmission, standardizing processing, constructing time period datasets and multi-level clustering, and constructing mixed probability distribution functions, it quantitatively evaluates the possible power transmission process of the base, and then determines a typical multi-segment power transmission process. It can intuitively judge the performance of the power transmission curve, and provides a method for determining the typical power transmission plan of the base, providing a basis for the selection of potential receiving ends, and playing a significant supporting role in promoting the efficient consumption of new energy.

[0205] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A base power transmission curve formulation method based on frequency analysis and multi-energy complementary analog operation, characterized in that, The method comprises the following steps: Step S1, obtaining power system source, network, load and storage related data of a clean energy base in a simulation year; Step S2, establishing a multi-energy complementary operation simulation model of the clean energy base; the multi-energy complementary operation simulation model comprises a target function system and operation constraints; Step S3, solving the multi-energy complementary operation simulation model to obtain the original power transmission of the clean energy base in the simulation year by time period t , , The number of time periods in the simulation year is obtained, so as to obtain the original power transmission process curve of the clean energy base in the simulation year by time period t constructed The target function system comprises a clean energy base power generation benefit maximum target function and a clean energy base new energy utilization rate maximum target function; the operation constraints comprise wind power station output constraints, photovoltaic power station output constraints, hydropower station constraints, thermal power station constraints, energy storage power station constraints, power transmission channel transmission capacity constraints and clean energy base multi-energy complementary system power load balance conditions; Step S4, analyzing the original power transmission process curve of the clean energy base in the simulation year to obtain a daily power transmission process characteristic curve of the clean energy base in the simulation year; the daily power transmission process characteristic curve is a multi-section characteristic curve fitted by characteristic values of corresponding probabilities determined based on probability parameters in each time period within a day; Step S4 specifically comprises: Step S41, for each time period t of the simulated year, the raw delivered power time series formed , for the simulated year has days, t is the hour period, expressed in terms of days as: daily delivered power time series , , represents the raw delivered power for the day, the hour period Step S42, Daily power transmission time sequence The original power transmission in each The corrected power supply is obtained by making adjustments. Thus, the corrected daily power transmission time series is obtained. The corrected daily power transmission time series This is for multi-stage daily power delivery; Step S43, for all days, the corrected daily delivered power time series re-expressed in time periods as: time period delivered power time series ; Step S44, using the improved frequency analysis algorithm, the power time series of each period Frequency analysis is performed to obtain the characteristic value of the power of each period The characteristic value of the power of each period in the simulation year is fitted to the corresponding probability of the power determined based on the probability parameters in the simulation year, and the characteristic curve of the daily power process is obtained.

2. The base power transmission curve formulation method based on frequency analysis and multi-energy complementary simulation operation according to claim 1, characterized in that, The power system source, network, load and storage related data comprises: Power source data: data of power sources participating in base power transmission dispatching and unit operation characteristics in the power system of the clean energy base, including wind power, photovoltaic power, hydropower and thermal power; Power grid data: characteristics of power transmission channels of the clean energy base and characteristics of internal collection lines of the base; Energy storage power source data: data of energy storage power stations and unit operation characteristics configured in the clean energy base planning; the energy storage power stations comprise pumped storage and electrochemical energy storage.

3. The base power transmission curve formulation method based on frequency analysis and multi-energy complementary simulation operation according to claim 1, characterized in that, Step S42 specifically comprises: Step S421, for the daily power transmission time series , using the algorithm of rolling window calculation of local standard deviation, the daily power transmission time series is calculated to obtain the local standard deviation time series reflecting the fluctuation characteristics of the first day original power transmission ; Step S422, the first extreme point in the local standard deviation time series appears at the time period Step S423, the second extreme point in the local standard deviation time series appears at the time period Step S424, the time period when the first extreme point appears and the time period when the second extreme point appears are analyzed to determine the time period when the extreme point appears Step S425, the time period when the extreme point appears is determined as the time period when the extreme point appears ; Step S423, dividing the time series of daily power supply power into three intervals, respectively, as the segment boundary points: a first interval ; a second interval ; and a third interval . As the segment boundary points, the time series of daily power supply power is divided into three intervals, respectively: a first interval ; a second interval ; and a third interval . Step S424, calculating the mean value of all original power transmission powers in the first interval as the corrected power transmission power of each element in the first interval; calculating the mean value of all original power transmission powers in the second interval as the corrected power transmission power of each element in the second interval; and calculating the mean value of all original power transmission powers in the third interval as the corrected power transmission power of each element in the third interval; The first interval corrected power, the second interval corrected power, and the third interval corrected power are sequentially spliced to obtain a corrected daily power time series .

4. The base power transmission curve formulation method based on frequency analysis and multi-energy complementary simulation operation according to claim 3, characterized in that, Step S421 specifically comprises: Let the rolling window length be , the rolling window moving step be 1, and the daily power transmission time series be , the rolling window moving and data coverage range be as follows: When the scroll window start bit is aligned then the in-window data range of the scroll window is: ; When the scroll window start bit is aligned then the in-window data range of the scroll window is: and so on; When the scroll window start bit is aligned then the in-window data range of the scroll window is: ; ii. When the start of the rolling window is aligned , , the in-window data range of the rolling window is re-expressed as: , , i.e. ; the corresponding local standard deviation is calculated using the following formula: : ; The rolling window start bits are sequentially aligned , ,…, , and then the local standard deviation time series reflecting the fluctuation characteristics of the original power transmission power on the first day is obtained .

5. The base power transmission curve formulation method based on frequency analysis and multi-energy complementary simulation operation according to claim 1, characterized in that, Step S44 specifically comprises: Step S441, according to the maximum power transmission capacity of the clean energy base external transmission channel , each modified power transmission in the time period power time series , the formula (1) is used for standardization processing, and the standardized power transmission is obtained , thereby obtaining the time period power transmission standardization time series :​ （1）； Step S442, density clustering is performed on the elements in the power standardization time series of each time period, and noise points are attributed to the nearest cluster according to their density, obtaining clusters, denoted as: ;​ Step S443, for each cluster , , fine-grained clustering is further performed to obtain fine-grained clusters , The number of fine-grained clusters obtained is ​ will be described below. one cluster of all fine-grained clusters, in ascending order of the normalized power-on values of the center points of the respective fine-grained clusters, and is denoted as: fine-grained cluster sequence , is the number of all fine-grained clusters; Step S444: Based on the results of fine-grained clustering, determine each fine-grained cluster. lower boundary point and the upper boundary point , This enables fine-grained clusters lower boundary point and fine-grained clusters upper boundary point Overlap, thus obtaining each fine-grained cluster interval = ; Step S445, for the fine-grained cluster , the corresponding interval is obtained , the frequency statistical analysis is carried out for each interval , and the optimal probability distribution function of the interval is obtained, and the method is as follows: For interval from a set of candidate distributions selecting a distribution such that: （2）； （3）； wherein: is the empirical distribution function of the data items of the interval ; is the cumulative distribution function of the distribution at the parameters , determined by maximum likelihood estimation, is the probability distribution function of the distribution at the parameters ; Step S446, the following formula is used to calculate one interval The probability density function of the mixed distribution formed represents the period power standardization time series The standardization probability density function ; （4）； wherein: the weight of the interval , , the sequence of intervals the amount of data in the interval ; denotes the normalized power delivered, , is an indicator function, which is 1 when and 0 otherwise; is the value of at is ; Step S447, using the formula , the normalized probability density function is obtained by performing a normalized reverse process on each period of the power transmission power probability distribution function ; is the normalized and restored power transmission power; Step S448, for each time period of the power transmission power probability distribution function analysis, the power transmission power characteristic value determined based on the probability parameter in the power transmission power probability distribution function .

6. The base power transmission curve formulation method based on frequency analysis and multi-energy complementary simulation operation according to claim 5, characterized in that, In step S444, each fine-grained cluster of intervals is determined in particular as follows: When the lower boundary point of the fine-grained cluster is the minimum of all elements in the fine-grained cluster ; When the upper boundary point of a fine-grained cluster is the same as the lower boundary point of a fine-grained cluster , the formula is: where: represents the maximum value of all elements in a fine-grained cluster ; represents the minimum value of all elements in a fine-grained cluster ; When the upper boundary point of a fine-grained cluster is the maximum of all elements in the fine-grained cluster .

7. The base power transmission curve formulation method based on frequency analysis and multi-energy complementary simulation operation according to claim 6, characterized in that, Step S448 specifically comprises: determining a period a probability parameter in the period a power supply probability distribution function selecting a probability parameter determining a power supply characteristic value 8. The base power transmission curve formulation method based on frequency analysis and multi-energy complementary simulation operation according to claim 7, characterized in that, The selection probability parameter The determination of the power transmission power characteristic value is specifically: ①in a time period a power supply power probability distribution function corresponding to the power supply power probability distribution curve, the maximum probability of determining the peak point position and the maximum power supply power ; If then is the power characteristic value; iii. If then: Solving the following equation determines the amount of change : ; According to the variation amount Determining the probability parameter Corresponding power transmission power interval lower bound And upper bound , and then determine the power transmission power interval; Taking the median of the power transmission power interval as the power transmission power characteristic value.

Images