A watershed water and scenery base matching storage gain evaluation method

Abstract

The present application belongs to the field of integrated watershed water and landscape base planning, and relates to a watershed water and landscape base storage allocation gain evaluation method. First, considering the investment and operation cost of energy storage and new energy, the annual power generation yield and the influence of discount rate, the base storage allocation gain index is proposed. Second, a watershed water and landscape storage integrated planning model embedded with 8760h complementary operation simulation is established. The general storage constraint is designed, the technical characteristics of different types of storage are represented by parameter differentiation, and the high-efficiency solution of the planning model is realized by convex linearization technology. The watershed new energy access capacity and 8760h operation simulation process before and after the configuration of different types of storage are calculated. The gain effect of different storage allocation schemes is quantitatively compared by the proposed storage allocation gain evaluation index to determine the optimal storage allocation scheme. The present application can effectively reduce the solution scale of the model while ensuring the accuracy and improving the solution speed through the nested dimension reduction and convex linearization modeling technology.

Description

Technical Field

[0001] This invention belongs to the field of integrated planning of watershed water, wind and solar power bases, and relates to a method for evaluating the storage gain of watershed water, wind and solar power bases. Background Technology

[0002] Integrated hydropower-wind-solar power development in river basins, with cascade hydropower regulation capacity and its transmission channels as key supports, leverages the abundant wind and solar resources surrounding the basin, the seasonal complementarity of hydropower, wind and solar power, hydropower transmission channels, and the flexible regulation capacity of cascade hydropower. This can effectively improve the development and utilization rate of renewable energy resources and the level of new energy consumption in the basin. However, with the continuous large-scale grid connection of new energy sources and the increasing demands of the power grid on the regulation capacity of cascade hydropower, the integrated development and operation of river basins is facing, or will soon face, the challenge of abundant new energy resources but insufficient hydropower regulation capacity. Developing energy storage configuration schemes based on the basin's resource endowment to achieve coordinated regulation of water and storage is a key technical factor in improving the new energy configuration level of integrated hydropower-wind-solar power bases. However, different types of energy storage have significant differences in regulation capacity and investment costs. How to scientifically plan the configuration of energy storage types and proportions in integrated hydropower-wind-solar power bases to maximize the benefits of energy storage and distribution is an urgent problem to be solved.

[0003] From the perspective of energy storage configuration planning for integrated hydropower, wind power, and solar power bases:

[0004] Reference 1 (Peng Yumin, Wang Xuelin, Liu Dexu, et al. Comparative Study on Dispatch and Operation of Water-Wind-Solar-Storage Complementary Systems Considering Mixed and Pure Pumped Storage Configurations [J]. Power System Protection and Control, 2024, 52(10):179-187.DOI:10.19783 / j.cnki.pspc.231085.) considers both mixed and pure pumped storage forms. Combining the seasonal and intraday complementarity of water, wind, and solar resources, it compares the differences between mixed and pure pumped storage systems in the cascade water-wind-solar-storage complementary systems before and after adding pumped storage, focusing on the differences in dispatch and operation. While this study explores the impact of pumped storage on the operation of the complementary system, it lacks a comparative analysis of the benefits of water-wind-solar-storage complementary systems with different energy storage capacities and does not demonstrate an analytical method for determining the optimal energy storage capacity of the system.

[0005] Reference 2 (Ma Chao, Liu Lu, Lian Jijian, et al. Research on capacity optimization configuration of hybrid pumped storage-wind-solar multi-energy complementary system based on comprehensive evaluation [J]. Journal of Hydraulic Engineering, 2025, 56(06):726-738.DOI:10.13243 / j.cnki.slxb.20240694) proposes a capacity optimization configuration method for hybrid pumped storage-wind-solar multi-energy complementary system based on comprehensive evaluation of the entire life cycle by coupling a new energy output scenario set considering uncertainty, a short-term collaborative operation optimization model, and a techno-economic evaluation model. Taking the Erduo hybrid pumped storage-wind-solar multi-energy complementary system in the upper reaches of the Yellow River as an example, it gives a recommended pumped storage installed capacity. Although this reference gives the annual increase in power generation revenue, it ignores the investment and operation and maintenance costs of new energy storage and new energy construction. Furthermore, this study only focuses on the application of one energy storage technology (hybrid pumped storage) and does not systematically compare and analyze the energy storage gain effect of different types and storage durations of energy storage technologies in the integrated watershed hydro-wind-solar base. Summary of the Invention

[0006] To address the aforementioned issues, this invention proposes a method for evaluating the energy storage gain of integrated watershed hydro-wind-solar bases. Using multiple watershed bases in Southwest China as examples, it quantitatively analyzes the specific contributions of different energy storage technologies to improving the overall benefits of these bases, thereby selecting the most reasonable energy storage configuration scheme.

[0007] Technical solution of the present invention:

[0008] A method for evaluating the storage gain of a watershed hydro-wind-solar base includes the following steps:

[0009] Step 1: Initial data collection;

[0010] Runoff data: Collect runoff data from the watershed and calculate the multi-year average daily runoff data. Compile basic data for hydropower stations within the watershed, including water level operating boundaries, maximum and minimum outflow, maximum power generation flow, guaranteed output, installed capacity, regulation performance, water level-reservoir capacity relationship curves, tailrace level-discharge flow curves, and output-head-power generation flow curves.

[0011] New energy power plant output data: Based on the historical output of new energy power plants already in operation in the integrated water, wind, and solar power base of the basin, the unit installed capacity output sequence of new energy power plants is calculated in hourly increments. The unit installed capacity cost and annual operation and maintenance cost of wind power and photovoltaic power are collected to determine the operating period.

[0012] Electricity price data: Settlement price of electricity generated in the river basin.

[0013] Step 2: Propose the storage gain index Y;

[0014] The energy storage-allocation gain index is defined as the ratio of the increase in power generation revenue over the entire life cycle to the increase in system cost after energy storage is configured in a watershed integrated hydro-wind-solar base. A higher value indicates a more significant gain effect from energy storage. The mathematical expression is as follows:

[0015]

[0016] In the formula, and These represent the annual electricity consumption at the base before and after energy storage allocation, in MWh. The average electricity price is expressed in yuan / MWh. The figure represents the increase in the annualized cost of new energy after the allocation of storage facilities compared to before the allocation, in yuan. The annualized cost of the energy storage power station is [amount in yuan].

[0017] The proposed indicators in this method consider the comprehensive benefits throughout the entire system's lifecycle. However, considering the differences in lifecycles among different energy storage systems, this method further employs the annualized method, combining the lifecycle cost and benefit indicators of new energy and different types of energy storage with the discount rate and their respective operating years for annualization. When calculating the annualized costs of energy storage and new energy power plants, the initial investment cost and subsequent costs such as operation and maintenance are comprehensively considered. The mathematical expression is as follows:

[0018]

[0019] In the formula, and These represent the annualized investment cost and annualized operation and maintenance cost of the energy storage power station, respectively, in yuan; and These represent the annualized investment cost and annualized operation and maintenance cost of the new energy power plant, respectively, in yuan. The calculation formulas for the above costs are as follows:

[0020]

[0021]

[0022] In the formula, The installed capacity of energy storage is expressed in MW. Energy storage capacity, MWh; The unit power cost of energy storage is expressed in yuan / MW; The unit capacity cost of energy storage is expressed in yuan / MWh; The percentage is the operation and maintenance cost coefficient for energy storage, %. and These represent the installed capacity of photovoltaic and wind power, respectively, in MW; and The unit installed costs for photovoltaic and wind power are respectively, in yuan / MW; and The figures are the annualized operation and maintenance costs for photovoltaic and wind power, respectively, in yuan / MW; and These are the annual value coefficients for energy storage and new energy power plants, respectively. The discount rate is %; and These represent the operating periods, in years, for energy storage and new energy power plants, respectively.

[0023] Step 3: Construct an 8760h integrated planning model for water, wind, solar, and energy storage;

[0024] Step 3.1: Assume that all power sources within the system are uniformly transmitted through transmission channels, and take maximizing the amount of electricity absorbed by the base as the objective function. The mathematical expression is as follows:

[0025]

[0026] In the formula, for day The combined power of water, wind, and solar energy storage ultimately contributes to the power output, in MW. For daily index, A set of daily indices for one year. ; For daily hourly indexes, For a daily hourly index set, .

[0027] Step 3.2: Establish the operational constraints for cascade hydropower. The mathematical expression for the water balance constraint is as follows:

[0028]

[0029] In the formula, For power station indexing; For the power station index set, ; For hydroelectric power station reservoir day Storage capacity at the end of the period, 10,000 m³ 3 ; For hydroelectric power station exist day Natural inflow runoff at that time, m 3 / s; For hydroelectric power station reservoir day Total outbound flow rate at time, m 3 / s; For hydroelectric power station exist day Power generation flow rate at time, m 3 / s, For hydroelectric power station exist day The discharge flow rate at that time, m 3 / s.

[0030] The mathematical expression for water level operation constraints is as follows:

[0031]

[0032] In the formula, For hydroelectric power station exist day Water level at the end of the time period, in meters; and Hydropower stations exist day The lower and upper boundaries of the water level at that time, in meters, comprehensively consider flood control needs, dead water level and flood limit water level restrictions; and Hydropower stations The initial and final water levels during the scheduling period, in meters; and Hydropower stations The water level values ​​for the initial and final scheduling periods are m.

[0033] The mathematical expressions for the upper and lower limits of flow are as follows:

[0034]

[0035] In the formula, and Power plants exist day The lower limit of outflow and power generation flow at that time, m 3 / s, the determination of the lower limit of outflow rate takes into account the comprehensive water demand at different times, including water supply, ecology, shipping, etc.; and Power plants exist day The upper limit of outflow and power generation flow at that time, m 3 / s.

[0036] The upper and lower limits of output constraints are expressed mathematically as follows:

[0037]

[0038] In the formula, For hydroelectric power station exist day Average output per hour, MW; For hydroelectric power station exist day The lower limit of power output at that time, MW; For hydroelectric power station exist day The maximum output at any given time, in MW.

[0039] The mathematical expression for the hydroelectric power generation function is as follows:

[0040]

[0041] In the formula, For hydroelectric power station The output-head-power generation flow rate curve function; For hydroelectric power station exist day The water head at that time, m.

[0042] The constraint of the hydroelectric characteristic curve is expressed mathematically as follows:

[0043]

[0044] In the formula, For hydroelectric power station Water level-reservoir capacity curve function; For hydroelectric power station Tailwater level-discharge curve function; For hydroelectric power station exist day Tailwater level at time, in meters; For hydroelectric power station The head loss constant, m; For hydroelectric power station exist day The water head at that time, m.

[0045] Step 3.3: Establish new energy constraints. The mathematical expression for the new energy output constraint is as follows:

[0046]

[0047] In the formula, and Hydropower stations The wind and solar power connected to the grid are in day Average output per hour, MW; For hydroelectric power station Wind power grid connection capacity, MW; For hydroelectric power station Photovoltaic grid connection capacity, MW; and Hydropower stations Surrounding wind and solar power day Unit installed capacity at a given time.

[0048] Step 3.4: Establish integrated operation constraints for hydropower, wind power, solar power, and energy storage. The mathematical expression for the bundled output constraint is as follows:

[0049]

[0050] In the formula, For hydroelectric power station exist day Average output per hour, MW; For the integrated watershed water, scenery and light base day The total output of the water, wind, and solar power storage system is MW. for day The amount of abandoned electricity at that time, MW; and , respectively, represent the charging and discharging power of the energy storage power station, in MW; , represents the maximum allowable curtailment rate, in %.

[0051] The mathematical expression for transmission channel constraints is as follows:

[0052]

[0053] In the formula, The upper limit of the transmission channel capacity is MW.

[0054] Step 3.5: Establish energy storage operation constraints. The mathematical expression for energy storage state constraints is as follows:

[0055]

[0056] In the formula, and These represent the initial and final states of charge of the energy storage power station, respectively, % . and The values ​​for the initial and final state of charge (SOC) of the energy storage power station are %, respectively. The SOC can be calculated using the following formula:

[0057]

[0058] In the formula, For energy storage power stations day State of charge at the end of the period, % The rated capacity of the energy storage power station is expressed in MWh. For energy storage power stations day State of charge at the end of the period, % and These represent the charging efficiency and discharging efficiency of the energy storage power station, respectively, in percentages (%). The duration of a single time period is h.

[0059] The mathematical expression for the energy storage charge and discharge constraints is as follows:

[0060]

[0061] In the formula, The charging and discharging power limit of the energy storage power station, in MW;

[0062] Modeling energy storage devices requires the introduction of complementary charging and discharging constraints to avoid simultaneous charging and discharging issues. However, such non-convex and nonlinear constraints significantly increase the difficulty of solving the optimization problem. Therefore, binary variables are introduced. The mathematical expression for energy storage charging and discharging constraints is transformed into the following linear constraint form:

[0063]

[0064] Step 3.6: Set energy storage parameters, including unit power cost, unit capacity cost, equipment operating life, initial and final state of charge, charge and discharge efficiency, operation and maintenance cost coefficient, etc. for various energy storage technologies.

[0065] Step 3.7, Short-to-Medium Nested Dimensionality Reduction: The model established in steps 3.1-3.6 would require a massive computational scale if solved directly hourly throughout the year. This method employs a short-to-medium nested dimensionality reduction strategy for hydropower. Specifically, the year is divided into a medium-term daily scale and a short-term hourly scale. At the medium-term daily scale, hydropower generation capacity is optimized, daily generation is rationally allocated, and short-term daily power boundaries are defined, while leveraging hydropower's regulatory capabilities across daily, weekly, monthly, and quarterly timescales. Subsequently, at the short-term hourly scale, daily hydropower power is optimized and allocated to hourly time periods within the day. Energy storage and new energy sources are still modeled hourly. This short-to-medium nested dimensionality reduction strategy effectively reduces the solution scale of the model while maintaining accuracy. The constraints for short-to-medium nested dimensionality reduction are shown in the following formula:

[0066]

[0067] In the formula, For hydroelectric power station exist Average daily power output, MW; and Hydropower stations exist Daily average power output, lower and upper limits, in MW; For hydroelectric power station exist Average daily output time, h.

[0068] At this point, the operational constraints of cascade hydropower can be transformed into:

[0069]

[0070] In the formula, and For hydroelectric power station reservoir Sun and The warehouse capacity at the end of the day, 10,000 m³ 3 ; For hydroelectric power station exist Daily natural inflow to the reservoir, m 3 / s; and Hydropower stations Reservoir and The reservoir is Total daily outbound flow, m 3 / s; For hydroelectric power station exist Daily power generation flow, m 3 / s, For hydroelectric power station exist Daily water discharge rate, m 3 / s; For hydroelectric power station exist Water level at the end of the day, in meters; and Hydropower stations exist The lower and upper boundaries of the daily water level operation, in meters, take into account flood control needs, dead water level and flood limit water level restrictions. and Hydropower stations The initial and final water levels during the scheduling period, in meters; and Hydropower stations The water level values ​​for the initial and final scheduling periods are in m; and Power plants exist Daily outflow and lower limit of power generation, m3 / s, the determination of the lower limit of outflow rate takes into account the comprehensive water demand at different times, including water supply, ecology, shipping, etc.; and Power plants exist Daily outflow and power generation flow limits, m 3 / s; For hydroelectric power station exist Average daily power output, MW; For hydroelectric power station exist day The lower limit of power output at that time, MW; For hydroelectric power station exist day Maximum output at any time, MW; For hydroelectric power station exist The water level of the day, m; For hydroelectric power station exist The tailwater level at the end of the day, in meters; For hydroelectric power station exist The water level of the sun, m.

[0071] Simultaneously, daily water level variation constraints are added on a medium-term scale:

[0072]

[0073] In the formula, and Hydropower stations The water level of the reservoir is The maximum daily drop and the maximum daily rise in water level, in meters (m).

[0074] Furthermore, the integration of new energy sources into large hydropower should not alter the existing capacity of large hydropower to meet the grid's peak-shaving demands. Therefore, the combined output of hydropower, wind power, solar power, and energy storage should meet certain output profile constraints. Based on the differentiated power generation characteristics of cascade hydropower during the flood and dry seasons, a unified per-unit output curve for combined hydropower, wind power, solar power, and energy storage has been determined for each river basin. The output profile constraints are shown in the following formula:

[0075]

[0076] In the formula, To create a bundled transmission mode library, this invention, based on the differentiated power generation characteristics of cascade hydropower during the flood and dry seasons, sets up two bundled transmission modes, which are... and Characterization, in which Corresponding to the double-peaked line pattern during the dry season (applicable to January-June and October-December); Corresponding to the single-peak flood season (applicable from July to September); This indicates that one operating mode can be set each day; For operating mode The corresponding typical per-unit curve; This is the magnification factor.

[0077] Step 3.8: Linearization of Nonlinear Constraints. Due to the non-convex nonlinear characteristics of the hydropower generation function, directly solving the model becomes difficult. Therefore, borrowing the triangular linear interpolation method, a small number of 0-1 integer variables are introduced to linearly model the nonlinear hydropower generation function. (Subscripts omitted) and The hydropower generation function can be simplified to ,in Power output (MW) for hydroelectric power stations. The head (m) is the water head. Power generation flow (m 3 / s), This is the hydropower generation function. Based on the variable range of reservoir head and power generation flow, three reference values ​​for head and three reference values ​​for power generation flow are sampled and denoted as follows: and , , , The three-dimensional surface representing the hydropower generation characteristics is divided into eight triangles with nine vertices each. Any point falling within each triangle can be uniquely determined by the reference values ​​of the three vertices of that triangle and their weighting coefficients. This is achieved by introducing three 0-1 variables. The triangle that the object falls into can be determined by the independent binary branch pattern.

[0078] Based on the above modeling method, nonlinear equality constraints It can be replaced with:

[0079]

[0080] in:

[0081]

[0082]

[0083] In the formula, The position coordinates are The weighting coefficient of the point.

[0084] The above formula ensures that the weights of the three vertices of the triangle are equal to 1, and implements the branch selection process of the triangle.

[0085] Step 3.9: Input the data collected in Step 1 into the 8760h integrated hydro-wind-solar-storage planning model established in Steps 3.1-3.8 to calculate the new energy access scale of the integrated hydro-wind-solar base without energy storage, and configure different proportions of energy storage based on this. Then, recalculate and output the optimal scale, operation process, and corresponding total power consumption of wind power and photovoltaic power stations under the corresponding energy storage scheme. Next, calculate the energy storage gain index and evaluate the gain effect of the energy storage configuration scheme on the base. Finally, adjust the model energy storage configuration scheme, repeat the above process, obtain the gain effect index value corresponding to different energy storage configuration schemes, and thus select the optimal energy storage configuration scheme.

[0086] The beneficial effects of this invention are:

[0087] This invention proposes a storage-allocation gain evaluation index, summarizes the parameters and characteristics of mainstream energy storage technologies, proposes a generalized set of operational constraints and parameter setting schemes for multiple types of energy storage, and constructs an integrated planning model for water, wind, solar, and energy storage with an embedded 8760-hour complementary operation simulation. By replacing energy storage parameters, the invention calculates the basin's renewable energy access capacity and the 8760-hour operation simulation process before and after different types of energy storage configurations, and quantifies the gain effect of energy storage on the integrated water, wind, and solar base of the basin through the storage-allocation gain evaluation index. Taking the Lancang River basin as an example, this invention quantitatively reveals the specific contributions of different energy storage technologies to improving the benefits of the integrated water, wind, and solar base, and selects the most reasonable energy storage configuration scheme, providing a reference for the scientific planning of integrated water, wind, and solar bases in the basin. Attached Figure Description

[0088] Figure 1 This is a framework diagram of the storage gain evaluation method;

[0089] Figure 2 It is a per-unit curve of the combined output of water, wind, solar and energy storage in the basin;

[0090] Figure 3 This is a diagram showing the annual increase in storage capacity after the base is allocated;

[0091] Figure 4 This is a diagram showing the annual gain effect of the base after the power transmission channel capacity is no longer limited;

[0092] Figure 5 This is a comparison chart of the annual gain effects of different short-term energy storage configuration schemes;

[0093] Figure 6 These are diagrams showing the operational results of different short-term energy storage configurations. Detailed Implementation

[0094] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0095] The overall process of this invention is as follows: Figure 1 As shown.

[0096] This invention was tested using a hydro-wind-solar power plant in the Lancang River basin. With the large-scale and continuous integration of new energy sources into the power grid, and the increasing demands on the grid's hydropower regulation capacity, the integrated development and operation of hydro-wind-solar power in the basin is facing, or will soon face, the dilemma of abundant new energy resources but insufficient hydropower regulation capacity. How to further enhance the basin's new energy integration capacity and the benefits of complementary hydro-wind-solar development through the configuration of energy storage technology is a new problem facing the integrated development of hydro-wind-solar power in the basin, urgently requiring the analysis of energy storage gain to provide decision support for optimizing energy storage schemes.

[0097] Step 1: Initial data collection;

[0098] Runoff data: Runoff data for the Lancang River basin from 2015 to 2024 were collected, and multi-year average daily runoff data were calculated. Simultaneously, basic data for each hydropower station within the basin were compiled, including water level operating boundaries, maximum and minimum outflow, maximum power generation flow, guaranteed output, installed capacity, regulation performance, water level-reservoir capacity curves, tailrace level-discharge flow curves, and output-head-power generation flow curves.

[0099] New energy power plant output data: Based on the historical output of new energy power plants already in operation at the Lancang River Basin hydro-wind-solar integrated base, a new energy unit installed capacity output sequence with hourly increments is calculated. The unit installed cost of wind power is set at 4.20 × 10 6 The unit installation cost of photovoltaic power is 3.45 × 10^6 yuan / MW. 6 Yuan / MW; annual operation and maintenance cost of wind power is 5.00×10 4 Yuan / MW, annual operation and maintenance cost of photovoltaic power is 3.80×10 4 The price is set at RMB / MW, and the operating period for both is uniformly set at 20 years.

[0100] Electricity price data: The electricity price settled on the generation side is 405.23 yuan / MWh.

[0101] Step 2: Propose the storage gain index Y;

[0102] The energy storage-allocation gain index is defined as the ratio of the increase in power generation revenue over the entire life cycle to the increase in system cost after energy storage is configured in a watershed integrated hydro-wind-solar base. A higher value indicates a more significant gain effect from energy storage. The mathematical expression is as follows:

[0103]

[0104] In the formula, and These represent the annual electricity consumption at the base before and after energy storage allocation, in MWh. The average electricity price is expressed in yuan / MWh. The figure represents the increase in the annualized cost of new energy after the allocation of storage facilities compared to before the allocation, in yuan. The annualized cost of the energy storage power station is [amount in yuan].

[0105] The proposed indicators in this method consider the comprehensive benefits throughout the entire system's lifecycle. However, considering the differences in lifecycles among different energy storage systems, this method further employs the annualized method, combining the lifecycle cost and benefit indicators of new energy and different types of energy storage with the discount rate and their respective operating years for annualization. When calculating the annualized costs of energy storage and new energy power plants, the initial investment cost and subsequent costs such as operation and maintenance are comprehensively considered. The mathematical expression is as follows:

[0106]

[0107] In the formula, and These represent the annualized investment cost and annualized operation and maintenance cost of the energy storage power station, respectively, in yuan; and These represent the annualized investment cost and annualized operation and maintenance cost of the new energy power plant, respectively, in yuan. The calculation formulas for the above costs are as follows:

[0108]

[0109]

[0110] In the formula, The installed capacity of energy storage is expressed in MW. Energy storage capacity, MWh; The unit power cost of energy storage is expressed in yuan / MW; The unit capacity cost of energy storage is expressed in yuan / MWh; The operation and maintenance cost coefficient for energy storage is %; and These represent the installed capacity of photovoltaic and wind power, respectively, in MW; and The unit installed costs for photovoltaic and wind power are respectively, in yuan / MW; and The figures are the annualized operation and maintenance costs for photovoltaic and wind power, respectively, in yuan / MW; and These are the annual value coefficients for energy storage and new energy power plants, respectively. The discount rate is %, and in this method it is taken as 5%. and These represent the operating periods, in years, for energy storage and new energy power plants, respectively.

[0111] Step 3: Construct an 8760h integrated planning model for water, wind, solar, and energy storage;

[0112] Step 3.1: Assuming all power sources within the system are uniformly transmitted through transmission channels, and taking the maximization of power absorption at the base as the objective function, the mathematical expression is as follows:

[0113]

[0114] In the formula, for day The combined power of water, wind, and solar energy storage ultimately contributes to the power output, in MW. For daily index; A set of daily indices for one year. ; For daily hourly indexes, For a daily hourly index set, .

[0115] Step 3.2: Establish the operational constraints for cascade hydropower. The mathematical expression for the water balance constraint is as follows:

[0116]

[0117] In the formula, For power station indexing; For the power station index set, ; For hydroelectric power station reservoir day Storage capacity at the end of the period, 10,000 m³ 3 ; For hydroelectric power station exist day Natural inflow runoff at that time, m 3 / s; For hydroelectric power station reservoir day Total outbound flow rate at time, m 3 / s; For hydroelectric power station exist day Power generation flow rate at time, m 3 / s, For hydroelectric power station exist day The discharge flow rate at that time, m 3 / s, this method sets it to 0.

[0118] The water level operation constraints are expressed mathematically as follows:

[0119]

[0120] In the formula, For hydroelectric power station reservoir day Water level at the end of the time period, in meters; and Hydropower stations exist day The lower and upper boundaries of the water level at that time, in meters, comprehensively consider flood control needs, dead water level and flood limit water level restrictions; and Hydropower stations The initial and final water levels during the scheduling period, in meters; and Hydropower stations The initial and final water levels during the scheduling period, in meters.

[0121] The mathematical expressions for the upper and lower limits of flow are as follows:

[0122]

[0123] In the formula, , Power plants exist day The lower limit of outflow and power generation flow at that time, m 3 / s, the determination of the lower limit of outflow rate takes into account the comprehensive water demand at different times, including water supply, ecology, shipping, etc.; , Power plants exist day The upper limit of outflow and power generation flow at that time, m 3 / s.

[0124] The upper and lower limits of output constraints are expressed mathematically as follows:

[0125]

[0126] In the formula, For hydroelectric power station exist day Average output per hour, MW; For hydroelectric power station exist day The lower limit of power output at that time, MW; For hydroelectric power station exist day The maximum output at any given time, in MW.

[0127] The mathematical expression for the hydroelectric power generation function is as follows:

[0128]

[0129] In the formula, For hydroelectric power station The output-head-power generation flow rate curve function; For hydroelectric power station exist day The water head at that time, m.

[0130] The constraint of the hydroelectric characteristic curve is expressed mathematically as follows:

[0131]

[0132] In the formula, For hydroelectric power station Water level-reservoir capacity curve function; For hydroelectric power station Tailwater level-discharge curve function; For hydroelectric power station exist day Tailwater level at time, in meters; For hydroelectric power station The head loss constant, m; For hydroelectric power station exist day The water head at that time, m.

[0133] Step 3.3: Establish new energy constraints. The mathematical expression for the new energy output constraint is as follows:

[0134]

[0135] In the formula, and Hydropower stations The wind and solar power connected to the grid are in day Average output per hour, MW; For hydroelectric power station Wind power grid connection capacity, MW; For hydroelectric power station Photovoltaic grid connection capacity, MW; and Hydropower stations Surrounding wind and solar power day Unit installed capacity at a given time.

[0136] Step 3.4: Establish integrated operation constraints for hydropower, wind power, solar power, and energy storage. The mathematical expression for the bundled output constraint is as follows:

[0137]

[0138] In the formula, For hydroelectric power station exist day Average output per hour, MW; For the integrated watershed water, scenery and light base day The total output of the water, wind, and solar power storage system is MW. for day The amount of abandoned electricity at that time, MW; and These represent the charging and discharging power of the energy storage power station, respectively, in MW. The maximum permissible curtailment rate is %, which is set to 0% in this invention.

[0139] The mathematical expression for transmission channel constraints is as follows:

[0140]

[0141] In the formula, This represents the upper limit of the transmission channel capacity, in MW.

[0142] Step 3.5: Establish energy storage operation constraints. The mathematical expression for energy storage state constraints is as follows:

[0143]

[0144] In the formula, and These represent the initial and final states of charge of the energy storage power station, respectively, %; and The values ​​for the initial and final state of charge (SOC) of the energy storage power station are %, respectively. The SOC can be calculated using the following formula:

[0145]

[0146] In the formula, For energy storage power stations day State of charge at the end of the period, % The rated capacity of the energy storage power station is expressed in MWh. For energy storage power stations day State of charge at the end of the period, % and These represent the charging efficiency and discharging efficiency of the energy storage power station, respectively, in percentages (%). The duration of a single time period is h.

[0147] The mathematical expression for the energy storage charge and discharge constraints is as follows:

[0148]

[0149] In the formula, , where MW represents the charging and discharging power limit of an energy storage power station.

[0150] Modeling energy storage devices requires the introduction of complementary charging and discharging constraints to avoid simultaneous charging and discharging issues. However, such non-convex and nonlinear constraints significantly increase the difficulty of solving the optimization problem. Therefore, binary variables are introduced. Transform the above equation into the following linear constraint form:

[0151]

[0152] Step 3.6: Set energy storage parameters. The various energy storage technical parameters required for model calculation are shown in Table 1.

[0153] Table 1 Energy Storage Parameter Settings

[0154]

[0155] Step 3.7: Short-to-Medium Nested Dimensionality Reduction. If the above model were directly solved hourly throughout the year, the computational scale would be enormous. Taking the Lancang River Basin hydro-wind-solar integrated base as an example, the model variable scale for hourly solutions throughout the year would be approximately 500,000, and the constraint scale approximately 510,000, making the solution extremely difficult. Therefore, a short-to-medium nested dimensionality reduction strategy for hydropower is adopted. Specifically, the year is divided into a medium-term daily scale and a short-term hourly scale. At the medium-term daily scale, the model optimizes hydropower generation capacity, rationally allocates daily hydropower generation, defines short-term daily power boundaries, and leverages the regulatory capacity of hydropower across daily, weekly, and monthly / quarterly time scales. Subsequently, at the short-term hourly scale, the daily hydropower power is optimized and allocated to hourly time periods within the day. Energy storage and new energy sources are still modeled hourly. This short-to-medium nested dimensionality reduction strategy effectively reduces the solution scale of the model while maintaining accuracy. Taking the Lancang River Basin base as an example, the original model variable scale is reduced to approximately 190,000, and the constraint scale is reduced to approximately 190,000. The short-to-medium nested dimensionality reduction constraints are shown in the following formula:

[0156]

[0157] In the formula, For hydroelectric power station exist Average daily power output, MW; and Hydropower stations exist Daily average power output, lower and upper limits, in MW; For hydroelectric power station exist Average daily output time, h.

[0158] At this point, the operational constraints of cascade hydropower can be transformed into:

[0159]

[0160] In the formula, and For hydroelectric power station reservoir Sun and The warehouse capacity at the end of the day, 10,000 m³ 3 ; For hydroelectric power station exist Daily natural inflow to the reservoir, m 3 / s; and Hydropower stations Reservoir and The reservoir is Total daily outbound flow, m 3 / s; For hydroelectric power station exist Daily power generation flow, m 3 / s, For hydroelectric power station exist Daily water discharge rate, m 3 / s, which is set to 0 in this invention; For hydroelectric power station exist Water level at the end of the day, in meters; and Hydropower stations exist The lower and upper boundaries of the daily water level operation, in meters, take into account flood control needs, dead water level and flood limit water level restrictions. and Hydropower stations The initial and final water levels during the scheduling period, in meters; and Hydropower stations The water level values ​​for the initial and final scheduling periods are in m; and Power plants exist Daily outflow and lower limit of power generation, m 3 / s, the determination of the lower limit of outflow rate takes into account the comprehensive water demand at different times, including water supply, ecology, shipping, etc.; and Power plants exist Daily outflow and power generation flow limits, m 3 / s; For hydroelectric power station exist Average daily power output, MW; For hydroelectric power station exist day The lower limit of power output at that time, MW; For hydroelectric power station exist day Maximum output at any time, MW; For hydroelectric power station exist The water level of the day, m; For hydroelectric power station exist The tailwater level at the end of the day, in meters; For hydroelectric power station exist The water level of the sun, m.

[0161] Simultaneously, daily water level variation constraints are added on a medium-term scale:

[0162]

[0163] In the formula, and Hydropower stations The water level of the reservoir is The maximum daily drop and the maximum daily rise in water level, in meters (m).

[0164] Furthermore, the integration of new energy sources into large hydropower should not alter the existing capacity of large hydropower to meet the grid's peak-shaving demands. Therefore, the bundled output of hydropower, wind power, solar power, and energy storage should meet certain output curve constraints. Based on the differentiated power generation characteristics of cascade hydropower during the flood and dry seasons, a unified per-unit output curve for bundled hydropower, wind power, solar power, and energy storage has been determined for each river basin, such as... Figure 2 As shown, the flood season is from July to September, and the dry season is from January to June and October to December. The power output alignment constraints are shown in the following formula:

[0165]

[0166] In the formula, To create a bundled transmission mode library, this study sets up two bundled transmission modes based on the differentiated power generation characteristics of cascade hydropower during the flood and dry seasons. and Characterization. Among them... Corresponding to the double-peak line pattern during the dry season (applicable to January-June and October-December); Corresponding to the single-peak flood season (applicable from July to September), see the specific flood season data. Figure 2 ; This indicates that one operating mode can be set each day; For operating mode The corresponding typical per-unit curve; This is the magnification factor.

[0167] Step 3.8: Linearization of Nonlinear Constraints. Due to the non-convex nonlinear characteristics of the hydropower generation function, directly solving the model becomes difficult. Therefore, borrowing the triangular linear interpolation method, a small number of 0-1 integer variables are introduced to linearly model the nonlinear hydropower generation function. (Subscripts omitted) and The hydropower generation function can be simplified to ,in Power output (MW) for hydroelectric power stations. The head (m) is the water head. Power generation flow (m 3 / s), This is the hydropower generation function. Based on the variable range of reservoir head and power generation flow, three reference values ​​for head and three reference values ​​for power generation flow are sampled and denoted as follows: and , , , The three-dimensional surface representing the hydropower generation characteristics is divided into eight triangles with nine vertices each. Any point falling within each triangle can be uniquely determined by the reference values ​​of the three vertices of that triangle and their weighting coefficients. This is achieved by introducing three 0-1 variables. The triangle that the object falls into can be determined by the independent binary branch pattern.

[0168] Based on the above modeling method, nonlinear equality constraints It can be replaced with:

[0169]

[0170] in:

[0171]

[0172]

[0173] In the formula, The position coordinates are The weighting coefficient of the point.

[0174] The above formula ensures that the weights of the three vertices of the triangle are equal to 1, and implements the branch selection process of the triangle.

[0175] Step 4: Input the data prepared in Step 1 into the 8760h integrated hydro-wind-solar-storage planning model established in Steps 3.1-3.8 to calculate the new energy access scale of the integrated hydro-wind-solar base without energy storage, and configure different proportions of energy storage based on this. Then, recalculate and output the optimal scale, operation process, and corresponding total power consumption of wind power and photovoltaic power stations under the corresponding energy storage scheme. Next, calculate the energy storage gain index and evaluate the gain effect of the energy storage configuration scheme on the base. Finally, adjust the model energy storage configuration scheme, repeat the above process, and obtain the gain effect index values ​​corresponding to different energy storage configuration schemes.

[0176] Step 5: Select the optimal energy storage configuration scheme.

[0177] Step 5.1: Analyze the gain effects of different types of energy storage technologies. Without energy storage, the Lancang River Basin hydropower-wind-solar integrated base can connect 1290MW of wind power capacity and 1146MW of photovoltaic capacity, with an annual electricity consumption of 4.45 × 10⁻⁶ MW. 7 MWh. Based on this, the energy storage scale was set at 15% of the installed capacity of new energy sources, with different types of energy storage technologies configured. The results showed that after configuring energy storage, the new energy access capacity increased by approximately 52.02%-64.04%, and the annual electricity consumption increased by approximately 6.29%-7.19%. This indicates that configuring energy storage significantly improved the base's new energy consumption capacity, with the increase in annual electricity consumption mainly stemming from the increased scale of new energy access at the base. However, compared to short-term energy storage technologies, configuring long-term energy storage technologies did not significantly improve the new energy access capacity of the cascade hydropower stations in the Lancang River basin. Figure 3 It can be seen that the gain effect of configuring short-term energy storage is the most significant. Compared with long-term energy storage, the average gain index of the base configured with short-term energy storage is increased by approximately 26.49%. Furthermore, under the same energy storage type, the gain effect of energy storage gradually weakens as the storage duration increases. This is mainly because for integrated watershed hydropower, wind power, and solar power bases, hydropower and new energy sources have a certain degree of seasonal complementarity, and hydropower itself has the ability to seasonally transfer large amounts of electricity, thus eliminating the need for excessive reliance on long-term energy storage technology. Configuring long-term energy storage would actually increase energy storage costs and weaken overall benefits.

[0178] Step 5.2: Analyze the impact of transmission channel capacity limitations on the gain effect of energy storage configuration. To further verify whether the transmission channel capacity limits the gain effect of configuring long-term energy storage, the upper limit constraint of the transmission channel capacity is ignored, and an operational simulation is conducted, with other parameter settings remaining consistent with those in Step 5.1. In a scenario without transmission channel capacity limitations, 15% of the installed capacity of new energy sources without energy storage is taken as the energy storage configuration capacity. Different types of energy storage technologies are configured, and the impact of energy storage configuration on the Lancang River Basin hydro-wind-solar integrated base is analyzed. Figure 4As shown, compared to scenarios with transmission channel capacity limitations, the scale of renewable energy access and annual electricity consumption in the river basin base increase to some extent in unrestricted scenarios. After configuring energy storage, the average increase in renewable energy access capacity at the Lancang River Basin hydro-wind-solar integrated base is approximately 63.33%; the average increase in annual electricity consumption is approximately 7.20%. Even after removing transmission channel capacity limitations, the gain effect of configuring short-term energy storage technology remains the most significant. Compared to long-term energy storage, the average gain index of configuring short-term energy storage in the base increases by approximately 37.83%.

[0179] Step 5.3: Analyze the short-term energy storage gain effect under various configuration schemes. Except for the energy storage configuration ratio and energy storage duration, the other parameters are the same as in Step 5.2. The energy storage configuration ratio is set to range from 5.00% to 35.00%, divided in 2.50% intervals, resulting in a total of 13 energy storage configuration ratio schemes. The calculation model comprehensively considers the combination of three energy storage durations and 13 energy storage configuration ratios, comparing the gain effect of each scheme on the Lancang River Basin's integrated water, wind, and solar power base. Figure 5 As shown. Figure 6 The results show the annual costs of energy storage, annual costs of adding new energy sources, and annual costs of increasing power generation under different schemes.

[0180] Depend on Figure 5 It can be seen that the optimal energy storage duration for the Lancang River Basin hydro-wind-solar integrated base is 2 hours. Within the energy storage allocation ratio range of 5.00%-35.00%, the optimal allocation ratio for the base is 5.00%. Under the same energy storage configuration ratio, the energy storage gain effect of the base gradually weakens as the energy storage duration increases. Under the same energy storage duration, the energy storage gain effect of the Lancang River Basin hydro-wind-solar integrated base shows a continuously weakening trend as the energy storage configuration ratio increases.

Claims

1. A method for evaluating the storage gain of a watershed water-wind-solar base, characterized in that, The steps are as follows: Step 1: Initial data collection; This includes runoff data, power output data from renewable energy plants, and electricity price data; Step 2: Propose the storage gain index Y; The energy storage gain index is defined as the ratio of the increase in power generation revenue over the entire life cycle to the increase in system cost after energy storage is configured in the integrated water, wind and solar power base in the basin. Step 3: Construct an 8760h integrated planning model for water, wind, solar, and energy storage; Step 3.1: Assume that all power sources within the system are uniformly transmitted through transmission channels, with the objective function being to maximize the amount of electricity absorbed by the base. ; objective function The mathematical expression is as follows: In the formula, for day The combined power of water, wind, and solar energy storage ultimately contributes to the power output, in MW. For daily index, A set of daily indices for one year. ; For daily hourly indexes, For the daily hourly index set, ; Step 3.2: Establish operational constraints for cascade hydropower; Step 3.3: Establish new energy constraints; Step 3.4: Establish operational constraints for integrated hydropower, wind power, solar power, and energy storage; Step 3.5: Establish energy storage operation constraints; Step 3.6: Set energy storage parameters, including unit power cost, unit capacity cost, equipment operating life, initial and final state of charge, charge and discharge efficiency, and operation and maintenance cost coefficient for various energy storage technologies; Step 3.7, Medium- and Short-Term Nested Dimensionality Reduction: Divide the whole year into medium-term daily scale and short-term hourly scale; on the medium-term daily scale, optimize the hydropower generation capacity, rationally allocate the daily power generation of hydropower, define the boundary of short-term daily power generation, and at the same time, give full play to the regulation capacity of hydropower across daily, weekly, monthly and quarterly time scales. Subsequently, on a short-term hourly scale, the daily hydropower output was optimized and allocated to hourly time periods within the day; Energy storage and new energy sources still use hourly modeling; the short-to-medium term nested dimensionality reduction constraints are shown in the following formula: In the formula, For hydroelectric power station exist Average daily power output, MW; and Hydropower stations exist Daily average power output, lower and upper limits, in MW; For hydroelectric power station exist Average daily output time, in hours; For hydroelectric power station exist day Average output per hour, MW; Step 3.8: Linearization of nonlinear constraints: Combining the triangular linear interpolation method, a small number of 0-1 integer variables are introduced to linearly model the nonlinear hydropower generation function; Step 3.9: Input the data collected in Step 1 into the 8760h integrated hydro-wind-solar-storage planning model established in Steps 3.1-3.8 to calculate the new energy access scale of the integrated hydro-wind-solar base without energy storage, and configure different proportions of energy storage based on this. Then, recalculate and output the optimal scale, operation process, and corresponding total power consumption of wind power and photovoltaic power stations under the corresponding energy storage scheme. Next, calculate the energy storage gain index and evaluate the gain effect of the energy storage configuration scheme on the base. Finally, adjust the model energy storage configuration scheme, repeat the above process, obtain the gain effect index value corresponding to different energy storage configuration schemes, and thus select the optimal energy storage configuration scheme.

2. The method for evaluating the storage gain of a watershed hydro-wind-solar base according to claim 1, characterized in that, In step 1, Runoff data: Collect runoff data of the basin and calculate the multi-year average daily step runoff data; organize the basic data of hydropower stations in the basin, including water level operating boundaries, maximum and minimum outflow, maximum power generation flow, guaranteed output, installed capacity, regulation performance, water level-storage capacity relationship curve, tailwater level-discharge flow curve, and output-head-power generation flow curve. New energy power plant output data: Based on the historical output of new energy power plants already in operation in the integrated water, wind and solar power base of the basin, the new energy unit installed capacity output sequence with hourly increments is calculated; the unit installed cost and annual operation and maintenance cost of wind power and photovoltaic power are collected to determine the operating period; Electricity price data: Settlement price of electricity generated in the river basin.

3. The method for evaluating the storage gain of a watershed hydro-wind-solar base according to claim 1, characterized in that, In step 2, the mathematical expression for the storage gain index Y is as follows: In the formula, and These represent the annual electricity consumption at the base before and after energy storage allocation, in MWh. The average electricity price is expressed in yuan / MWh. The figure represents the increase in the annualized cost of new energy after the allocation of storage facilities compared to before the allocation, in yuan. The annualized cost of the energy storage power station is [amount in yuan]. Furthermore, the equivalent annual value method is adopted to annualize the life-cycle cost and benefit indicators of new energy and different types of energy storage by combining the discount rate and their respective operating years; when calculating the annualized cost of energy storage and new energy power plants, the initial investment cost and operation and maintenance are comprehensively considered, and the mathematical expression is as follows: In the formula, and These represent the annualized investment cost and annualized operation and maintenance cost of the energy storage power station, respectively, in yuan; and These represent the annualized investment cost and annualized operation and maintenance cost of the new energy power plant, respectively, in yuan; the cost calculation formula is as follows: In the formula, The installed capacity of energy storage is expressed in MW. Energy storage capacity, MWh; The unit power cost of energy storage is expressed in yuan / MW; The unit capacity cost of energy storage is expressed in yuan / MWh; The percentage is the operation and maintenance cost coefficient for energy storage, %. and These represent the installed capacity of photovoltaic and wind power, respectively, in MW; and The unit installed costs for photovoltaic and wind power are respectively, in yuan / MW; and The figures are the annualized operation and maintenance costs for photovoltaic and wind power, respectively, in yuan / MW; and These are the annual value coefficients for energy storage and new energy power plants, respectively. The discount rate is %; and These represent the operating periods, in years, for energy storage and new energy power plants, respectively.

4. The method for evaluating the storage gain of a watershed hydro-wind-solar base according to claim 1, characterized in that, Step 3.2 is as follows: The mathematical expression for water balance constraints is as follows: In the formula, For power station indexing; For the power station index set, ; For hydroelectric power station reservoir day Storage capacity at the end of the period, 10,000 m³ 3 ; For hydroelectric power station exist day Natural inflow runoff at that time, m 3 / s; For hydroelectric power station reservoir day Total outbound flow rate at time, m 3 / s; For hydroelectric power station exist day Power generation flow rate at time, m 3 / s, For hydroelectric power station exist day The discharge flow rate at that time, m 3 / s; The water level operation constraints are expressed mathematically as follows: In the formula, For hydroelectric power station exist day Water level at the end of the time period, in meters; and Hydropower stations exist day The lower and upper boundaries of the water level operation at that time, in meters; and Hydropower stations The initial and final water levels during the scheduling period, in meters; and Hydropower stations The water level values ​​for the initial and final scheduling periods are in m; The mathematical expressions for the upper and lower limits of flow are as follows: In the formula, and Power plants exist day The lower limit of outflow and power generation flow at that time, m 3 / s; and Power plants exist day The upper limit of outflow and power generation flow at that time, m 3 / s; The upper and lower limits of output constraints are expressed mathematically as follows: In the formula, For hydroelectric power station exist day Average output per hour, MW; For hydroelectric power station exist day The lower limit of power output at that time, MW; For hydroelectric power station exist day Maximum output at any time, MW; The mathematical expression for the hydroelectric power generation function is as follows: In the formula, For hydroelectric power station The output-head-power generation flow rate curve function; For hydroelectric power station exist day The water head at that time, in meters; The constraint of the hydroelectric characteristic curve is expressed mathematically as follows: In the formula, For hydroelectric power station Water level-reservoir capacity curve function; For hydroelectric power station Tailwater level-discharge curve function; For hydroelectric power station exist day Tailwater level at time, in meters; For hydroelectric power station The head loss constant, m; For hydroelectric power station exist day The water head at that time, m.

5. The method for evaluating the storage gain of a watershed hydro-wind-solar base according to claim 4, characterized in that, In step 3.3, the mathematical expression for the power output constraint of new energy sources is as follows: In the formula, and Hydropower stations The wind and solar power connected to the grid are in day Average output per hour, MW; For hydroelectric power station Wind power grid connection capacity, MW; For hydroelectric power station Photovoltaic grid connection capacity, MW; and Hydropower stations Surrounding wind and solar power day Unit installed capacity at a given time.

6. The method for evaluating the storage gain of a watershed hydro-wind-solar base according to claim 5, characterized in that, In step 3.4, the mathematical expression for the bundling force constraint is as follows: In the formula, For hydroelectric power station exist day Average output per hour, MW; For the integrated watershed water, scenery and light base day The total output of the combined power of water, wind, light, and energy storage is MW. for day The amount of abandoned electricity at that time, MW; and These represent the charging and discharging power of the energy storage power station, respectively, in MW. The maximum allowable curtailment rate is % The mathematical expression for transmission channel constraints is as follows: In the formula, This represents the upper limit of the transmission channel capacity, in MW.

7. The method for evaluating the storage gain of a watershed hydro-wind-solar base according to claim 6, characterized in that, In step 3.5, The mathematical expression for the energy storage state constraint is as follows: In the formula, and These represent the initial and final states of charge of the energy storage power station, respectively, % . and The values ​​for the initial and final state of charge (SOC) of the energy storage power station are %, and the SOC is calculated using the following formula: In the formula, For energy storage power stations day State of charge at the end of the period, % The rated capacity of the energy storage power station is expressed in MWh. For energy storage power stations day State of charge at the end of the period, % and These represent the charging efficiency and discharging efficiency of the energy storage power station, respectively, in percentages (%). The duration of a single time period, in hours (h). The mathematical expression for the energy storage charge and discharge constraints is as follows: In the formula, The charging and discharging power limit of the energy storage power station, in MW; Introducing binary variables The mathematical expression for energy storage charging and discharging constraints is transformed into the following linear constraint form: 。 8. The method for evaluating the storage gain of a watershed hydro-wind-solar base according to claim 7, characterized in that, Step 3.7 also includes: The operational constraints of cascade hydropower are transformed into: In the formula, and For hydroelectric power station reservoir Sun and The warehouse capacity at the end of the day, 10,000 m³ 3 ; For hydroelectric power station exist Daily natural inflow to the reservoir, m 3 / s; and Hydropower stations reservoir and The reservoir is Total daily outbound flow, m 3 / s; For hydroelectric power station exist Daily power generation flow, m 3 / s, For hydroelectric power station exist Daily water discharge rate, m 3 / s; For hydroelectric power station exist Water level at the end of the day, in meters; and Hydropower stations exist The lower and upper boundaries of the daily water level operation, in meters; and Hydropower stations The initial and final water levels during the scheduling period, in meters; and Hydropower stations The water level values ​​for the initial and final scheduling periods are in m; and Power plants exist Daily outflow and lower limit of power generation, m 3 / s; and Power plants exist Daily outflow and power generation flow limits, m 3 / s; For hydroelectric power station exist Average daily power output, MW; For hydroelectric power station exist day The lower limit of power output at that time, MW; For hydroelectric power station exist day Maximum output at any time, MW; For hydroelectric power station exist The water level of the day, m; For hydroelectric power station exist The tailwater level at the end of the day, in meters; For hydroelectric power station exist The water level of the day, m; Simultaneously, daily water level variation constraints are added on a medium-term scale: In the formula, and Hydropower stations The water level of the reservoir is Maximum daily drop and maximum daily rise in water level, in meters; After new energy sources are integrated into large hydropower plants, the original capacity of these plants to respond to the grid's peak-shaving demands should not be altered. Therefore, the combined output of hydropower, wind power, solar power, and energy storage should meet certain output profile constraints. Based on the differentiated power generation characteristics of cascade hydropower during the flood and dry seasons, a unified per-unit output curve for combined hydropower, wind power, solar power, and energy storage has been determined for each river basin. The output profile constraints are shown in the following formula: In the formula, To create a bundled transmission mode library, based on the differentiated power generation characteristics of cascade hydropower during the flood and dry seasons, two bundled transmission modes are set up, consisting of... and Characterization; among which The corresponding double-peak line pattern during the dry season is applicable from January to June and from October to December; This corresponds to a single-peak flood season and is suitable for July to September. This indicates that one operating mode can be set each day; For operating mode The corresponding typical per-unit curve; This is the magnification factor.

9. The method for evaluating the storage gain of a watershed hydro-wind-solar base according to claim 1, characterized in that, Step 3.8 is as follows: omit subscript and The hydropower generation function is simplified to ,in Power output for the hydroelectric power station, MW; The head is measured in meters (m). For power generation flow, m 3 / s; Let be the hydropower generation function; based on the variable range of reservoir head and power generation flow, three head reference values ​​and three power generation flow reference values ​​are used, denoted as respectively. and , , , The three-dimensional surface representing the hydropower generation characteristics is divided into eight triangles with nine vertices. Any point falling within each triangle is uniquely determined by the reference values ​​of the three vertices of that triangle and their weighting coefficients. This is achieved by introducing three 0-1 variables. The triangle that the character falls into is determined by the independent binary branch pattern; Based on the above modeling method, nonlinear equality constraints Replaced with: in: In the formula, The position coordinates are The weighting coefficient of the point; The above formula ensures that the weights of the three vertices of the triangle are equal to 1, and implements the branch selection process of the triangle.

Images