New energy and energy storage station capacity planning method based on hierarchical and phased source load matching

Abstract

The application provides a new energy and energy storage station capacity planning method based on hierarchical staged source-load matching, relates to the technical field of new energy and energy storage station capacity planning, and comprises the following steps: characterizing the whole-network source-load matching coefficient of the system level and the station level to obtain the whole-network source-load matching coefficient and the station source-load matching coefficient; characterizing the whole-network source-load matching degree of the system level and the station level based on the whole-network source-load matching coefficient and the station source-load matching coefficient to obtain the whole-network source-load matching degree and the station source-load matching degree; establishing an upper model based on the system-level source-load matching, wherein the upper model optimizes the wind-solar ratio by taking the total wind power capacity and the total photovoltaic capacity of the system as decision variables; establishing a lower model, wherein the lower model optimizes the access capacity of each node by taking the capacity of each new energy and energy storage station as a decision variable; and solving the upper model and the lower model to obtain a recommended planning scheme. The application has the advantage of improving the matching accuracy of new energy and energy storage station planning.

Description

Technical Field

[0001] This invention relates to the field of new energy and energy storage power plant capacity planning technology, and more specifically, to a new energy and energy storage power plant capacity planning method based on hierarchical and phased source-load matching. Background Technology

[0002] New energy and energy storage planning is an important part of the energy system.

[0003] Traditional planning methods aim for stable output from new energy sources and energy storage, ideally with a horizontal straight line as the output curve. However, this results in other power sources bearing the brunt of load fluctuation regulation. With the increasing peak load and wider peak-to-valley difference in new power systems, system regulation resources are becoming increasingly scarce, and maintaining power balance is becoming increasingly difficult, necessitating improvements in new energy and energy storage planning. Source-load matching establishes a two-way feedback mechanism based on traditional unidirectional power dispatch, utilizing the safe and efficient interaction between sources and loads to ensure the consumption of clean energy and reliable power supply. The fit between source and load curves is the core indicator for evaluating source-load matching. Existing methods can be divided into deviation evaluation and likelihood evaluation methods, but both are only applicable to system-level macro-level assessments. Furthermore, current technologies lack quantitative guidance for micro-level matching at the plant level, relying solely on system-level statistical indicators. This fails to quantify the spatiotemporal matching characteristics of new energy output and local load at the plant level, leading to investment redundancy or power shortage risks due to matching imbalances at critical nodes. Meanwhile, traditional planning takes a horizontal straight line as the ideal curve, ignoring the time-varying characteristics of load. The peak-shaving characteristics of new energy exacerbate the peak-valley difference in the system, resulting in high pressure on system regulation resources and limited new energy consumption.

[0004] Therefore, it is necessary to optimize the planning methods for new energy and energy storage to improve the accuracy of the planning for new energy and energy storage sites. Summary of the Invention

[0005] The purpose of this invention is to provide a capacity planning method for new energy and energy storage sites based on hierarchical and phased source-load matching, which can improve the accuracy of planning for new energy and energy storage sites.

[0006] This invention is achieved through the following technical solution:

[0007] The capacity planning method for new energy and energy storage sites based on hierarchical and phased source-load matching includes the following steps:

[0008] The source-load matching coefficients of the entire network at the system level and at the station level are characterized respectively to obtain the source-load matching coefficients of the entire network and the source-load matching coefficients of the station.

[0009] Based on the network-wide source-load matching coefficient and the station source-load matching coefficient, the network-wide source-load matching degree at the system level and the station level are characterized respectively, and the network-wide source-load matching degree and the station source-load matching degree are obtained.

[0010] Based on system-level source-load matching, an upper-level model is established, which optimizes the wind-solar power ratio using the total wind power capacity and total photovoltaic capacity of the system as decision variables.

[0011] Based on the source-load matching at the site level, a lower-level model is established. The lower-level model optimizes the access capacity of each node using the capacity of each new energy and energy storage site as the decision variable.

[0012] The recommended planning scheme is obtained by solving the upper-level model and the lower-level model.

[0013] Preferably, the method for obtaining the overall network source-load matching coefficient and the station source-load matching coefficient is as follows:

[0014] ;

[0015] ;

[0016] in, The source-load matching coefficient of the entire network is given. For the daily electricity consumption of new energy and energy storage across the entire network, Daily power consumption of the entire network For new energy or energy storage stations The aforementioned station source-load matching coefficient, For new energy or power station Daily electricity consumption.

[0017] Preferably, the method for obtaining the overall network source-load matching degree and the station source-load matching degree is as follows:

[0018] ;

[0019] ;

[0020] in, The total network source-load matching degree, Contribute to new energy and energy storage Matching load sequences for new energy and energy storage power outputs. Let be the joint distribution function of source and load. and They are respectively and The marginal distribution function, This is the total network load sequence;

[0021] ;

[0022] ;

[0023] in, For new energy or energy storage stations The aforementioned station source load matching degree, and They are either new energy or energy storage stations. The output power and its matching load sequence, for and The Pearson correlation coefficient.

[0024] Preferably, the method for establishing the upper-level model is as follows:

[0025] The first objective function is established with the goal of minimizing the annualized construction cost and simulated operation cost of new energy sources, and the second objective function is established with the goal of maximizing the source-load matching degree of the entire network.

[0026] Multiple upper-level model constraints are established, including resource endowment constraints, new energy operation constraints, upper-level model traditional power source operation constraints, upper-level model power grid operation constraints, new energy installed capacity penetration rate constraints, and electricity penetration rate constraints.

[0027] Preferably, the method for establishing the first objective function is as follows:

[0028] ;

[0029] in, and These represent the average annualized construction cost and simulated operating cost per unit wind power capacity within the planning system, respectively. For wind power planning capacity, and These represent the average annualized construction cost and simulated operating cost per unit photovoltaic capacity within the planned system, respectively. For photovoltaic planning capacity;

[0030] The method for establishing the second objective function is as follows:

[0031] ;

[0032] ;

[0033] ;

[0034] in, Contribute to new energy and energy storage Matching load sequences for new energy and energy storage power outputs. and These are the source output sequence and the load output sequence, respectively. To match the load sequence, and It is divided into wind power output coefficient and photovoltaic power output coefficient for time period t.

[0035] Preferably, the method for establishing multiple upper-level model constraints is as follows:

[0036] The resource endowment constraint is:

[0037] ;

[0038] ;

[0039] in, and These are the theoretical exploitable capacity of wind power and the theoretical exploitable capacity of photovoltaic power in the planned area, respectively.

[0040] The operating constraints of the new energy source are as follows:

[0041] ;

[0042] ;

[0043] in, and These are the theoretical maximum power output coefficients for wind power and photovoltaic power, respectively. and These are the wind power output coefficient and photovoltaic power output coefficient for time period t, respectively.

[0044] The traditional power supply operation constraints of the upper-level model are:

[0045] ;

[0046] ;

[0047] ;

[0048] in, For traditional power units The output during time period t, and Traditional power units Upper and lower limits of output and The units The upper limit of the ramp rate, and The units During the period Continuous running time and continuous downtime and These are the minimum running time and the minimum downtime, respectively. This is a sign function, with a value of 0 or 1, representing the unit. The on / off status;

[0049] The power grid operation constraints of the upper-level model are:

[0050] ;

[0051] in, for Network load during the specified time period;

[0052] The constraint on the penetration rate of new energy installations is:

[0053] ;

[0054] in, To achieve the target of increasing the penetration rate of new energy installations, For traditional power units The installed capacity;

[0055] The electricity penetration rate constraint is:

[0056] ;

[0057] in, To achieve the target of renewable energy power penetration rate For traditional power units During the period of effort.

[0058] Preferably, the method for establishing the lower-level model is as follows:

[0059] A third objective function is established with the goal of minimizing the annualized construction cost and simulated operation cost of new energy and energy storage, and a fourth objective function is established with the goal of maximizing the cumulative source-load matching degree of new energy and energy storage stations.

[0060] Multiple lower-level model constraints are established, including constraints on the operation of new energy power plants, constraints on the operation of energy storage power plants, constraints on the operation of traditional power sources in the lower-level model, constraints on the operation of the power grid in the lower-level model, and constraints on the wind and solar power ratio.

[0061] Preferably, the method for establishing the third objective function is as follows:

[0062] ;

[0063] ;

[0064] ;

[0065] in, and These represent the annualized construction cost and simulated operating cost of new energy sources and energy storage within the system, respectively. , and Wind farm Photovoltaic power stations and energy storage power station The annualized investment and construction cost per unit capacity, , and Wind farm Photovoltaic power stations and energy storage power station Investment and construction capacity, , and Wind farm Photovoltaic power stations and energy storage power station The unit capacity simulated operating cost, , and Wind farm Photovoltaic power stations and energy storage power station During the period Operating capacity;

[0066] The method for establishing the fourth objective function is as follows:

[0067] ;

[0068] ;

[0069] ;

[0070] in, and They are either new energy or energy storage stations. The output sequence and the matching load sequence, For station In time period The output coefficient, and They are respectively and Mid-term The value, For station Operating capacity for Network load during the time period The source-load matching coefficient for the entire network. For new energy or energy storage stations The aforementioned station source-load matching coefficient, This is for the matching degree of the entire network source and load.

[0071] Preferably, the method for establishing multiple lower-level model constraints is as follows:

[0072] The operational constraints for the new energy power stations are as follows:

[0073] ;

[0074] ;

[0075] In the formula: and Wind farm and photovoltaic power station In time period The output coefficient, , Wind farm and photovoltaic power station In time period The upper limit of output;

[0076] The operating constraints of the energy storage power station are:

[0077] ;

[0078] ;

[0079] ;

[0080] ;

[0081] in, and Energy storage power stations During the period The charging and discharging power, For energy storage power stations During the period The charge capacity, and These represent energy storage power stations 0-1 variables in the charging and discharging states, and For energy storage power stations The charge and discharge efficiency, The number of hours in the time period; and Energy storage power stations The upper and lower limits of the charge capacity;

[0082] The conventional power supply operation constraints of the lower-level model are:

[0083] ;

[0084] ;

[0085] ;

[0086] in, For traditional power units During the period of efforts, and Traditional power units Upper and lower limits of output and The units The upper limit of the ramp rate, and The units During the period Continuous running time and continuous downtime and These are the minimum running time and the minimum downtime, respectively. This is a sign function, with a value of 0 or 1, representing the unit. The on / off status;

[0087] The lower-level model power grid operation constraints are:

[0088] ;

[0089] ;

[0090] ;

[0091] in, and They represent the transmission sections respectively. The upper and lower limits of transmission power, The power transfer distribution factor, For system reserve rate, This represents the system's historical maximum load.

[0092] The wind-solar ratio constraint is as follows:

[0093] .

[0094] Preferably, the method for solving the upper-level model and the lower-level model is as follows:

[0095] Construct multiple scenarios with guaranteed rate renewable energy output: Based on historical renewable energy output data, select the output processes at guaranteed rates of 95%, 75%, 50%, 25%, and 5% in the daily power distribution of wind power and photovoltaic power, respectively as scenarios with minimal, relatively small, moderate, relatively large, and maximum renewable energy output.

[0096] For multiple scenarios, source-load matching optimization based on the upper-level model and the lower-level model is performed sequentially: the second-generation non-dominated sorting genetic algorithm is used to solve the Pareto solution set, and then the scheme is compared and selected to obtain the new energy and energy storage planning solution sets corresponding to new energy output scenarios with different guarantee rates.

[0097] Operational simulations and result evaluations are conducted on each planning solution in the new energy and energy storage planning solution set. The recommended planning scheme is output by balancing the guarantee rate and the objective function.

[0098] The technical solution of the present invention has at least the following advantages and beneficial effects:

[0099] This invention achieves quantitative guidance on source-load matching from macro to micro levels by characterizing source-load matching coefficients and source-load matching degree at the subsystem and site levels, thereby improving the accuracy of planning for new energy and energy storage sites.

[0100] The upper-level model of this invention optimizes the total wind and solar power allocation and outputs constraints to the lower level. The lower-level model uses the capacity of new energy and energy storage stations as decision variables to achieve accurate planning of the capacity of each station.

[0101] This invention uses "source follows load" instead of "output horizontal linear" as the optimization objective, so that new energy and energy storage actively match the load fluctuation characteristics, reduce the regulation burden of other power sources, and alleviate the system balance pressure of increasing peak-valley difference;

[0102] This invention combines construction costs and simulated operating costs to avoid the risk of building but not using the project. It also ensures the robustness of the planning scheme under volatility by using wind and solar power ratio constraints and multi-scenario Pareto solution set selection. Attached Figure Description

[0103] Figure 1 A flowchart illustrating the capacity planning method for new energy and energy storage stations based on hierarchical and phased source-load matching provided in an embodiment of the present invention;

[0104] Figure 2 A schematic diagram illustrating the principle of using source-load matching degree and source-load matching coefficient to represent the embodiment of the present invention;

[0105] Figure 3 This is a schematic diagram of the process for obtaining the recommended planning scheme according to an embodiment of the present invention. Detailed Implementation

[0106] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0107] Example

[0108] This embodiment provides a capacity planning method for new energy and energy storage sites based on hierarchical and phased source-load matching. (See also...) Figure 1 This includes the following steps:

[0109] Step S1: Characterize the source-load matching coefficients of the entire network at the system level and the station level respectively to obtain the source-load matching coefficients of the entire network and the source-load matching coefficients of the station.

[0110] In this embodiment, the ratio of daily electricity generation from new energy sources and energy storage to the daily electricity generation of the entire grid load is defined as the source-load matching coefficient. Using this coefficient to scale the load curve eliminates the difference in magnitude between source and load, facilitating a more refined characterization of the source-load relationship. In this embodiment, the methods for obtaining the entire grid source-load matching coefficient and the power station source-load matching coefficient are as follows:

[0111] ;

[0112] ;

[0113] in, The source-load matching coefficient of the entire network is given. For the daily electricity consumption of new energy and energy storage across the entire network, Daily power consumption of the entire network For new energy or energy storage stations The aforementioned station source-load matching coefficient, For new energy or power station Daily electricity consumption.

[0114] It should be noted that the total daily electricity generation of new energy sources in the entire network is the sum of the daily electricity generation of all new energy sources and energy storage power stations. Therefore, the relationship between the total network source-load matching coefficient and the power station source-load matching coefficient is as follows:

[0115] .

[0116] Step S2: Based on the overall network source-load matching coefficient and the station source-load matching coefficient, characterize the overall network source-load matching degree at the system level and the station level respectively to obtain the overall network source-load matching degree and the station source-load matching degree.

[0117] Based on the source-load matching coefficient, the degree of closeness between the output curve of new energy sources or energy storage and the matching load curve is defined as the source-load matching degree. Mutual information (MI) is used as the characterization index to calculate the source-load matching degree. Therefore, the preferred method for obtaining the source-load matching degree of the entire network and the source-load matching degree of the power station is as follows:

[0118] ;

[0119] ;

[0120] in, The total network source-load matching degree, Contribute to new energy and energy storage Matching load sequences for new energy and energy storage power outputs. Let be the joint distribution function of source and load. and They are respectively and The marginal distribution function, This is the total network load sequence;

[0121] Assuming that the source and load follow a Gaussian distribution, the formula for calculating the source-load matching degree of the entire network is:

[0122] ;

[0123] ;

[0124] ;

[0125] in, For new energy or energy storage stations The aforementioned station source load matching degree, and They are either new energy or energy storage stations. The output power and its matching load sequence, for and The Pearson correlation coefficient.

[0126] Due to the station Matching load It's just a matter of matching the load across the entire network. Part of the source-load matching degree of the station Matching degree with the entire network source and load The relationship between them exhibits non-linear characteristics. Considering that increasing the source-load matching degree of a power station inevitably leads to an increase in the source-load matching degree information of the entire network, with specific information contributing significantly, the cumulative source-load matching degree information from the power station to the entire network is weighted and simplified using the power station source-load matching coefficient:

[0127] .

[0128] This embodiment proposes two indicators, source-load matching coefficient and source-load matching degree, to quantitatively characterize the source-load relationship at the system level and the site level in two stages. The degree of closeness between the output of new energy sources and energy storage and the system load is also divided into two stages, which can be referred to... Figure 2 The source-load matching degree and source-load matching coefficient are used to characterize the matching process, respectively. The source-load matching coefficient mainly eliminates the difference in magnitude, while the source-load matching degree provides a precise characterization. The larger the value, the closer the power output process on the source side is to the load process. This two-stage matching process is applicable to both system-level and station-level source-load matching. The overall network source-load matching coefficient is equal to the sum of the station source-load matching coefficients, and the overall network source-load matching degree is calculated using a weighted simplification of the station source-load matching degrees. In other words, this embodiment proposes a hierarchical and phased source-load matching characterization method to quantitatively characterize the source-load relationship at the system-level and station-level. When establishing the characterization in steps S1-S2, the first stage uses the ratio of daily power generation to load to establish the source-load matching coefficient, eliminating the difference in magnitude between source and load. The second stage uses the mutual information between source and load to establish the source-load matching degree, accurately characterizing the degree of similarity between source and load curves. The source-load relationship is also characterized at the system level and the station level. The source-load matching coefficient of the entire network is equal to the sum of the source-load matching coefficients of the stations, and the source-load matching degree of the entire network is equal to the weighted sum of the source-load matching degrees of the stations. The system level and the station level can be evaluated in a coordinated manner.

[0129] Step S3: Based on system-level source-load matching, establish an upper-level model. The upper-level model optimizes the wind-solar power ratio using the total wind power capacity and total photovoltaic capacity of the system as decision variables.

[0130] Based on the source-load matching at the site level, a lower-level model is established. The lower-level model optimizes the access capacity of each node using the capacity of each new energy and energy storage site as the decision variable.

[0131] This embodiment improves upon the objective function, constraints, and model framework. Regarding the objective function, in addition to the annualized construction cost, the simulated operating cost is also considered for the economic objective, to avoid planned power sources being built but not used due to high operating costs. The output reliability objective uses the maximum source-load matching degree as the objective function to achieve a "source follows load" matching effect. Regarding constraints, a wind-solar ratio constraint is added, using the wind-solar ratio that maximizes the natural complementarity of wind and solar as a limiting condition for new energy and energy storage planning. Regarding the model framework, optimization is performed in two layers. The upper layer uses the total wind power capacity and total photovoltaic capacity as decision variables to perform system-level source-load matching optimization, obtaining the optimal wind-solar ratio as one of the constraints of the lower-level model. The lower layer uses the capacity of each new energy and energy storage site as decision variables to perform site-level source-load matching optimization, obtaining the capacity output of each site as the final planning scheme.

[0132] On the one hand, the upper-level model is geared towards total new energy planning, using the total system wind power capacity and total photovoltaic capacity as decision variables, considering various constraints, and optimizing the wind-solar power ratio. The method for establishing the upper-level model is as follows:

[0133] The first objective function is established with the goal of minimizing the annualized construction cost and simulated operation cost of new energy sources, and the second objective function is established with the goal of maximizing the source-load matching degree of the entire network.

[0134] Multiple upper-level model constraints are established, including resource endowment constraints, new energy operation constraints, upper-level model traditional power source operation constraints, upper-level model power grid operation constraints, new energy installed capacity penetration rate constraints, and electricity penetration rate constraints.

[0135] Specifically, the method for establishing the first objective function is as follows:

[0136] ;

[0137] in, and These represent the average annualized construction cost and simulated operating cost per unit wind power capacity within the planning system, respectively. For wind power planning capacity, and These represent the average annualized construction cost and simulated operating cost per unit photovoltaic capacity within the planned system, respectively. For photovoltaic planning capacity;

[0138] The method for establishing the second objective function is as follows:

[0139] ;

[0140] ;

[0141] ;

[0142] in, Contribute to new energy and energy storage Matching load sequences for new energy and energy storage power outputs. and These are the source output sequence and the load output sequence, respectively. To match the load sequence, and Divided into time periods The wind power output coefficient and the photovoltaic power output coefficient.

[0143] Then, the method for establishing multiple upper-level model constraints is as follows:

[0144] The resource endowment constraint is:

[0145] ;

[0146] ;

[0147] in, and These are the theoretical exploitable capacity of wind power and the theoretical exploitable capacity of photovoltaic power in the planned area, respectively.

[0148] The operating constraints of the new energy source are as follows:

[0149] ;

[0150] ;

[0151] in, and These are the theoretical maximum power output coefficients for wind power and photovoltaic power, respectively. and These are the wind power output coefficient and photovoltaic power output coefficient for time period t, respectively.

[0152] This embodiment assigns consecutive numbers to traditional power generating units and assumes that the units operate with similar characteristics. It primarily considers constraints on the upper and lower limits of unit output, ramp-up constraints, and continuous start-up and shutdown duration constraints. Therefore, the constraints for the traditional power generation operation in the upper-level model are:

[0153] ;

[0154] ;

[0155] ;

[0156] in, For traditional power units During the period of efforts, and Traditional power units Upper and lower limits of output and The units The upper limit of the ramp rate, and The units During the period Continuous running time and continuous downtime and These are the minimum running time and the minimum downtime, respectively. This is a sign function, with a value of 0 or 1, representing the unit. The on / off status;

[0157] The power grid operation constraints of the upper-level model are:

[0158] ;

[0159] in, For time period Overall network load;

[0160] The constraint on the penetration rate of new energy installations is:

[0161] ;

[0162] in, To achieve the target of increasing the penetration rate of new energy installations, For traditional power units The installed capacity;

[0163] The electricity penetration rate constraint is:

[0164] ;

[0165] in, To achieve the target of increasing the penetration rate of new energy electricity, For traditional power units During the period of effort.

[0166] On the other hand, the method for establishing the lower-level model is as follows:

[0167] A third objective function is established with the goal of minimizing the annualized construction cost and simulated operation cost of new energy and energy storage, and a fourth objective function is established with the goal of maximizing the cumulative source-load matching degree of new energy and energy storage sites.

[0168] The lower-level model primarily focuses on precise or incremental planning for new energy and energy storage. Using the capacity of each new energy and energy storage facility as the decision variable, and considering wind-solar ratio constraints and other constraints, it optimizes the access capacity at each node to form a planning scheme. Multiple constraints are established for the lower-level model, including operational constraints for new energy facilities, operational constraints for energy storage power stations, operational constraints for traditional power sources, operational constraints for the power grid, and wind-solar ratio constraints.

[0169] As a preferred embodiment, the method for establishing the third objective function is as follows:

[0170] ;

[0171] ;

[0172] ;

[0173] in, and These represent the annualized construction cost and simulated operating cost of new energy sources and energy storage within the system, respectively. , and Wind farm Photovoltaic power stations and energy storage power station The annualized investment and construction cost per unit capacity, , and Wind farm Photovoltaic power stations and energy storage power station Investment and construction capacity, , and Wind farm Photovoltaic power stations and energy storage power station The unit capacity simulated operating cost, , and Wind farm Photovoltaic power stations and energy storage power station During the period Operating capacity;

[0174] The method for establishing the fourth objective function is as follows:

[0175] ;

[0176] ;

[0177] ;

[0178] in, and They are either new energy or energy storage stations. The output sequence and the matching load sequence, For station In time period The output coefficient, and They are respectively and The value of time period t in the middle. For station Operating capacity For time period Overall network load The source-load matching coefficient for the entire network. For new energy or energy storage stations The aforementioned station source-load matching coefficient, This is for the matching degree of the entire network source and load.

[0179] Finally, the method for establishing multiple lower-level model constraints is as follows:

[0180] The operational constraints for the new energy power stations are as follows:

[0181] ;

[0182] ;

[0183] In the formula: and Wind farm and photovoltaic power station In time period The output coefficient, , Wind farm and photovoltaic power station In time period The upper limit of output;

[0184] The operational constraints of the energy storage power station mainly include energy storage charging and discharging power constraints, state of charge constraints, charge quantity constraints, and mutual exclusion constraints of operating states.

[0185] ;

[0186] ;

[0187] ;

[0188] ;

[0189] in, and Energy storage power stations During the period The charging and discharging power, For energy storage power stations During the period The charge capacity, and These represent energy storage power stations 0-1 variables in the charging and discharging states, and For energy storage power stations The charge and discharge efficiency, The number of hours in the time period; and Energy storage power stations The upper and lower limits of the charge capacity;

[0190] The conventional power supply operation constraints of the lower-level model are:

[0191] ;

[0192] ;

[0193] ;

[0194] in, For traditional power units During the period of efforts, and Traditional power units Upper and lower limits of output and The units The upper limit of the ramp rate, and The units During the period Continuous running time and continuous downtime and These are the minimum running time and the minimum downtime, respectively. This is a sign function, with a value of 0 or 1, representing the unit. The on / off status;

[0195] The lower-level model power grid operation constraints are:

[0196] ;

[0197] ;

[0198] ;

[0199] in, and They represent the transmission sections respectively. The upper and lower limits of transmission power, The power transfer distribution factor, For system reserve rate, This represents the system's historical maximum load.

[0200] The ratio of total wind power to total photovoltaic capacity, obtained from the aforementioned system-level source-load matching optimization to maximize the utilization of the natural complementarity of wind and solar power, serves as a restrictive condition for the planning of new energy power plants in the source-load matching optimization of power plants. Therefore, the wind-solar ratio constraint is:

[0201] .

[0202] Step S4: Solve the upper-level model and the lower-level model to obtain the recommended planning scheme.

[0203] As a preferred embodiment, the method for solving the upper-level model and the lower-level model is as follows:

[0204] Construct multiple scenarios with guaranteed rate renewable energy output: Based on historical renewable energy output data, select the output processes at guaranteed rates of 95%, 75%, 50%, 25%, and 5% in the daily power distribution of wind power and photovoltaic power, respectively as scenarios with minimal, relatively small, moderate, relatively large, and maximum renewable energy output.

[0205] For multiple scenarios, source-load matching optimization based on the upper-level model and the lower-level model is performed sequentially: the second-generation non-dominated sorting genetic algorithm (NSGA-II) is used to solve the problem, and after obtaining the Pareto solution set, the scheme is compared and selected to obtain the new energy and energy storage planning solution sets corresponding to new energy output scenarios with different guarantee rates.

[0206] Operational simulations and result evaluations are conducted on each planning solution in the new energy and energy storage planning solution set. The recommended planning scheme is output by balancing the guarantee rate and the objective function.

[0207] A complete solution method example can be found in [reference]. Figure 3 .

[0208] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A capacity planning method for new energy and energy storage power stations based on hierarchical and phased source-load matching, characterized in that, Includes the following steps: The source-load matching coefficients of the entire network at the system level and at the station level are characterized respectively to obtain the source-load matching coefficients of the entire network and the source-load matching coefficients of the station. Based on the network-wide source-load matching coefficient and the station source-load matching coefficient, the network-wide source-load matching degree at the system level and the station level are characterized respectively, and the network-wide source-load matching degree and the station source-load matching degree are obtained. Based on system-level source-load matching, an upper-level model is established, which optimizes the wind-solar power ratio using the total wind power capacity and total photovoltaic capacity of the system as decision variables. Based on the source-load matching at the site level, a lower-level model is established. The lower-level model optimizes the access capacity of each node using the capacity of each new energy and energy storage site as the decision variable. The recommended planning scheme is obtained by solving the upper-level model and the lower-level model; The methods for obtaining the overall network source-load matching coefficient and the station source-load matching coefficient are as follows: ； ； in, The source-load matching coefficient of the entire network is given. For the daily electricity consumption of new energy and energy storage across the entire network, Daily power consumption of the entire network For new energy or energy storage stations The aforementioned station source-load matching coefficient, For new energy or energy storage stations Daily electricity consumption; The methods for obtaining the overall network source-load matching degree and the station source-load matching degree are as follows: ； ； in, The total network source-load matching degree, Contribute to new energy and energy storage Matching load sequences for new energy and energy storage power outputs. Let be the joint distribution function of source and load. and They are respectively and The marginal distribution function, This is the total network load sequence; ； ； in, For new energy or energy storage stations The aforementioned station source load matching degree, and They are either new energy or energy storage stations. The output power and its matching load sequence, for and The Pearson correlation coefficient; The method for establishing the upper-level model is as follows: The first objective function is established with the goal of minimizing the annualized construction cost and simulated operation cost of new energy sources, and the second objective function is established with the goal of maximizing the source-load matching degree of the entire network. Establish various upper-level model constraints, including resource endowment constraints, new energy operation constraints, upper-level model traditional power source operation constraints, upper-level model power grid operation constraints, new energy installed capacity penetration rate constraints, and electricity penetration rate constraints; The method for establishing the lower-level model is as follows: A third objective function is established with the goal of minimizing the annualized construction cost and simulated operation cost of new energy and energy storage, and a fourth objective function is established with the goal of maximizing the cumulative source-load matching degree of new energy and energy storage stations. Multiple lower-level model constraints are established, including constraints on the operation of new energy power plants, constraints on the operation of energy storage power plants, constraints on the operation of traditional power sources in the lower-level model, constraints on the operation of the power grid in the lower-level model, and constraints on the wind and solar power ratio.

2. The capacity planning method for new energy and energy storage stations based on hierarchical and phased source-load matching according to claim 1, characterized in that, The method for establishing the first objective function is as follows: ； in, and These represent the average annualized construction cost and simulated operating cost per unit wind power capacity within the planning system, respectively. For wind power planning capacity, and These represent the average annualized construction cost and simulated operating cost per unit photovoltaic capacity within the planned system, respectively. For photovoltaic planning capacity; The method for establishing the second objective function is as follows: ； ； ； in, Contribute to new energy and energy storage Matching load sequences for new energy and energy storage power outputs. and These are the source output sequence and the load output sequence, respectively. To match the load sequence, and Divided into time periods The wind power output coefficient and the photovoltaic power output coefficient.

3. The capacity planning method for new energy and energy storage stations based on hierarchical and phased source-load matching according to claim 2, characterized in that, The method for establishing multiple upper-level model constraints is as follows: The resource endowment constraint is: ； ； in, and These are the theoretical exploitable capacity of wind power and the theoretical exploitable capacity of photovoltaic power in the planned area, respectively. The operating constraints of the new energy source are as follows: ； ； in, and These are the theoretical maximum power output coefficients for wind power and photovoltaic power, respectively. and Time periods The wind power output coefficient and the photovoltaic power output coefficient; The traditional power supply operation constraints of the upper-level model are: ； ； ； in, For traditional power units During the period of effort, and Traditional power units Upper and lower limits of output and The units The upper limit of the ramp rate, and The units During the period Continuous running time and continuous downtime and These are the minimum running time and the minimum downtime, respectively. This is a symbolic function with a value of 0 or 1, representing the unit. The on / off status; The power grid operation constraints of the upper-level model are: ； in, This is the total network load sequence; The constraint on the penetration rate of new energy installations is: ； in, To achieve the target of increasing the penetration rate of new energy installations, For traditional power units The installed capacity; The electricity penetration rate constraint is: ； in, To achieve the target of increasing the penetration rate of new energy electricity, For traditional power units During the period of effort.

4. The capacity planning method for new energy and energy storage power stations based on hierarchical and phased source-load matching according to claim 1, characterized in that, The method for establishing the third objective function is as follows: ； ； ； in, and These represent the annualized construction cost and simulated operating cost of new energy sources and energy storage within the system, respectively. , and Wind farm Photovoltaic power stations and energy storage power station The annualized investment and construction cost per unit capacity, , and Wind farm Photovoltaic power stations and energy storage power station Investment and construction capacity, , and Wind farm Photovoltaic power stations and energy storage power station The unit capacity simulated operating cost, , and Wind farm Photovoltaic power stations and energy storage power station During the period Operating capacity; The method for establishing the fourth objective function is as follows: ； ； ； in, and They are either new energy or energy storage stations. The output sequence and the matching load sequence, For station The output coefficient during time period t, and They are respectively and The value of time period t in the middle. For station Operating capacity This is the total network load sequence. The source-load matching coefficient for the entire network. For new energy or energy storage stations The aforementioned station source-load matching coefficient, For the matching degree of source and load across the entire network.

5. The capacity planning method for new energy and energy storage stations based on hierarchical and phased source-load matching according to claim 4, characterized in that, The method for establishing multiple lower-level model constraints is as follows: The operational constraints for the new energy power stations are as follows: ； ； In the formula: and Wind farm and photovoltaic power station In time period The output coefficient, , Wind farm and photovoltaic power station In time period The upper limit of output; The operating constraints of the energy storage power station are: ； ； ； ； in, and Energy storage power stations During the period The charging and discharging power, For energy storage power stations During the period The charge capacity, and These represent energy storage power stations 0-1 variables in the charging and discharging states, and For energy storage power stations The charge and discharge efficiency, The number of hours in the time period; and Energy storage power stations The upper and lower limits of the charge capacity; The conventional power supply operation constraints of the lower-level model are: ； ； ； in, For traditional power units The output during time period t, and Traditional power units Upper and lower limits of output and The units The upper limit of the ramp rate, and The units During the period Continuous running time and continuous downtime and These are the minimum running time and the minimum downtime, respectively. This is a symbolic function with a value of 0 or 1, representing the unit. The on / off status; The lower-level model power grid operation constraints are: ； ； ； in, and They represent the transmission sections respectively. The upper and lower limits of transmission power, The power transfer distribution factor, For system reserve rate, This represents the system's historical maximum load. The wind-solar ratio constraint is as follows: 。 6. The capacity planning method for new energy and energy storage stations based on hierarchical and phased source-load matching according to claim 1, characterized in that, The method for solving the upper-level model and the lower-level model is as follows: Construct multiple scenarios with guaranteed rate renewable energy output: Based on historical renewable energy output data, select the output processes at guaranteed rates of 95%, 75%, 50%, 25%, and 5% in the daily power distribution of wind power and photovoltaic power, respectively as scenarios with minimal, relatively small, moderate, relatively large, and maximum renewable energy output. For multiple scenarios, source-load matching optimization based on the upper-level model and the lower-level model is performed sequentially: the second-generation non-dominated sorting genetic algorithm is used to solve the Pareto solution set, and then the scheme is compared and selected to obtain the new energy and energy storage planning solution sets corresponding to new energy output scenarios with different guarantee rates. Operational simulations and result evaluations are conducted on each planning solution in the new energy and energy storage planning solution set. The recommended planning scheme is output by balancing the guarantee rate and the objective function.

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