A source-grid-load coordinated scheduling method based on multi-time scale coupling

Abstract

A source-grid-load coordinated scheduling method based on multi-time scale coupling comprises the following steps: Step 1: performing pre-season scheduling, dividing the year into multiple typical seasons according to the distribution characteristics of water resources in the year, respectively formulating corresponding water resource allocation strategies, comprehensively considering the seasonal variation characteristics of new energy output, formulating the coordinated scheduling arrangement within the season, and outputting the pre-season scheduling plan; Step 2: based on the pre-season scheduling plan output in Step 1, combining the load prediction and new energy output prediction information in the day-ahead scheduling stage, carrying out day-ahead scheduling, and outputting the day-ahead scheduling plan; Step 3: based on the day-ahead scheduling plan output in Step 2, combining the updated weather and load prediction information within the day, carrying out intra-day scheduling, and outputting the intra-day scheduling plan; Step 4: based on the intra-day scheduling plan output in Step 3, combining the real-time monitoring data, carrying out real-time scheduling, and outputting the real-time scheduling plan.

Description

Technical Field

[0001] This invention relates to the fields of new energy and green energy technology, and in particular to large-scale new energy dispatching technology, specifically a source-grid-load coordinated dispatching technology based on multi-timescale coupling. Background Technology

[0002] To enhance system flexibility and improve the utilization rate of new energy sources, patent document CN109245169A discloses a "wind-solar-hydro-storage" joint dispatching method. This scheme is based on the energy complementarity characteristics of the integrated "wind-solar-hydro-storage" base. With the goal of optimizing the economic efficiency of the integrated "wind-solar-hydro-storage" system, it establishes a complementary output system model to compensate for the fluctuation of new energy output, provide more stable power to the grid, and improve the utilization rate of new energy sources. Patent document CN117081175A discloses a power production simulation method for an integrated hydro-wind-solar-storage base. It makes full use of the rapid adjustment capability of pumped storage and the flexible peak-shaving capability of hydropower to achieve smooth adjustment of wind and solar fluctuations, thereby adapting to load characteristics under different time scales and enhancing the system's regulation capability.

[0003] The aforementioned methods leverage the energy complementarity of integrated wind-solar-hydro-storage bases to mitigate wind and solar fluctuations and enhance the flexibility of the source-side system. However, most still focus on the coordinated optimization of a single time scale or a local power system, failing to fully consider the multi-time scale evolution characteristics of new energy output under the combined influence of seasonal trends and short-term random fluctuations. Furthermore, related studies generally neglect the coordination of other links in the power system, such as the grid side and the load side, resulting in a one-sidedness of the overall dispatch method at the system level. As the installed capacity and power generation ratio of new energy continue to increase, the power system's reliance on flexibility resources is growing stronger. There is an urgent need to coordinate the regulation capabilities and response characteristics of the source, grid, and load sides at multiple time scales to achieve coordinated optimization across multiple time scales and links, so as to better support the safe consumption and efficient utilization of high-proportion new energy.

[0004] In summary, this invention proposes a source-grid-load coordinated scheduling technology based on multi-timescale coupling. Summary of the Invention

[0005] The purpose of this invention is to address the technical problem of low power system operating efficiency caused by the randomness and volatility of power output during large-scale renewable energy integration. Existing technologies mostly utilize the energy complementarity of integrated wind-solar-hydro-storage bases to mitigate wind and solar fluctuations and improve the flexibility of the source-side system. However, most of them still focus on the coordination and optimization of a single time scale or a local power system, without fully considering the multi-time scale evolution characteristics of renewable energy output under the superposition of seasonal trends and short-term random fluctuations. In addition, existing technologies generally ignore the coordination of other links in the power system, such as the grid side and the load side, making the overall dispatching method one-sided at the system level. In view of this, this invention is proposed.

[0006] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0007] A source-grid-load coordinated scheduling method based on multi-timescale coupling includes a pre-season scheduling phase, a day-ahead scheduling phase, an intraday scheduling phase, and a real-time scheduling phase; it includes the following steps:

[0008] Step 1: Execute pre-season scheduling. Based on the distribution characteristics of water resources throughout the year, the year is divided into multiple typical seasons. Corresponding water resource allocation strategies are formulated for each season. Taking into account the seasonal variation characteristics of new energy output, a coordinated scheduling arrangement within the season is formulated, and a pre-season scheduling plan is output.

[0009] Step 2: Based on the pre-season scheduling plan output in Step 1, and combined with the load forecast and new energy output forecast information in the day-ahead scheduling phase, carry out day-ahead scheduling and output the day-ahead scheduling plan.

[0010] Step 3: Based on the day-ahead scheduling plan output in Step 2, and combined with the intraday updated meteorological and load forecast information, carry out intraday scheduling and output the intraday scheduling plan;

[0011] Step 4: Based on the intraday scheduling plan output in Step 3, and combined with real-time monitoring data, conduct real-time scheduling and output the real-time scheduling plan.

[0012] In step 1, the pre-season scheduling phase includes the following steps:

[0013] Step 1-1) Based on the historical operation data and load history data of the integrated wind-solar-water-storage base, analyze its typical seasonal variation patterns, summarize and construct historical datasets including wind speed, light intensity, temperature, runoff and load, substitute historical wind speed data into equation (1) to calculate historical wind power output data; substitute historical light intensity and temperature data into equation (2) to calculate historical photovoltaic output data. The finally obtained watershed runoff data, wind and solar historical output data and load history data together constitute the input of step 1; Step 1-2) Formulate the pre-season scheduling plan. Based on the historical dataset generated in step 1-1), statistically analyze the errors between watershed runoff data, wind and solar historical data and load historical data and actual data, construct the corresponding historical data error distribution, determine the input data error range of water, wind, solar and load in the pre-season scheduling stage to determine the degree of input data error. Specifically, use relative error to analyze the accuracy of water, wind and solar output and load demand. The relative error formula is shown in equation (3):

[0014]

[0015] In equation (3), the scenario is a time scale of t and a time period of s. The relative error between the input data sample and the actual data is represented by i, where i ∈ [1, I], I is the total number of samples, and k = w, wd, pv, ld represent runoff data, wind power data, solar power data, and load data, respectively. This represents the input data sample data. Represents actual data;

[0016] Based on the quantization results of the relative error, the error probability distribution of various input data is constructed, as shown in Equation (4):

[0017]

[0018] make As the relative error used to establish input data error constraints at the time scale t;

[0019] Steps 1-3) Based on the relative error of the input data for the pre-season scheduling phase determined in Step 1-2), and combined with the regulation capabilities of the power generation, grid, and load sides, comprehensively utilize the adjustability of hydropower stations, the charging and discharging flexibility of energy storage units, the transmission capacity of DC interconnection channels, and the regulation characteristics of flexible loads to reduce the uncertainty of wind and solar power output, enhance the system's ability to cope with fluctuations in wind and solar power output, and further improve the absorption level and utilization efficiency of wind and solar resources; Steps 1-4) Based on the reserve plan for the pre-season scheduling phase generated in Step 1-3), and combined with the actual operating status of the system, to ensure... To ensure the safe and stable operation of the system, further set corresponding operating constraints based on the technical characteristics and equipment status of the source, grid, and load; Steps 1-5) Based on the relevant constraint sets established in Steps 1-1) to 1-4), solve the problem with the goal of minimizing the overall operating cost of the system; Steps 1-6) Based on the solution results of Steps 1-5), generate the power output configuration plan of the integrated wind-solar-water-storage base, determine the configuration of purchased and transmitted electricity, form a pre-season scheduling plan, provide maintenance backup and water allocation plan for Step 2, and complete the execution of Step 1. The specific model involved in Step 1-1) is as follows: The power output uncertainty model of wind turbine (4) is shown in Equation (1):

[0020]

[0021] In equation (1), t = d, h, q, m represent different time scales, including four time granularities: day, hour, quarter-hour, and minute; s is the time period index at time scale t, s ∈ [1, S]; S is the total number of scheduling time periods at time scale t; the scenario is time period s at time scale t; n is the integrated power station index, n ∈ [1, N]; and N is the total number of integrated power stations. For the wind power output of the integrated power station n, Let n be the wind speed of the integrated power station. These are the cut-in wind speed, rated wind speed, and cut-out wind speed of the wind turbine unit in the integrated power station n. The rated power of the wind turbine unit in the integrated power station n;

[0022] The output model of the photovoltaic unit is shown in equation (2):

[0023]

[0024] In equation (2), the scenario is a time scale of t and a time period of s. For the photovoltaic output of integrated power station n, The rated power of the photovoltaic units in the integrated power station n is... and Let represent the surface shortwave incident radiation capacity and solar radiation intensity under standard test conditions of the photovoltaic unit of the integrated power station n, respectively. Let be the temperature coefficient of the output power of the solar cell modules in the integrated power station n. and These represent the photovoltaic module temperature and air temperature of the integrated power station n, respectively. and These represent the temperatures under standard test conditions and normal conditions for the photovoltaic modules of the integrated power station n, respectively; in steps 1-3), the specific models involved are as follows: the power output and reservoir capacity models of the hydropower station are shown in equation (5):

[0025]

[0026] In equation (5), the scenario is a time scale of t and a time period of s. and These represent the total downstream discharge and inflow of the integrated hydropower station n, respectively. and These represent the power generation discharge and water abandonment discharge of the integrated power station n, respectively. This represents the output of the integrated power station n, a hydroelectric power station. Let n be the overall power output coefficient of the integrated power station. These represent the head height, reservoir level, tailrace level, and head loss height of the integrated power station n. The reservoir capacity of the integrated power station n is [missing information]. and These are the coefficients of each term in the reservoir capacity-water level-storage capacity relationship curve of the integrated power station n hydropower station, which is fitted using a polynomial. and The coefficients of the tailwater level-total discharge flow relationship curve of the integrated power station n hydropower station are respectively obtained by using polynomial fitting, and Δs is the dispatch time interval; the energy storage unit and energy storage capacity model are shown in Equation (6):

[0027]

[0028] In equation (6), the scenario is a time scale of t and a time period of s. The energy storage capacity of the energy storage unit in the integrated power station n is [the energy storage capacity]. and These represent the charging and discharging efficiencies of the energy storage unit in the integrated power station n. and These represent the charging and discharging power of the energy storage unit in the integrated power station n. and These represent the minimum and maximum energy storage capacities of the energy storage units in the integrated power station n, respectively. and These are the minimum and maximum charging power of the energy storage unit in the integrated power station n, respectively. and These are the minimum and maximum discharge powers of the integrated power station n, respectively.

[0029] During the pre-season scheduling phase, in order to cope with the power station equipment maintenance plan, a corresponding backup plan needs to be preset. Considering the regulation capacity of the source, grid and load sides and the seasonal characteristics of wind, solar and water resources, the system scheduling flexibility is mainly ensured by the reservoir storage capacity of the hydropower station. The backup plan constraints during the pre-season phase are shown in Equation (7):

[0030]

[0031] Mode (7) In the context, the scene represents a time period s on a timescale of d, where D is the total number of days in the water season. The reserve capacity of the integrated power station n is the hydropower station's capacity. The maximum output of the integrated power station n is the hydropower station. The power output of the integrated power station n is λ n The maintenance reserve coefficient for integrated power station n is given. During the pre-season scheduling phase, the output deviation of wind and solar resources is smoothed out by long-term statistical characteristics, and its impact is relatively small. During the scheduling phase, the main source of error is the seasonal runoff deviation. Therefore, the focus of scheduling work is to carry out medium- and long-term scheduling planning for water resources. Through the complementary reservoir capacity of the cascade hydropower station reservoir group, the optimal scheduling of reservoirs and the efficient utilization of water resources throughout the year are ensured. In addition, relevant reserves are set up to cope with maintenance and ensure the stable operation of the system.

[0032] In steps 1-4), the following operational constraints are further set for the technical characteristics and equipment status of the source, grid, and load: Operational constraints for the integrated "wind-solar-hydro-storage" base on the source side: The output of the wind turbine is limited by its maximum and minimum output range, and power curtailment may occur during actual operation; In order to accurately describe this limitation, the following constraints are set for the wind turbine, as shown in equation (8):

[0033]

[0034] In equation (8), the scenario is a time scale of t and a time period of s. and These represent the minimum and maximum output of the wind turbine units in the integrated power station n, respectively. The grid-connected power of the wind turbine units in the integrated power station n. Let n be the curtailment power of the wind turbine in the integrated power station n; the output of the photovoltaic unit is also subject to similar constraints as the wind turbine; to accurately describe this limitation, the following constraints are set for the photovoltaic unit, as shown in equation (9):

[0035]

[0036] In equation (9): the scene is a time scale of t and a time period of s. and These are the minimum and maximum outputs of the photovoltaic units in the integrated power station n, respectively. For the grid-connected power of the photovoltaic units in the integrated power station n, Let n be the curtailment power of the photovoltaic units in the integrated power station n; the regulation capacity of the hydropower station is affected by various factors such as water volume and unit operating efficiency; to accurately describe this limitation, the following constraints are set for the hydropower station, as shown in equation (10):

[0037]

[0038] In equation (10), the scenario is a time scale of t and a time period of s. and These represent the maximum and minimum total discharge flows of the integrated power station n, respectively. and These represent the maximum and minimum power generation discharge flows of the integrated power station n. and These represent the maximum and minimum reservoir capacities of the integrated power station n. and Let n be the maximum and minimum head heights of the integrated power station n, respectively; the charging and discharging capacity of the energy storage unit depends on the charging state of the battery and the capacity of the energy storage device. To accurately describe this limitation, the following constraints are set for the energy storage unit, as shown in equation (11):

[0039]

[0040] In equation (11), the scenario is a time period s on the time scale t. Considering that the energy storage unit cannot perform charging and discharging operations at the same time, constraints are imposed on it; in addition, its energy storage capacity is subject to certain limitations. and These represent the maximum and minimum capacities of the energy storage units in the integrated power station n, respectively. Finally, to ensure the continuity and stability of the dispatching process, the energy storage status during the pre-season dispatching phase is constrained, and the initial energy storage capacity of the energy storage units is set to the rated capacity. 50％ S represents the total number of scheduling periods; network-side constraints include network security:

[0041] This invention treats the local topology network, DC tie-line channel, and receiving-end topology network as a large network, and ensures power supply security by setting power flow constraints. The specific steps are as follows: First, the system's node admittance matrix Y is obtained through a strategy. NOD,NOD NOD represents the total number of nodes in the system. A balanced node is selected, and the inverse of the column and row containing the balanced node is obtained. NOD-1,NOD-1Inserting all-zero columns and all-zero rows at the positions of the balance nodes yields matrix X. NOD,NOD Next, the generation shift distribution factor (GSDF) of each branch is calculated. For branches b and nod, b and nod are the circuit branch and node indices of the system, respectively, b∈[1,B], nod∈[1,NOD], and B is the total number of branches. The power shift distribution factor is obtained by formula (12):

[0042] GS(b,nod)=(X(head b ,nod)-X(end b ,nod))*XK b (12)

[0043] In equation (12), GS(b,nod) represents the power transfer factor matrix of node nod on branch b, and head b and end b These are the first and last nodes of branch b, XK. b The impedance of branch b is represented by the power transfer distribution factor matrix GS. B,NOD The constraints on the power of generator nodes and load nodes are set as shown in equation (13):

[0044]

[0045] In equation (13), the scenario is a time scale of t and a time period of s, P b,min and P b,max These represent the maximum and minimum power that branch b can withstand, respectively. and Let be the power transferred from the generator node and the load node to branch b, respectively, where gen is the generator node index (gen∈(1,GEN)) and l is the load node index (l∈(1,L)). and The output power of the generator node and the demand power of the load node are respectively; the method analyzes the power network based on the power transfer distribution factor, calculates the impact of generator nodes and load nodes on power transfer, and sets corresponding constraints to ensure that the power flow always remains within the normal range of the branch carrying capacity, thereby ensuring the stability and security of the power system; the load side mainly constrains the local load, including elastic load transfer constraints, as shown in equation (14):

[0046]

[0047] In equation (14), the scenario is a time scale of t and a time period of s. For the original load demand, To account for the load demand after participating in flexible load transfer, For the flexible load transfer, the local load demand must remain unchanged before and after the transfer; the source, grid, and load must jointly comply with the power balance constraint to ensure the stable operation of the system, as shown in equation (15):

[0048]

[0049] In equation (15), and The system purchases and transmits power through DC interconnection channels. At different time scales, the system must meet the dynamic balance between power generation output and load demand to avoid the fluctuation risk caused by power supply and demand imbalance. If the system experiences load loss or large-scale wind and solar curtailment within a certain time scale, it indicates that the current dispatch strategy cannot effectively adapt to the uncertainty of wind and solar power output and load fluctuations. Therefore, it is necessary to further optimize the dispatch scheme with narrow time scales. This can be achieved by adjusting the water volume regulation strategy of hydropower stations, optimizing the charging and discharging plan of energy storage units, and rationally arranging the transfer of flexible loads in local loads. This will improve the system's adaptability to fluctuations in wind and solar power output and ensure the stability and reliability of power supply.

[0050] In steps 1-5), the objective function used is shown in equation (16):

[0051]

[0052] In equation (16), the scenario is a time scale of t and a time period of s, F t For the overall system operating cost, For the operating costs of the hydropower station, For the operating costs of energy storage units, To incur penalties for wind and solar power curtailment For flexible load transfer costs, For DC-DC interconnection channel power adjustment costs, This is the power generation cost coefficient for hydropower stations. and These are the power generation cost coefficient and the energy storage cost coefficient for energy storage units, respectively. This is the energy storage operating cost coefficient for the energy storage unit. and These are the penalty cost coefficients for wind and solar power curtailment. This is the flexible load transfer cost coefficient. and These are the cost coefficients for purchased and transmitted electricity, respectively.

[0053] In step 2, the day-ahead scheduling phase includes the following steps: Step 2-1) Based on the historical operation data and load history data of the integrated wind-solar-hydro-storage base, multi-source data prediction is performed to construct a prediction dataset including wind speed, solar intensity, temperature, runoff and load. Wind power predicted output data is calculated based on wind speed, and solar power predicted output data is calculated based on solar intensity and temperature. Finally, the watershed runoff prediction data, wind and solar power predicted output data and load prediction data of the day-ahead scheduling phase are used as inputs for step 2.

[0054] Step 2-2) Based on the watershed runoff forecast data, wind and solar power output forecast data and load forecast data generated in Step 2-1), perform statistical analysis of day-ahead forecast errors, construct the corresponding forecast error distribution, determine the error range of the input data in the day-ahead scheduling stage to determine the degree of various forecast errors, and then determine the relative error of the input data in the day-ahead scheduling stage; Step 2-3) Based on the relative error of the input data in the day-ahead scheduling stage determined in Step 2-2), in order to cope with the power output fluctuations and forecast uncertainties of renewable energy such as wind and solar in the day-ahead stage, a backup plan is set for the system during the scheduling process to improve the flexibility and security of the system in response to sudden fluctuations. A backup capacity setting mechanism is introduced, utilizing the reservoir capacity of hydropower stations and energy storage units, the transmission capacity of DC interconnection channels and the transferability of flexible loads to improve the flexibility of system scheduling. The backup plan constraints in the day-ahead scheduling stage are set as shown in Equation (17):

[0055]

[0056] In equation (17), the scenario is the s-period at the h-timescale. and These represent the maximum and minimum ramp rates of the integrated power station n. During the day-ahead dispatch phase, due to the large climate prediction errors and inherent volatility of natural resources such as water, wind, and solar power, climate prediction deviation becomes the main source of error. The core of the dispatch work is to smooth out the output fluctuations of the integrated base and improve the utilization efficiency of wind and solar resources. Based on the above, in the day-ahead dispatch, through the pre-allocation of capacity of energy storage units and hydropower stations, as well as the optimized dispatch of DC interconnection channels, output smoothing and efficient utilization of wind and solar resources are achieved, while enhancing the system's ability to cope with sudden load increases. In addition, it is necessary to formulate a flexible load transfer plan to provide new load supply schemes for intraday dispatch, so as to further improve the overall control capability of the system. The strategy analyzes the load loss rate of each period and uses a price mechanism to stimulate flexible load transfer, adjusting the load of periods with higher load loss rates to periods with more wind and solar curtailment, so as to optimize the load distribution and improve the level of new energy consumption.

[0057] Steps 2-4) Based on the reserve plan generated in Step 2-3) for the day-ahead scheduling phase, and in conjunction with the relevant operational constraints of the system during the day-ahead scheduling phase, the corresponding operational constraints are set according to the technical characteristics of the source, grid, and load, and the equipment status, as follows:

[0058] On the source side, there are constraints on the integrated "wind-solar-hydro-storage" base, including upper limit constraints on wind and solar power output, power output constraints and water balance constraints on hydropower stations, and charging and discharging power constraints, state of charge constraints and charging and discharging efficiency constraints on energy storage units.

[0059] Based on the aforementioned constraints on generating units, the day-ahead dispatching phase further introduces constraints on the start-up and shutdown characteristics and ramp-up capabilities of hydropower stations. The relevant constraint formulas are shown in equation (18):

[0060]

[0061] Mode (18) In the scenario, the time scale is t and the time interval is s, where Td and Tu are the minimum shutdown and startup times, respectively. The working state of the integrated power station n is 0 for shutdown and 1 for startup. To ensure that the power ramp-up does not exceed the limit during operation, the ramp-up rate of the unit is limited. In addition, considering that frequent start-up and shutdown will increase operating costs and aggravate mechanical wear of equipment, affecting the service life and operational stability of the unit, the start-up and shutdown time of the unit is constrained to reduce unnecessary start-up and shutdown operations and improve the reliability of the unit.

[0062] Network security constraints are imposed on the network side, including power flow constraints and capacity limits for the local topology network, DC tie-in channels and receiving-end topology network, and power boundary constraints for DC tie-in channels;

[0063] The load side is subject to elastic load transfer constraints, including total conservation constraints before and after the elastic load transfer of local loads;

[0064] Based on the above load transfer constraints, in order to prevent large-scale load transfer from impacting the operation of the power system and causing problems such as system frequency fluctuations or dispatch instability, it is necessary to reasonably limit the amount of flexible load transfer; the corresponding constraint model is shown in equation (19):

[0065]

[0066] In equation (19), the scenario is the time period s under the time scale h, and β is the proportion of load that can participate in flexible load transfer. The maximum load capacity that can participate in the response within the specified time period is limited. The adjusted flexible load should be controlled within a reasonable range to ensure the feasibility of load adjustment and the stability of power grid operation. Power balance constraints should be followed between power sources, grids, and loads.

[0067] Step 2-5): Based on the relevant constraint set established in Steps 2-1) to 2-4), the solution is performed with the goal of minimizing the overall operating cost of the system. The overall operating cost of the system includes the operating cost of the hydropower station, the operating cost of the energy storage unit, the cost of wind and solar curtailment, the cost of wind and solar curtailment penalty, the cost of flexible load transfer, and the cost of DC interconnection channel power adjustment. The operating cost of the hydropower station includes the power generation cost of the hydropower station. The operating cost of the energy storage unit includes the power generation cost, the energy storage cost, and the energy storage operation cost of the energy storage unit. The cost of wind and solar curtailment includes the cost of wind power curtailment and the cost of solar power curtailment. The cost of DC interconnection channel power adjustment includes the cost of purchasing and transmitting electricity. The objective function is shown in Equation (20):

[0068]

[0069] In equation (20), the scenario is a time scale t and a time period s. The start-up and shutdown cost coefficient for integrated power station n is given; the start-up and shutdown cost of hydropower stations is further considered during the daytime dispatch phase to reduce meaningless start-up and shutdown operations of hydropower stations.

[0070] Steps 2-6): Based on the solution results of Steps 2-5), formulate the power output configuration plan for the integrated "wind-solar-hydro-storage" base, and determine the configuration of purchased and transmitted electricity. Further, conduct a comprehensive analysis of the load shedding rate and curtailment rate for each time period, and use electricity price signals to further guide the transfer of flexible loads, prompting the load to purposefully shift from periods with high load shedding rates to off-peak periods with large amounts of wind and solar curtailment. Based on the adjusted load distribution, reconstruct the corrected load demand curve, conduct production time series simulation, and then generate an updated day-ahead scheduling plan. This provides a water allocation plan, power output constraint boundaries, and a corrected load supply curve for Step 3, thus completing the execution of Step 2.

[0071] In step 3, the intraday scheduling phase includes the following steps:

[0072] Step 3-1) Based on the historical operation data and load history data of the integrated "wind-solar-water-storage" base, multi-source data prediction is carried out to construct a prediction dataset including wind speed, light intensity, temperature, runoff and load. Wind power predicted output data is calculated based on wind speed, and solar power predicted output data is calculated based on light intensity and temperature. Finally, watershed runoff prediction data, wind and solar power predicted output data and load prediction data are used as inputs for Step 3.

[0073] Step 3-2) Based on the watershed runoff forecast data, wind and solar power output forecast data and load forecast data generated in Step 3-1), perform intraday forecast error statistical analysis, construct the corresponding forecast error distribution, determine the error range of the input data in the intraday scheduling stage to determine the degree of various forecast errors, and then determine the relative error of the input data in the intraday scheduling stage; Step 3-3) Based on the relative error of the input data in the intraday scheduling stage determined in Step 3-2), consider the fluctuation and uncertainty of the output of water, wind and solar resources in the intraday scheduling stage, as well as the possibility of short-term load surges, and set up a backup plan for the system during the scheduling process to improve the system's response capability to short-term fluctuations in source load. The method utilizes the rapid adjustment characteristics of energy storage units and hydropower stations, combined with the optimized scheduling of DC interconnection channels, and sets the backup plan constraints for the intraday scheduling stage as shown in Equation (21):

[0074]

[0075] In Equation (21), the scenario is the s-period at the q-time scale, μ is the load fluctuation coefficient, and γ is the maximum vibration amplitude coefficient of the hydropower station and energy storage unit at the q-scale. The main uncertainty in the intraday dispatching phase comes from short-term meteorological disturbances and sudden load increases, which puts higher demands on the real-time response capability of the dispatching system. Based on the above analysis, the intraday dispatching phase relies on the rapid response characteristics of the hydropower station and energy storage unit, combined with the transmission capacity of the DC interconnection channel, to cope with the output fluctuations on both sides of the source and load, and to ensure the safety and flexibility of the system operation; Step 3-4) Based on the standby plan for the day-ahead dispatching phase generated in Step 3-3), combined with the system's daily... The relevant operational constraints during the pre-dispatch phase are set according to the technical characteristics and equipment status of the power source, grid, and load, as follows: On the source side, these include upper limit constraints on wind and solar power output, start-up and shutdown constraints, output constraints, ramp-up rate constraints, and water balance constraints for hydropower stations, and charging / discharging power constraints, state-of-charge constraints, and charging / discharging efficiency constraints for energy storage units, constituting a constraint set for the integrated "wind-solar-hydro-storage" base; on the grid side, these include network security constraints, including power flow constraints and capacity limits for the local topology network, DC interconnection channels, and receiving-end topology network, as well as power boundary constraints for DC interconnection channels; on the load side, these include the transferability of flexible loads within the local load. The load transfer ratio constraint, load transfer time constraint, load transfer quantity boundary constraint, and total quantity conservation constraint before and after load transfer constitute the flexible load transfer constraint; the power balance constraint applies between the source, grid, and load; steps 3-5) are based on the relevant constraint set established in steps 3-1) to 3-4), and the solution is performed with the goal of minimizing the overall system operating cost. The overall system operating cost includes the hydropower station operating cost, energy storage unit operating cost, wind and solar curtailment cost, wind and solar curtailment penalty cost, flexible load transfer cost, and DC interconnection channel power adjustment cost. The hydropower station operating cost includes the hydropower station generation cost and the hydropower station start-up and shutdown cost. The unit operating cost includes the power generation cost, energy storage cost, and energy storage operation cost of the energy storage unit. The wind and solar curtailment cost includes the wind power curtailment cost and the solar power curtailment cost. The DC interconnection channel power adjustment cost includes the cost of purchasing and transmitting electricity. Steps 3-6) Based on the solution results of Steps 3-5), compare the day-ahead dispatch plan, adjust the output boundaries of hydropower stations and energy storage units, generate hydropower station adjustment plans and energy storage unit adjustment output plans, and combine the inertia characteristics of various power sources to determine the configuration of purchased and transmitted electricity, forming an intraday dispatch plan, providing power source inertia, output boundary constraints, and demand load curves for Step 4.

[0076] In step 4, the real-time scheduling phase includes the following steps:

[0077] Step 4-1): Based on the real-time monitoring data and load detection data of the integrated "wind-solar-water-storage" base, construct a real-time monitoring dataset including wind speed, light intensity, temperature, runoff, and load. Calculate wind power output data based on wind speed, and calculate solar power output data based on light intensity and temperature. This results in watershed runoff monitoring data, wind and solar power output data, and load monitoring data, which together serve as input for Step 4. Step 4-2): Based on the watershed runoff monitoring data, wind and solar power output data, and load monitoring data generated in Step 4-1), perform real-time monitoring error statistical analysis, construct a corresponding prediction error distribution, determine the error range of the input data in the real-time scheduling stage to determine the degree of various prediction errors, and then determine the relative error of the input data in the real-time scheduling stage. Step 4-3): Based on... The relative error of the input data in the real-time scheduling stage determined in step 4-2) is considered. The wind and solar fluctuations at the minute level in the real-time scheduling stage are taken into account. The core of the scheduling work is to smooth out these rapid fluctuations. Considering that the hydropower station cannot quickly adjust its output at the minute level, in order to improve the system's response capability to short-term fluctuations of source load, the inertia of each unit is used to resist short-term disturbances. Based on the inertia information of each unit transmitted in the intraday scheduling stage, the system prioritizes the use of inertia characteristics for anti-interference adjustment. Based on the inertia information of each unit transmitted in the intraday scheduling stage, anti-interference is carried out. If the inertia response is insufficient to smooth out the fluctuations, the energy storage unit is activated for further adjustment. If the energy storage still cannot completely absorb the disturbances, the power is adjusted through the DC interconnection channel to achieve the final stability of the power grid. The inertia model of each unit is established as follows: The rotating standby model of the hydropower station is shown in equation (22):

[0078]

[0079] In equation (22), during the time period s on the m-timescale, This indicates the change in power released or absorbed by the integrated power station n during the real-time dispatch phase due to inertial response. Let n be the unit inertia constant of the integrated power station n. This represents the base capacity of the integrated power station n. Indicates the rate of change of system frequency. This represents the kinetic energy of the rotating components of an integrated hydropower station (n). This represents the rated capacity of the integrated power station n. Let n be the moment of inertia of the generator rotor in a hydropower station with integrated power plant n. Let n be the angular velocity of the generator rotor of the integrated power station n; the virtual inertia model of the wind turbine is shown in equation (23):

[0080]

[0081] In equation (23), the time period s is at the m-time scale. This represents the power change provided by the virtual inertia of the wind turbine units in the integrated power station n during the real-time scheduling phase. Let represent the virtual inertia constant of the wind turbine unit in the integrated power station n. The base capacity of the wind turbine unit in the integrated power station n is represented by the following formula (24):

[0082]

[0083] In equation (24), during the s time period at the m time scale, This represents the power change provided by the virtual inertia of the photovoltaic units in the integrated power station n during the real-time scheduling phase. Let represent the virtual inertia constant of the photovoltaic units in an integrated power station. Let represent the base capacity of the photovoltaic units in the n-integrated power station; considering that the rotational inertia and virtual inertia of each power source are subject to certain limitations during operation, constraints are established for them, as shown in equation (25):

[0084]

[0085] In equation (25), during the time period s at the time scale m, em = w, wd, and pv represent hydropower, wind power, and photovoltaic power, respectively. This represents the power variation provided by the rotational inertia or virtual inertia of each power source. and These represent the maximum and minimum net output of each power source. Provide power to each power source;

[0086] Step 4-4): Based on the inertia models of each unit in the real-time scheduling phase provided in Step 4-3), and combined with the relevant operational constraints provided by the system in the intraday scheduling phase, the following operational constraints are set for the technical characteristics and equipment status of the source, grid, and load: On the source side, these include upper limit constraints on wind and solar power output, start-up and shutdown constraints, output constraints, ramp-up rate constraints, and water balance constraints for hydropower stations, and charging / discharging power constraints, state-of-charge constraints, and charging / discharging efficiency constraints for energy storage units, constituting a constraint set for the integrated "wind-solar-hydro-storage" base; on the grid side, these include network security constraints, including power flow constraints and capacity limits for the local topology network, DC interconnection channels, and receiving-end topology network, and power boundary constraints for DC interconnection channels; on the load side, these include constraints on the proportion of flexible load transfer, load transfer time constraints, and load transfer quantity boundaries. The constraints on the total amount of load before and after load transfer constitute the flexible load transfer constraint; the power balance constraint between the source, grid and load follows; in step 4-5), based on the relevant constraint set established in steps 4-1) to 4-4), the solution is performed with the goal of minimizing the comprehensive operating cost of the system. The comprehensive operating cost of the system includes the operating cost of hydropower stations, the operating cost of energy storage units, the cost of wind and solar curtailment, the cost of wind and solar curtailment penalty, the cost of flexible load transfer, and the power adjustment cost of DC interconnection channels. The operating cost of hydropower stations includes the power generation cost and the start-up and shutdown cost of hydropower stations. The operating cost of energy storage units includes the power generation cost, the energy storage cost, and the energy storage operation cost of energy storage units. The cost of wind and solar curtailment includes the cost of wind power curtailment and the cost of solar power curtailment. The power adjustment cost of DC interconnection channels includes the cost of purchasing and transmitting electricity.

[0087] Steps 4-6): Based on the solution results of Steps 4-5), compare the intraday scheduling plan, adjust the output boundaries of hydropower stations and energy storage units, generate hydropower station adjustment plans and energy storage unit adjustment output plans, combine the inertia characteristics of various power sources, determine the configuration of purchased power and transmitted power, form a real-time scheduling plan, and provide decision-making for real-time scheduling.

[0088] This invention also includes a power supply system for a combined wind-solar-hydro-storage DC interconnection channel. This system consists of an integrated wind-solar-hydro-storage power generation base, a power transmission channel network, and a receiving-end power grid, with the three components working together to form a system-level coupling. The integrated wind-solar-hydro-storage power generation base is based on multi-stage cascade hydropower stations, with each stage of hydropower stations arranged sequentially from upstream to downstream according to the natural water flow. Each stage of hydropower station is equipped with wind turbines, photovoltaic units, and energy storage units. Each stage of hydropower station and its supporting wind turbines, photovoltaic units, and energy storage units together constitute an integrated power station. Multiple cascade hydropower stations and their auxiliary power stations further form multiple integrated power station units, ultimately integrating to form a complete integrated wind-solar-hydro-storage power generation base. In this system, various power sources are first connected to the local AC collection bus of their respective power stations. After being stepped up or connected to the grid, they are then uniformly connected to the main AC collection bus within the base, realizing the physical integration and synchronous regulation of multi-source power, and finally supplying power to the power transmission channel network.

[0089] The power transmission network includes a local topology network, local loads, transformer 1, rectifier system 1, rectifier system 2, transformer 2, and a DC interconnection channel. The main AC busbar within the integrated wind-solar-hydro-storage power generation base is connected to the local topology network and the low-voltage side of transformer 1. The local topology network directly supplies power to local loads, meeting the regional electricity demand. Transformer 1 steps up the low-voltage AC to high-voltage AC and outputs it from the high-voltage side to the rectifier system 1 via the DC interconnection channel. The rectifier system 1 further converts the high-voltage AC energy into high-voltage DC energy, which is then transmitted to the receiving area via the DC interconnection channel. The DC energy first enters the rectifier system 2, where the converter converts the high-voltage DC energy into high-voltage AC energy. It then connects to the high-voltage side of transformer 2, is stepped down, and outputs to the receiving-end grid busbar, achieving long-distance power transmission. In addition, when local renewable energy output is insufficient or the system requires external support, the receiving area can also reverse the power transmission to the local topology network via the DC interconnection channel. The power transmission process is the reverse of the above process, used to supplement local load demand or participate in energy optimization scheduling.

[0090] The receiving-end power grid includes receiving-end loads, receiving-end topology network, and receiving-end power source. The receiving-end power grid bus collects the AC power output from transformer 2 and connects it together with the local power provided by the receiving-end power source to the receiving-end topology network. The receiving-end topology network coordinates and distributes the power and transmits it to the receiving-end load in a unified manner, realizing the integrated power supply of remote power and local power source.

[0091] Compared with the prior art, the present invention has the following technical effects:

[0092] 1) Based on the energy complementarity characteristics of the integrated "wind-solar-hydro-storage" base, this invention considers the source-load prediction error at multiple time scales, combines the adjustment response speed characteristics of source, grid and load at each time scale, and alleviates the problem of power curtailment under the background of large-scale new energy access by coordinating and scheduling resources on the three sides of source, grid and load, thereby improving the system operation efficiency.

[0093] 2) This invention constructs a four-layer rolling scheduling mechanism of pre-season, pre-day, intra-day, and real-time, which fully considers the differences in time scale and output characteristics of water, wind, and solar resources. Each scheduling stage is interconnected and refined layer by layer, which improves scheduling accuracy and flexibility, reduces the impact of new energy fluctuations on system stability, and enhances the real-time matching capability of source and load.

[0094] 3) This invention achieves coordinated operation of different regulation resources across multiple time scales, dynamically optimizes resource allocation based on load and new energy output forecast information, reduces unit start-up and shutdown frequency and operating pressure, extends equipment service life, and improves overall operating economy;

[0095] 4) This invention sets power constraints on each link of the source, grid, and load to ensure that the power flow of each node meets the safe operation limit. It considers the upper and lower limits of the power of each component in the network, coordinates and controls the power flow in the power transmission path, and prevents equipment damage, voltage overruns, or system instability caused by overload or reverse power flow, thereby improving the controllability and safety margin of the entire system operation. Attached Figure Description

[0096] The present invention will be further described below with reference to the accompanying drawings and embodiments:

[0097] Figure 1 This is a schematic diagram of the integrated power supply system for the "wind-solar-hydro-storage" base in this invention;

[0098] Figure 2 This is a flowchart illustrating the nested guidance process across multiple time scales: pre-season, day-day, intraday, and real-time.

[0099] Figure 3 This is a schematic diagram of the overall process of the present invention;

[0100] Figure 4 This is a schematic diagram of the scheduling situation during the pre-season scheduling phase in an embodiment of the present invention;

[0101] Figure 5 This is a schematic diagram of the scheduling situation during the day-ahead scheduling phase in an embodiment of the present invention;

[0102] Figure 6 This is a schematic diagram of the scheduling situation during the intraday scheduling phase in an embodiment of the present invention;

[0103] Figure 7 This is a schematic diagram of the real-time scheduling phase in an embodiment of the present invention. Detailed Implementation

[0104] This invention provides a source-grid-load coordinated scheduling method based on multi-timescale coupling. The purpose is to compensate for errors in the source-load forecasting process by coordinating and scheduling resources on the source, grid and load sides, thereby enhancing the system's ability to cope with wind and solar load fluctuations and improving the system's utilization rate of new energy sources.

[0105] like Figure 2 As shown, this method specifically includes a pre-season scheduling phase, a day-ahead scheduling phase, an intraday scheduling phase, and a real-time scheduling phase, and specifically includes the following steps:

[0106] Step 1: Execute pre-season scheduling. Based on the annual distribution characteristics of water resources, the year is divided into three typical water seasons: high-water season, normal-water season, and low-water season. Three pre-season scheduling operations are performed annually. Considering the seasonal characteristics of new energy sources, one pre-season scheduling operation is performed for each water season. The scheduling time resolution is 1 day, and the pre-season scheduling plan is output. Step 2: Based on the pre-season scheduling plan output in Step 1, and combined with the next day's load forecast and new energy output forecast results, one day-ahead scheduling operation is performed daily. The scheduling time resolution is 1 hour, and the day-ahead scheduling plan is output. Step 3: Based on the day-ahead scheduling plan output in Step 2, and combined with intraday updated meteorological and load forecast information, intraday scheduling is performed every 4 hours on a rolling basis. The scheduling time resolution is 15 minutes, and the intraday scheduling plan is output. Step 4: Based on the intraday scheduling plan output in Step 3, and combined with real-time monitoring data, real-time scheduling is performed every 15 minutes on a rolling basis. The scheduling time resolution is 1 minute, and the real-time scheduling plan is output. (See also...) Figure 3 In step 1, the pre-season scheduling phase includes the following steps: Step 1-1) Based on the historical operation data and load history data of the integrated wind-solar-water-storage base, analyze its typical seasonal variation patterns, summarize and construct historical datasets including wind speed, solar intensity, temperature, runoff, and load, substitute historical wind speed data into equation (1) to calculate historical wind power output data; substitute historical solar intensity and temperature data into equation (2) to calculate historical solar power output data. The final obtained watershed runoff data, historical wind and solar power output data, and historical load data together constitute the input of step 1. The model formulas involved are as follows:

[0107] The uncertain output model of wind turbine 4 in the system is shown in equation (1):

[0108]

[0109] In equation (1), t = d, h, q, m represents different time scales, including four time granularities: day, hour, quarter-hour, and minute. s is the time period index at time scale t, s∈[1,S], where S is the total number of scheduling time periods at time scale t, the scenario is time period s at time scale t, and n is the integrated power station index, n∈[1,N], where N is the total number of integrated power stations. The wind turbine 4 of the integrated power station n provides power output. Let n be the wind speed of the integrated power station. These are the cut-in wind speed, rated wind speed, and cut-out wind speed of wind turbine 4 in the integrated power station n. The rated power of wind turbine 4 in the integrated power station n.

[0110] The output model of photovoltaic unit 5 in the system is shown in equation (2):

[0111]

[0112] In equation (2), the scenario is a time scale of t and a time period of s. The photovoltaic unit 5 of the integrated power station n provides power output. The rated power of photovoltaic unit 5 in integrated power station n. and These represent the surface shortwave incident radiation capacity and solar radiation intensity under standard test conditions of photovoltaic unit 5 in integrated power station n, respectively. The temperature coefficient of output power of the solar cell modules of photovoltaic unit 5 in integrated power station n. and These are the module temperature and air temperature of photovoltaic unit 5 in the integrated power station n, respectively. and These represent the temperatures of the photovoltaic unit 5 components in the integrated power station n under standard test conditions and normal test conditions, respectively.

[0113] Step 1-2) The formulation of the pre-season scheduling plan is based on the historical dataset generated in step 1-1). The errors between the watershed runoff data, wind and solar historical data, and load historical data and the actual data are statistically analyzed to construct the corresponding historical data error distribution. The input data error range of water, wind, solar and load in the pre-season scheduling stage is determined to determine the degree of input data error. Specifically, the strategy uses relative error analysis to analyze the accuracy of water, wind and solar power output and load demand. The relative error formula is shown in equation (3):

[0114]

[0115] In equation (3), the scenario is a time scale of t and a time period of s. The relative error between the input data sample and the actual data is represented by i, where i ∈ [1, I], I is the total number of samples, and k = w, wd, pv, ld represent runoff data, wind power data, solar power data, and load data, respectively. This represents the input data sample data. Represents actual data.

[0116] Based on the quantization results of the relative error, the error probability distribution of various input data is constructed, as shown in Equation (4):

[0117]

[0118] make As the relative error used to establish input data error constraints at the time scale t.

[0119] Steps 1-3) Based on the relative error of the input data determined in Step 1-2), and combined with the regulation capabilities of the power source, grid, and load, this step comprehensively utilizes the adjustability of hydropower station 6, the charging and discharging flexibility of energy storage unit 7, the transmission capacity of the DC interconnection channel, and the regulation characteristics of flexible loads to reduce the uncertainty of wind and solar power output, enhance the system's ability to cope with fluctuations in wind and solar power output, and further improve the absorption and utilization efficiency of wind and solar resources. The relevant model formulas are shown below:

[0120] The output and reservoir capacity model of the cascade hydropower station 6 in the system is shown in equation (5):

[0121]

[0122] In equation (5), the scenario is a time scale of t and a time period of s. and These represent the total downstream discharge and inflow of hydropower station 6, which is an integrated power station n. and These are the power generation discharge and water abandonment discharge of hydropower station 6, which are integrated power station n and integrated power station n, respectively. This indicates the output of hydropower station 6, which represents integrated power station n. The comprehensive output coefficient of hydropower station 6 is the integrated power station n. The water head, reservoir level, tailrace level, and head loss height of hydropower station 6 represent the integrated power station n, respectively. The reservoir capacity of the integrated power station n hydropower station 6, and These are the coefficients of each term in the reservoir capacity-water level-storage capacity relationship curve of the integrated power station n hydropower station 6, which are fitted using polynomial fitting. and These are the coefficients of each term in the polynomial fitting curve of the tailwater level of the reservoir and the total outflow of the integrated power station n hydropower station 6, and Δs is the scheduling time interval.

[0123] The output and energy storage capacity model of energy storage unit 7 in the system are shown in equation (6):

[0124]

[0125] In equation (6), the scenario is a time scale of t and a time period of s. This refers to the energy storage capacity of energy storage unit 7 in the integrated power station n. and These represent the charging and discharging efficiencies of the energy storage unit 7 in the integrated power station n. and These represent the charging and discharging power of the energy storage unit 7 in the integrated power station n. and These represent the minimum and maximum energy storage capacities of the energy storage unit 7 in the integrated power station n, respectively. and These are the minimum and maximum charging power of the energy storage unit 7 in the integrated power station n, respectively. and These represent the minimum and maximum discharge power of the integrated power station n, respectively.

[0126] During the pre-season scheduling phase, in order to cope with the power station equipment maintenance plan, a corresponding backup plan needs to be preset. Considering the regulation capacity of the source, grid and load sides and the seasonal characteristics of wind, solar and water resources, the system scheduling flexibility is mainly ensured by the reservoir reserve capacity of the cascade hydropower station 6. The backup plan constraints during the pre-season phase are shown in equation (7):

[0127]

[0128] Mode (7) In the context, the scene represents a time period s on a timescale of d, where D is the total number of days in the water season. The reserve capacity of hydropower station 6 is for integrated power station n. The maximum output of hydropower station 6 is the integrated power station n. For the integrated power station n, the hydropower station 6 output, λ n The maintenance reserve factor for hydropower station 6 is the integrated power station n. During the pre-season scheduling phase, the output deviation of wind and solar resources is smoothed out by long-term statistical characteristics, and the impact is relatively small. During the scheduling phase, the main source of error is the seasonal runoff deviation. Therefore, the focus of the scheduling work is to carry out medium- and long-term scheduling planning for water resources. Through the reservoir capacity complementarity of the cascade hydropower station 6 reservoir group, the optimal scheduling of reservoirs and the efficient utilization of water resources throughout the year are ensured. In addition, relevant reserves are set up to cope with maintenance and ensure the stable operation of the system.

[0129] Steps 1-4) Based on the reserve plan generated in Steps 1-3) for the pre-season scheduling phase, and considering the actual operating status of the system, to ensure the safe and stable operation of the system, the following corresponding operating constraints are set for the technical characteristics and equipment status of the source, grid, and load:

[0130] Operational constraints of the integrated "wind-solar-hydro-storage" base on the source side: The output of wind turbine 4 is limited by its maximum and minimum output range, and power curtailment may occur during actual operation. To accurately describe this limitation, the following constraints are set for wind turbine 4, as shown in equation (8):

[0131]

[0132] In equation (8), the scenario is a time scale of t and a time period of s. and These represent the minimum and maximum output of wind turbine 4 in the integrated power station n, respectively. The on-grid power of wind turbine 4 in integrated power station n. Let be the curtailment power of wind turbine 4 in the integrated power station n. The output of photovoltaic unit 5 is also subject to similar constraints as wind turbine 4. To accurately describe this constraint, the following constraints are set for photovoltaic unit 5, as shown in equation (9):

[0133]

[0134] In equation (6): the scene is a time scale of t and a time period of s. and These are the minimum and maximum outputs of photovoltaic unit 5 in the integrated power station n, respectively. The grid-connected power of photovoltaic unit 5 in integrated power station n. The curtailment power of photovoltaic unit 5 in integrated power station n.

[0135] The regulation capacity of hydropower station 6 is affected by various factors such as water volume and unit operating efficiency. To accurately describe this limitation, the following constraints are set for hydropower station 6, as shown in equation (10):

[0136]

[0137] In equation (10), the scenario is a time scale of t and a time period of s. and These represent the maximum and minimum total discharge flows of hydropower station 6, which are integrated power station n and 6, respectively. and These represent the maximum and minimum power generation discharge flows of hydropower station 6, which are integrated power station n and integrated power station n, respectively. and These refer to the maximum and minimum reservoir capacities of the integrated power station n and the hydropower station 6, respectively. and These represent the maximum and minimum head heights of hydropower station 6, which are integrated power station n and hydropower station 6, respectively.

[0138] The charging and discharging capability of the energy storage unit 7 depends on the state of charge of the battery and the capacity of the energy storage device. To accurately describe this limitation, the following constraints are set for the energy storage unit 7, as shown in equation (11):

[0139]

[0140] In equation (11), the scenario is a time period s on the time scale t. Considering that the energy storage unit 7 cannot perform charging and discharging operations at the same time, constraints are imposed on it. In addition, its energy storage capacity is subject to certain limitations. and These are the maximum and minimum values ​​of the capacity of energy storage unit 7 in integrated power station n, respectively. Finally, to ensure the continuity and stability of the dispatching process, the energy storage status during the pre-season dispatching phase is constrained, and the initial energy storage capacity of energy storage unit 7 is set to 50% of the rated capacity.

[0141] Network side includes network security constraints:

[0142] This invention treats the local topology network 8, the DC tie channel, and the receiving-end topology network 15 as a large network, and ensures power supply security by setting power flow constraints. The specific steps are as follows: First, obtain the node admittance matrix Y of the system. NOD,NOD NOD represents the total number of nodes in the system. A balanced node is selected, and the inverse of the column and row containing the balanced node is obtained. NOD-1,NOD-1 Inserting all-zero columns and all-zero rows at the positions of the balance nodes yields matrix X. NOD,NOD Next, the generation shift distribution factor (GSDF) of each branch is calculated for branch b and node nod, where b and nod are the circuit branch and node indices of the system, respectively, b∈[1,B], nod∈[1,NOD], and B is the total number of branches. The power shift distribution factor is obtained through formula (12):

[0143] GS(b,nod)=(X(head b ,nod)-X(end b ,nod))*XK b (12)

[0144] In equation (12), GS(b,nod) represents the power transfer factor matrix of node nod on branch b, and head b and endb The first and last sections of branch b are respectively

[0145] Point, XK b The impedance of branch b is represented by the power transfer distribution factor matrix GS. B,NOD The constraints on the power of generator nodes and load nodes are set as shown in equation (13):

[0146]

[0147] In equation (13), the scenario is a time scale of t and a time period of s, P b,min and P b,max These represent the maximum and minimum power that branch b can withstand, respectively. and Let be the power transferred from the generator node and the load node to branch b, respectively, where gen is the generator node index, gen∈(1,GEN), and GEN is the generator node index.

[0148] The total number of electrical nodes, where l is the load node index, l∈(1,L). and These represent the output power of the generator node and the demand power of the load node, respectively; the strategy is based on...

[0149] The power transfer distribution factor is used to analyze the power network, calculate the impact of generator nodes and load nodes on power transfer, and establish corresponding constraints to ensure that the power flow is always maintained.

[0150] By keeping the branch line within its normal carrying capacity, the stability and security of the power system can be guaranteed.

[0151] The load side mainly constrains the local load 9, including elastic load transfer constraints, as shown in equation (14):

[0152]

[0153] In equation (14), the scenario is a time scale of t and a time period of s. For the original load demand, To account for the load demand after participating in flexible load transfer, For elasticity

[0154] The load transfer volume must ensure that the total local load demand remains unchanged before and after the transfer.

[0155] The power source, grid, and load must jointly comply with the power balance constraint to ensure stable system operation, as shown in equation (15):

[0156]

[0157] In equation (15), and The system needs to meet the power output requirements on the generation side at different time scales, namely, purchasing and transmitting power through DC interconnection channels.

[0158] Dynamic balance with load-side demand is crucial to avoid fluctuation risks caused by power supply-demand imbalances. If the system experiences load shedding or large-scale wind and solar power curtailment within a certain timescale, it indicates that...

[0159] The existing dispatching strategy cannot effectively adapt to the uncertainty of wind and solar power output and load fluctuations. Therefore, it is necessary to further optimize the narrow time-scale dispatching scheme by adjusting the water volume regulation strategy of the hydropower station.

[0160] By optimizing the charging and discharging plans of energy storage units 7 and rationally arranging the flexible load transfer in local load 9, the system's adaptability to fluctuations in wind and solar power output can be improved, ensuring stable power supply.

[0161] Qualitative and reliable.

[0162] Steps 1-5) Based on the relevant constraint set established in steps 1-1) to 1-4), the solution is performed with the goal of minimizing the overall operating cost of the system. The objective function is shown in equation (16):

[0163]

[0164] In equation (16), the scenario is a time scale of t and a time period of s, F t For the overall system operating cost, The operating cost of the hydropower station is 6. For the operating cost of energy storage unit 7, To incur penalties for wind and solar power curtailment For flexible load transfer costs, For DC-DC interconnection channel power adjustment costs, The power generation cost coefficient for hydropower stations is 6. and These are the power generation cost coefficient and energy storage cost coefficient for energy storage unit 7, respectively. This represents the energy storage operating cost coefficient for energy storage unit 7. and These are the penalty cost coefficients for wind and solar power curtailment. This is the flexible load transfer cost coefficient. and These are the cost coefficients for purchased and transmitted electricity, respectively.

[0165] Steps 1-6): Based on the solution results of Steps 1-5), generate the power output configuration plan of the integrated wind-solar-hydro-storage base, determine the configuration of purchased and transmitted electricity, form a pre-season scheduling plan, provide maintenance backup and water allocation plan for Step 2, and complete the execution of Step 1.

[0166] See Figure 3 In step 2, the day-ahead scheduling phase includes the following steps:

[0167] Step 2-1): Based on the historical operation data and load history data of the integrated wind-solar-hydro-storage base, multi-source data prediction is performed to construct a prediction dataset including wind speed, light intensity, temperature, runoff, and load. Wind power predicted output data is calculated based on wind speed, and photovoltaic predicted output data is calculated based on light intensity and temperature. Finally, the watershed runoff prediction data, wind and solar power predicted output data, and load prediction data for the day-ahead scheduling stage are used as inputs for Step 2.

[0168] Step 2-2): Based on the watershed runoff forecast data, wind and solar power output forecast data, and load forecast data generated in Step 2-1), perform statistical analysis of day-ahead forecast errors, construct the corresponding forecast error distribution, determine the error range of the input data in the day-ahead scheduling stage to determine the degree of various forecast errors, and then determine the relative error of the input data in the day-ahead scheduling stage.

[0169] Step 2-3) Based on the relative error of the input data in the day-ahead scheduling phase determined in Step 2-2), in order to cope with the output fluctuations and prediction uncertainties of renewable energy sources such as wind and solar power in the day-ahead phase, a backup plan is set for the system during the scheduling process to improve the system's flexibility and security in response to sudden fluctuations. A backup capacity setting mechanism is introduced, utilizing the reservoir storage capacity of hydropower station 6 and energy storage unit 7, the transmission capacity of DC interconnection channel, and the transferability of flexible loads to improve the flexibility of system scheduling. The backup plan constraints for the day-ahead scheduling phase are set as shown in Equation (17):

[0170]

[0171] In equation (17), the scenario is the s-period at the h-timescale. and These represent the maximum and minimum ramp rates of hydropower station 6, which are integrated power station n and 6, respectively. During day-ahead dispatch, due to the significant climate prediction errors and inherent volatility of natural resources such as water, wind, and solar power, climate prediction bias becomes the main source of error. The core of dispatch work lies in smoothing out power output fluctuations at the integrated base and improving the utilization efficiency of wind and solar resources. Based on the above, in day-ahead dispatch, through the pre-allocation of capacity of energy storage unit 7 and hydropower station 6, and the transmission capacity of DC interconnection channels, power output smoothing and efficient utilization of wind and solar resources are achieved, while enhancing the system's ability to cope with sudden load increases. Furthermore, a flexible load transfer plan needs to be formulated to provide new load supply schemes for intraday dispatch, further enhancing the overall control capability of the system. The strategy analyzes the load shedding rate for each period and uses a price mechanism to stimulate flexible load transfer, adjusting the load from periods with higher load shedding rates to periods with more wind and solar curtailment, thereby optimizing load distribution and improving the level of new energy consumption.

[0172] Steps 2-4): Based on the reserve plan generated in Step 2-3) for the day-ahead scheduling phase, and considering the relevant operational constraints of the system during the day-ahead scheduling phase, the following operational constraints are set for the technical characteristics and equipment status of the source, grid, and load:

[0173] The source side is subject to constraints on the integrated "wind-solar-hydro-storage" base, including upper limit constraints on wind and solar power output, output constraints and water balance constraints on hydropower station 6, and charging and discharging power constraints, state of charge constraints and charging and discharging efficiency constraints on energy storage unit 7.

[0174] Based on the aforementioned constraints on the generating units, the day-ahead dispatching phase further introduces constraints on the start-up and shutdown characteristics and ramping capability of hydropower station 6, and the relevant constraint formulas are shown in equation (18):

[0175]

[0176] Mode (18) In the scenario, the time scale is t and the time interval is s, where Td and Tu are the minimum shutdown and startup times, respectively. The diagram shows the operating status of hydropower station 6 in integrated power station n, with 0 representing shutdown and 1 representing startup. To ensure that hydropower station 6 does not exceed power ramp-up limits during operation, relevant limits are set for the ramp-up rate of hydropower station 6 units. In addition, considering that frequent start-ups and shutdowns will increase operating costs and accelerate mechanical wear of equipment, affecting the service life and operational stability of the units, the start-up and shutdown times of the units are constrained to reduce unnecessary start-up and shutdown operations, thereby improving the reliability and economy of the units.

[0177] Network security constraints are imposed on the network side, including power flow constraints and capacity limits for the local topology network 8, DC tie-in channels and receiving-end topology network 15, and power boundary constraints for DC tie-in channels.

[0178] The load side is subject to elastic load transfer constraints, including the total amount conservation constraints before and after the elastic load transfer of local load 9.

[0179] Based on the above load transfer constraints, in order to prevent large-scale load transfer from impacting the power system operation and causing problems such as system frequency fluctuations or dispatch instability, it is necessary to reasonably limit the amount of flexible load transfer. The corresponding constraint model is shown in equation (19):

[0180]

[0181] In equation (19), the scenario is the time period s under the time scale h, and β is the proportion of load that can participate in flexible load transfer. This represents the maximum load capacity available for response within the specified time period. The adjusted flexible load should be kept within a reasonable range to ensure the feasibility of load adjustments and the stability of the power grid operation.

[0182] The power balance constraint applies between the power source, grid, and load.

[0183] Step 2-5): Based on the relevant constraint set established in Steps 2-1) to 2-4), the solution is performed with the goal of minimizing the overall operating cost of the system. The overall operating cost of the system includes the operating cost of hydropower station 6, the operating cost of energy storage unit 7, the cost of wind and solar curtailment, the cost of wind and solar curtailment penalty, the cost of flexible load transfer, and the cost of DC interconnection channel power adjustment. The operating cost of hydropower station 6 includes the power generation cost of hydropower station 6. The operating cost of energy storage unit 7 includes the power generation cost, energy storage cost, and energy storage operation cost of energy storage unit 7. The cost of wind and solar curtailment includes the cost of wind power curtailment and the cost of solar power curtailment. The cost of DC interconnection channel power adjustment includes the cost of purchasing and transmitting electricity. The objective function is shown in Equation (20):

[0184]

[0185] In equation (20), the scenario is a time scale t and a time period s. The start-up and shutdown cost coefficient for hydropower station 6 is given by the integrated power station n. During the current dispatch phase, the start-up and shutdown costs of hydropower station 6 are further considered to reduce meaningless start-up and shutdown operations.

[0186] Steps 2-6): Based on the solution results of Step 2-5), formulate the power output allocation plan for the integrated "wind-solar-hydro-storage" base, and determine the allocation of purchased and transmitted electricity. Further comprehensive analysis of the load shedding rate and curtailment rate for each time period is conducted. Electricity price signals are used to further guide flexible load transfer, prompting the load to purposefully shift from periods with higher load shedding rates to off-peak periods with greater wind and solar curtailment. Based on the adjusted load distribution, a revised load demand curve is reconstructed, and production time-series simulation is performed to generate an updated day-ahead scheduling plan. This provides a water allocation plan, power output constraint boundaries, and a revised load supply curve for Step 3, completing the execution of Step 2.

[0187] See Figure 3 In step 3, the intraday scheduling phase includes the following steps:

[0188] Step 3-1): Based on the historical operation data and load history data of the integrated wind-solar-water-storage base, multi-source data prediction is carried out to construct a prediction dataset including wind speed, light intensity, temperature, runoff and load. Wind power predicted output data is calculated based on wind speed, and photovoltaic predicted output data is calculated based on light intensity and temperature. Finally, watershed runoff prediction data, wind and solar power predicted output data and load prediction data are used as inputs for step 3.

[0189] Step 3-2): Based on the watershed runoff forecast data, wind and solar power output forecast data, and load forecast data generated in Step 3-1), perform intraday forecast error statistical analysis, construct the corresponding forecast error distribution, determine the error range of the input data in the intraday scheduling stage to determine the degree of various forecast errors, and then determine the relative error of the input data in the intraday scheduling stage.

[0190] Step 3-3) Based on the relative error of the input data for the intraday scheduling phase determined in Step 3-2), considering the fluctuation and uncertainty of the output of resources such as water, wind and solar power during the intraday scheduling phase, as well as the possibility of short-term load surges, a backup plan is set for the system during the scheduling process to improve the system's response capability to short-term fluctuations in source load. The method utilizes the rapid adjustment characteristics of energy storage unit 7 and hydropower station 6, combined with the optimized scheduling of DC interconnection channels, and sets the backup plan constraints for the intraday scheduling phase as shown in Equation (21):

[0191]

[0192] In Equation (21), the scenario is the s-period under the q-time scale, μ is the load fluctuation coefficient, and γ is the maximum vibration amplitude coefficient of hydropower station 6 and energy storage unit 7 under the q-time scale. The main uncertainty in the intraday scheduling stage comes from short-term meteorological disturbances and sudden load increases, which puts forward higher requirements for the real-time response capability of the scheduling system. Based on the above analysis, the intraday scheduling stage relies on the rapid response characteristics of hydropower station 6 and energy storage unit 7, combined with the power transmission capacity of the DC interconnection channel, to cope with the power output fluctuations on both sides of the source and load, and to ensure the safety and flexibility of system operation.

[0193] Steps 3-4): Based on the reserve plan generated in step 3-3), and considering the relevant operational constraints of the system during the day-ahead scheduling phase, the following operational constraints are set for the technical characteristics and equipment status of the source, grid, and load:

[0194] The source side includes upper limit constraints on wind and solar power output, start-up and shutdown constraints, output constraints, ramp-up rate constraints and water balance constraints of hydropower station 6, and charging and discharging power constraints, state of charge constraints and charging and discharging efficiency constraints of energy storage unit 7, which constitute the constraint set of the integrated "wind-solar-hydro-storage" base.

[0195] Network security constraints are imposed on the network side, including power flow constraints and capacity limits for the local topology network 8, DC tie-in channels and receiving-end topology network 15, and power boundary constraints for DC tie-in channels.

[0196] The load side includes the flexible load transfer ratio constraint, load transfer time constraint, load transfer amount boundary constraint, and total amount conservation constraint before and after load transfer in the local load 9, which constitute the flexible load transfer constraint.

[0197] The power balance constraint applies between the power source, grid, and load.

[0198] Steps 3-5): Based on the relevant constraint set established in steps 3-1) to 3-4), the solution is performed with the goal of minimizing the overall operating cost of the system. The overall operating cost of the system includes the operating cost of hydropower station 6, the operating cost of energy storage unit 7, the cost of wind and solar curtailment, the cost of wind and solar curtailment penalty, the cost of flexible load transfer, and the cost of DC interconnection channel power adjustment. The operating cost of hydropower station 6 includes the power generation cost and the start-up and shutdown cost of hydropower station 6. The operating cost of energy storage unit 7 includes the power generation cost, energy storage cost, and energy storage operation cost of energy storage unit 7. The cost of wind and solar curtailment includes the cost of wind power curtailment and the cost of photovoltaic curtailment. The cost of DC interconnection channel power adjustment includes the cost of purchasing and transmitting electricity.

[0199] Steps 3-6): Based on the solution results of Steps 3-5), compare the day-ahead scheduling plan, adjust the output boundaries of hydropower station 6 and energy storage unit 7, generate the adjustment plan for hydropower station 6 and the adjustment output plan for energy storage unit 7, and combine the inertia characteristics of various power sources to determine the configuration of purchased power and transmitted power, forming the intraday scheduling plan, providing power source inertia, output boundary constraints and demand load curves for Step 4.

[0200] See Figure 3 In step 4, the real-time scheduling phase includes the following steps:

[0201] Step 4-1): Based on the real-time monitoring data and load detection data of the integrated "wind-solar-water-storage" base, construct a real-time monitoring dataset including wind speed, light intensity, temperature, runoff and load. Calculate wind power monitoring output data based on wind speed, and calculate photovoltaic monitoring output data based on light intensity and temperature. Finally, the watershed runoff monitoring data, wind and solar power monitoring output data, and load monitoring data are used as inputs for Step 4.

[0202] Step 4-2): Based on the watershed runoff monitoring data, wind and solar power output data, and load monitoring data generated in Step 4-1), perform real-time monitoring error statistical analysis, construct the corresponding prediction error distribution, determine the error range of the input data in the real-time scheduling stage to determine the degree of various prediction errors, and then determine the relative error of the input data in the real-time scheduling stage.

[0203] Step 4-3): Based on the relative error of the input data in the real-time dispatch phase determined in Step 4-2), considering the minute-level wind and solar fluctuations in the real-time dispatch phase, the core of the dispatch work lies in smoothing out these rapid fluctuations. Considering that hydropower station 6 cannot quickly adjust its output on a minute-level time scale, in order to improve the system's response capability to short-term fluctuations in source load, the inertia of each unit is used to resist short-term disturbances. Based on the inertia information of each unit transmitted during the intraday dispatch phase, the system prioritizes using inertia characteristics for anti-interference adjustment. If the inertia response is insufficient to smooth out fluctuations, energy storage unit 7 is activated for further adjustment; if energy storage still cannot completely absorb the disturbance, power adjustment is carried out through the DC interconnection channel to achieve the final stability of the power grid. The inertia model of each unit is established as follows:

[0204] The rotating standby model of the hydropower station 6 is shown in equation (22):

[0205]

[0206] In equation (22), during the time period s on the m-timescale, This indicates the power change of hydropower station 6, which is an integrated power station n, during the real-time dispatch phase due to inertial response, which is either released or absorbed by the power station 6. Let n be the unit inertia constant of hydropower station 6, which is an integrated power station. This represents the baseline capacity of hydropower station 6, which is an integrated power station n. Indicates the rate of change of system frequency. The kinetic energy of the rotating components of hydropower station 6 represents the kinetic energy of the integrated power station n. This indicates the rated capacity of hydropower station 6, which represents integrated power station n. The moment of inertia of the generator rotor of hydropower station 6 is given by the integrated power station n. Let n be the angular velocity of the generator rotor of hydropower station 6, which is an integrated power station.

[0207] The virtual inertia model of wind turbine 4 is shown in equation (23):

[0208]

[0209] In equation (23), the time period s is at the m-time scale. This represents the power change provided by the virtual inertia of the wind turbine 4 in the integrated power station n during the real-time scheduling phase. The virtual inertia constant of wind turbine 4 in integrated power station n is represented. This represents the base capacity of wind turbine 4 in the integrated power station n.

[0210] The virtual inertia model of photovoltaic unit 5 is shown in equation (24):

[0211]

[0212] In equation (24), during the s time period at the m time scale, This represents the power change provided by the virtual inertia of photovoltaic unit 5 in integrated power station n during the real-time scheduling phase. Let represent the virtual inertia constant of photovoltaic unit 5 in integrated power station n. This represents the base capacity of photovoltaic unit 5 in the integrated power station n.

[0213] Considering that the rotational inertia and virtual inertia of each power source are subject to certain limitations during operation, constraints are established for them, as shown in equation (25):

[0214]

[0215] In equation (25), during the time period s at the time scale m, em = w, wd, and pv represent hydropower, wind power, and photovoltaic power, respectively. This represents the power variation provided by the rotational inertia or virtual inertia of each power source. and These represent the maximum and minimum net output of each power source. It provides power to various power sources.

[0216] Step 4-4): Based on the inertia models of each unit during the real-time scheduling phase provided in Step 4-3), and combined with the relevant operational constraints provided by the system during the intraday scheduling phase, the following operational constraints are set for the technical characteristics and equipment status of the source, grid, and load:

[0217] The source side includes upper limit constraints on wind and solar power output, start-up and shutdown constraints, output constraints, ramp-up rate constraints and water balance constraints of hydropower station 6, and charging and discharging power constraints, state of charge constraints and charging and discharging efficiency constraints of energy storage unit 7, which constitute the constraint set of the integrated "wind-solar-hydro-storage" base.

[0218] Network security constraints are imposed on the network side, including power flow constraints and capacity limits for the local topology network 8, DC tie-in channels and receiving-end topology network 15, and power boundary constraints for DC tie-in channels.

[0219] The load side includes constraints on the proportion of flexible load transfer, constraints on the time period of load transfer, constraints on the boundary of load transfer amount, and constraints on the total amount of load transfer before and after transfer, which constitute the constraints on flexible load transfer.

[0220] The power balance constraint applies between the power source, grid, and load.

[0221] Steps 4-5): Based on the relevant constraint set established in steps 4-1) to 4-4), the solution is performed with the goal of minimizing the overall operating cost of the system. The overall operating cost of the system includes the operating cost of hydropower station 6, the operating cost of energy storage unit 7, the cost of wind and solar curtailment, the cost of wind and solar curtailment penalty, the cost of flexible load transfer, and the cost of DC interconnection channel power adjustment. The operating cost of hydropower station 6 includes the power generation cost and the start-up and shutdown cost of hydropower station 6. The operating cost of energy storage unit 7 includes the power generation cost, energy storage cost, and energy storage operation cost of energy storage unit 7. The cost of wind and solar curtailment includes the cost of wind power curtailment and the cost of solar power curtailment. The cost of DC interconnection channel power adjustment includes the cost of purchasing and transmitting electricity.

[0222] Steps 4-6): Based on the solution results of Steps 4-5), compare the intraday scheduling plan, adjust the output boundaries of hydropower station 6 and energy storage unit 7, generate the adjustment plan for hydropower station 6 and the adjustment output plan for energy storage unit 7, and combine the inertia characteristics of various power sources to determine the configuration of purchased power and transmitted power, forming a real-time scheduling plan to provide decision-making for real-time scheduling.

[0223] In summary, this method first analyzes the prediction errors of hydropower, wind power, photovoltaic power generation, and load at different time scales based on relative error analysis, and determines the prediction errors at each time scale through the distribution of relative errors in historical predictions. Secondly, after clarifying the prediction error ranges at each time scale, it combines the regulation characteristics of the power source, grid, and load sides, and reduces the uncertainty of wind and solar power generation and improves its utilization efficiency by scheduling hydropower stations (6), energy storage units (7), DC interconnection channels, and flexible loads. Finally, considering the differences in the response speed of regulation resources on the power source, grid, and load sides, and combining grid security requirements, a multi-time-scale optimized scheduling framework covering four levels—pre-season, pre-day, intraday, and real-time—is constructed with the goal of minimizing the overall system operating cost. This ensures the coordinated operation of various resources, improves the overall system efficiency, alleviates the curtailment problem under the background of large-scale renewable energy integration, and enhances the system's operating efficiency and economy, demonstrating strong application prospects.

[0224] To better complement the aforementioned source-grid-load coordinated scheduling method based on multi-timescale coupling, this invention also provides a power supply system for a combined "wind-solar-hydro-storage" DC interconnection channel that can be used in conjunction with it. The system framework is as follows: Figure 1 As shown, the system consists of an integrated wind-solar-hydro-storage power generation base 1, a power transmission channel network 2, and a receiving-end power grid 3, which together form a system-level coupling.

[0225] See Figure 1 The integrated wind-solar-hydro-storage power generation base 1 is based on a multi-stage cascade hydropower station 6. Each stage of the hydropower station 6 is laid out sequentially along the natural water flow from top to bottom. Each stage of the hydropower station is equipped with wind turbine units 4, photovoltaic units 55, and energy storage units 7. Each stage of the hydropower station and its supporting wind turbine units 4, photovoltaic units 5, and energy storage units 7 together constitute an integrated power station. Multiple cascade hydropower stations 6 and their auxiliary power stations further form multiple integrated power station units, ultimately integrating to form a complete integrated wind-solar-hydro-storage power generation base. Various power sources are first connected to the local AC collection bus of their respective integrated power stations. After voltage boosting or grid connection, they are uniformly connected to the main AC collection bus within the base, completing the physical integration and coordinated control of multi-source power, and ultimately providing stable power to the power transmission network 2.

[0226] See Figure 1The power transmission channel network 2 includes a local topology network 8, local loads 9, transformers 110, rectifier systems 111 and 212, transformers 213, and a DC interconnection channel. The main AC collection bus within the base is connected to the low-voltage side of the local topology network 8 and transformer 110, respectively. The local topology network 8 can directly supply power to the local load 9 to meet the power demand within the region. The transformer 110 steps up the low-voltage AC power to high-voltage AC power and outputs it from the high-voltage side to the rectifier system 111. The rectifier system 111 further converts the high-voltage AC power into high-voltage DC power and sends it to the receiving area through the DC interconnection channel. The DC power first enters the rectifier system 212, and the converter converts the high-voltage DC power into high-voltage AC power. Then it is connected to the high-voltage side of the transformer 213, and after step-down processing, it is output to the receiving end grid bus to realize long-distance power transmission. In addition, when the local renewable energy output is insufficient or the system needs external support, the receiving area can also send power back to the local topology network 8 through the DC interconnection channel. The power transmission process is the reverse process mentioned above, which is used to supplement the demand of the local load 9 or participate in energy optimization scheduling.

[0227] See Figure 1 The receiving-end power grid 3 includes receiving-end load 14, receiving-end topology network 15, and receiving-end power source 16. The receiving-end power grid bus collects the AC power output from transformer 2 and connects it together with the local power provided by receiving-end power source 16 to the receiving-end topology network 15. The receiving-end topology network coordinates and distributes the power and uniformly transmits it to the receiving-end load 14, realizing the integrated power supply of remote power and local power source.

[0228] Example:

[0229] This invention selects a certain integrated watershed base as the research area and a typical day in summer as the research period to analyze the overall system operation under the scheduling method proposed in this invention. The basic parameters of the integrated wind-solar-water-storage base are shown in Table 1; the system operating cost is shown in Table 2; and the power flow network diagram used is the IEEE-30 node.

[0230] Table 1 Basic Parameters of Integrated Power Plant

[0231]

[0232]

[0233] Table 2 System Operating Costs

[0234]

[0235] This invention uses a specific day during the high-water season as the research period to analyze the operational effectiveness of the integrated coordinated scheduling strategy of "wind-solar-hydro-storage" across multiple time scales and the utilization of new energy sources. (See reference...) Figure 4 The figure shows the first ten days of the pre-season scheduling. During the pre-season scheduling phase, the reservoir regulation capacity of hydropower station 6 and the flexible regulation capacity of energy storage unit 7 are mainly utilized to jointly optimize the allocation of water, wind, solar, and energy storage resources. During this phase, by rationally arranging the output of hydropower and the charging and discharging behavior of energy storage, the fluctuations of wind and solar power are smoothed, the curtailment rate is reduced, and the energy utilization efficiency and output stability of the system are initially improved.

[0236] See Figure 5 Day-ahead scheduling is conducted on the 6th day of the pre-season scheduling. During the day-ahead scheduling phase, the rapid regulation capabilities of hydropower station 6 and energy storage unit 7, as well as the transmission capacity of the DC interconnection channel, are utilized in conjunction with the forecast results of hydropower, wind, solar and load, to optimize the allocation of energy output. In addition, to further reduce the regulation pressure on the units and improve the load adaptability to the distribution of renewable energy, flexible load transfer is carried out based on the analysis of load shedding rate and curtailment rate. Compared with the load transfer before, the load curve after the transfer is smoother and more adapted to the distribution of renewable energy, further improving the overall operating economy of the system and the renewable energy absorption level.

[0237] See Figure 6 The intraday scheduling is conducted from 8:00 to 11:00 during the day-ahead scheduling period. This intraday scheduling phase primarily addresses short-cycle fluctuations in wind and solar power and real-time load changes, offering higher time resolution and response sensitivity. During this phase, the system utilizes the rapid adjustment capabilities of hydropower station 6 and energy storage unit 7, as well as the transmission capacity of the DC interconnection channel, to flexibly adjust energy output configuration, continuously revise the day-ahead plan, and match source-load changes in real time. This reduces wind and solar power curtailment, lowers the load shedding rate, and improves system stability and the utilization efficiency of new energy resources.

[0238] See Figure 7 Real-time dispatch is conducted during the 8th quarter-hour of the day's scheduling. This real-time dispatch phase addresses sudden disturbances and instantaneous deviations between power source and load during power system operation, offering greater flexibility. During this phase, the system primarily utilizes the inertia characteristics of each generating unit, the energy storage unit 7, and the transmission capacity of the DC interconnection channel to regulate energy output. By rapidly coordinating various flexible adjustment resources, it compensates for the differences between wind and solar power output and load fluctuations in real time, ultimately achieving dynamic balance and stable power output for the system.

[0239] In summary, this method considers the regulation characteristics of the source, grid, and load sides at various time scales. By adjusting multiple resources, it can smooth out fluctuations in renewable energy output, improve renewable energy utilization, alleviate the problem of power curtailment under the background of large-scale renewable energy access, and improve system operating efficiency and economy. It has strong application prospects.

Claims

1. A source-grid-load coordinated scheduling method based on multi-time-scale coupling, characterized in that, It includes the preseason scheduling phase, the day-ahead scheduling phase, the intraday scheduling phase, and the real-time scheduling phase; it includes the following steps: Step 1: Execute pre-season scheduling. Based on the distribution characteristics of water resources throughout the year, the year is divided into multiple typical seasons. Corresponding water resource allocation strategies are formulated for each season. Taking into account the seasonal variation characteristics of new energy output, a coordinated scheduling arrangement within the season is formulated, and a pre-season scheduling plan is output. Step 2: Based on the pre-season scheduling plan output in Step 1, and combined with the load forecast and new energy output forecast information in the day-ahead scheduling phase, carry out day-ahead scheduling and output the day-ahead scheduling plan. Step 3: Based on the day-ahead scheduling plan output in Step 2, and combined with the intraday updated meteorological and load forecast information, carry out intraday scheduling and output the intraday scheduling plan; Step 4: Based on the intraday scheduling plan output in Step 3, and combined with real-time monitoring data, conduct real-time scheduling and output the real-time scheduling plan; Step 1 includes the following steps: Step 1-1) Obtain watershed runoff data, historical wind and solar power output data, and historical load data; Step 1-2) The errors between the data obtained in Step 1-1) and the actual data are statistically analyzed to construct the corresponding historical data error distribution. The input data error range of water, wind and solar load in the pre-season scheduling stage is determined to determine the degree of input data error. Specifically, the accuracy of water, wind and solar power output and load demand is analyzed by relative error analysis. The relative error formula is shown in Equation (3): （3）； In equation (3), the scenario is Time scale Time period This is a sample of the relative error between the input data and the actual data. For sample index, , The total number of samples, These represent runoff data, wind power data, solar power data, and load data, respectively. This represents the input data sample data. Represents actual data; Based on the quantization results of the relative error, the error probability distribution of various input data is constructed, as shown in Equation (4): （4）； make As The relative error used to establish input data error constraints over a time scale, where, This is the first sample of relative error between the input data and the actual data. This is a sample of the relative error between the second input data and the actual data. For the first One sample of relative error between input data and actual data; Steps 1-3) Based on the relative error of the input data in the pre-season scheduling phase determined in Steps 1-2), combined with the regulation capabilities of the source, grid and load sides, the adjustability of hydropower stations, the charging and discharging flexibility of energy storage units, the transmission capacity of DC interconnection channels and the regulation characteristics of flexible loads are comprehensively utilized to reduce the uncertainty of wind and solar power output. Steps 1-4) Set corresponding operating constraints based on the technical characteristics of the source, grid, and load, and the status of the equipment; Steps 1-5) Solve the problem with the goal of minimizing the overall operating cost of the system; Steps 1-6) Based on the solution results of Steps 1-5), generate the power output configuration plan of the integrated "wind-solar-hydro-storage" base, determine the configuration of purchased power and transmitted power, and form a pre-season scheduling plan.

2. The method according to claim 1, characterized in that, The model involved in step 1-1) is specifically as follows: The uncertain output model of wind turbine (4) is shown in equation (1): （1）； In equation (1), It represents different time scales, including four time granularities: day, hour, quarter-hour, and minute. In order to be in Time-scale index, , In order to be in Total number of scheduling periods on a time scale, scenario: Time scale Time period For integrated power plant index, , The total number of integrated power plants, Integrated power station Wind power output, Integrated power station wind speed, , , Integrated power station The cut-in wind speed, rated wind speed, and cut-out wind speed of the wind turbine. for cubed, for cubed, for cubed, Integrated power station The rated power of the wind turbine unit; The output model of the photovoltaic unit is shown in equation (2): （2）； In equation (2), the scenario is Time scale Time period Integrated power station Photovoltaic power output, Integrated power station The rated power of the photovoltaic unit, and These respectively represent integrated power plants The surface shortwave incident radiation capacity and solar radiation intensity under standard test conditions of the photovoltaic unit. Integrated power station The temperature coefficient of output power of solar cell modules, and Integrated power station The temperature of the photovoltaic module and the air temperature, and These respectively represent integrated power plants Temperature of photovoltaic modules under normal conditions and temperature under standard test conditions; In steps 1-3), the specific models involved are as follows: The power output and reservoir capacity model of the hydropower station are shown in equation (5): （5）； In equation (5), the scenario is Time scale Time period and Integrated power station The total discharge and inflow of the hydropower station and Integrated power station The discharge flow and abandoned water flow of the hydropower station. Indicates integrated power station The output of the hydroelectric power station, Integrated power station The overall output coefficient of the hydropower station, , , , Representing integrated power plants The hydropower station's head height, reservoir level, tailrace level, and head loss height. Integrated power station The reservoir's water capacity for The square of, , and These are integrated power plants using polynomial fitting. The coefficients of each term in the reservoir capacity-water level-capacity relationship curve of a hydroelectric power station. for The square of, , and These are integrated power plants using polynomial fitting. The coefficients of each item in the curve showing the relationship between the tailwater level of the hydropower station's reservoir and the total outflow. The scheduling time interval; The energy storage unit and energy storage capacity model are shown in equation (6): （6）； In equation (6), the scenario is Time scale Time period Integrated power station The energy storage capacity of the energy storage unit Integrated power station of The energy storage capacity of the time-limited energy storage unit and Integrated power station The charging and discharging efficiency of the energy storage unit. and Integrated power station The charging and discharging power of the energy storage unit, and Integrated power station The minimum and maximum energy storage capacity of the energy storage unit, and Integrated power station The minimum and maximum charging power of the energy storage unit, and Integrated power station The minimum and maximum discharge power; During the pre-season scheduling phase, in order to cope with the power station equipment maintenance plan, a corresponding backup plan needs to be preset. Considering the regulation capacity of the source, grid and load sides and the seasonal characteristics of wind, solar and water resources, the system scheduling flexibility is mainly ensured by the reservoir storage capacity of the hydropower station. The backup plan constraints during the pre-season phase are shown in equation (7): （7）； In equation (7), the scenario is Time scale Time period Integrated power station The reserve capacity of hydroelectric power stations The total number of days in the water season. To establish the relative error of the input data error constraint, Integrated power station The various energy sources output, , Integrated power station The maximum output of the hydroelectric power station Integrated power station The hydroelectric power station output, Integrated power station The maintenance reserve coefficient of hydropower stations; during the pre-season scheduling phase, the output deviation of wind and solar resources is smoothed by long-term statistical characteristics and has a relatively small impact. During the scheduling phase, the main source of error is the seasonal runoff deviation. Therefore, the focus of scheduling work is to carry out medium- and long-term scheduling planning for water resources. Through the complementary reservoir capacity of the cascade hydropower station reservoir group, the optimal scheduling of reservoirs and the efficient utilization of water resources throughout the year are ensured. In addition, relevant reserves are set up to cope with maintenance and ensure the stable operation of the system.

3. The method according to claim 2, characterized in that, In steps 1-4), the following operational constraints are further set based on the technical characteristics of the source, grid, and load, and the equipment status: Constraints on the operation of integrated wind-solar-hydro-storage bases on the source side: The output of a wind turbine is limited by its maximum and minimum output range, and power curtailment may occur during actual operation. To accurately describe this limitation, the following constraints are set for the wind turbine, as shown in equation (8): （8）； In equation (8), the scenario is Time scale Time period and Integrated power station The minimum and maximum output of the wind turbine units, Integrated power station The grid-connected power of wind turbine units, Integrated power station The amount of abandoned power from wind turbines; The output of photovoltaic units is also subject to similar constraints as that of wind turbine units; to accurately describe this limitation, the following constraints are set for photovoltaic units, as shown in equation (9): （9）； In equation (9): the scenario is Time scale Time period and Integrated power station The minimum and maximum output of the photovoltaic units, Integrated power station The grid-connected power of photovoltaic units, Integrated power station The amount of electricity curtailed by photovoltaic power units; The regulation capacity of a hydropower station is affected by various factors such as water volume and unit operating efficiency. To accurately describe this limitation, the following constraints are set for the hydropower station, as shown in equation (10): （10）； In equation (10), the scenario is Time scale Time period and Integrated power station The maximum and minimum total discharge flows of the hydropower station. and Integrated power station The maximum and minimum discharge flows for power generation at the hydropower station. and Integrated power station The maximum and minimum reservoir capacity of the hydropower station. and Integrated power station The maximum and minimum head heights of the hydropower station; The charging and discharging capability of the energy storage unit depends on the state of charge of the battery and the capacity of the energy storage device. To accurately describe this limitation, the following constraints are set for the energy storage unit, as shown in equation (11): （11）； In equation (11), the scenario is Time scale During certain time periods, constraints are imposed to ensure that energy storage units cannot perform charging and discharging operations simultaneously. In addition, its energy storage capacity has certain limitations. and Integrated power station The maximum and minimum values ​​of the energy storage unit capacity are determined. Finally, to ensure the continuity and stability of the scheduling process, the energy storage status during the pre-season scheduling phase is constrained, and the initial energy storage capacity of the energy storage units is set as the midpoint between the maximum and minimum values ​​of the energy storage unit capacity. Integrated power station The energy storage capacity at the first scheduling moment, Integrated power station The The energy storage capacity at each scheduling moment; Network side includes network security constraints: The local topology network, DC tie-line network, and receiving-end topology network are treated as a large network. Power supply security is ensured by setting power flow constraints. The specific steps are as follows: First, the system's node admittance matrix is ​​obtained through the strategy. , Let the total number of nodes in the system be denoted by ',' and after selecting the balancing nodes, the matrix is ​​obtained by removing the columns and rows containing the balancing nodes and then inverting the matrix. Inserting all-zero columns and all-zero rows at the positions of the balance nodes yields the matrix. Next, the power transfer distribution factor for each branch is calculated, for each branch. and node, and These are the system's circuit branches and node indices, respectively. , , Given the total number of branches, the power transfer distribution factor is obtained using formula (12): （12）； In equation (12), represent Node to branch The power transfer factor matrix on, and Branch roads The first and last nodes, Representative branch road The impedance, based on the power transfer distribution factor matrix The constraints on the power of generator nodes and load nodes are set as shown in equation (13): （13）； In equation (13), the scenario is Time scale Time period and Branch roads Minimum and maximum power that can be withstood and These are the generator node and load node pairs of the branch lines. The transfer power, For the generation node index, , For load node indexing, , and The output power of generator nodes and the demand power of load nodes are respectively considered. The method analyzes the power network based on the power transfer distribution factor, calculates the impact of generator nodes and load nodes on power transfer, and sets corresponding constraints to ensure that the power flow always remains within the normal range of the branch carrying capacity, thereby ensuring the stability and security of the power system. The load side mainly constrains the local load, including elastic load transfer constraints, as shown in equation (14): （14）； In equation (14), the scenario is Time scale Time period For the original load demand, To account for the load demand after participating in flexible load transfer, For flexible load transfer, the local load demand must remain unchanged before and after the transfer; The power source, grid, and load must jointly comply with the power balance constraint to ensure stable system operation, as shown in equation (15): （15）； In equation (15), the scenario is Time scale Time period and The system purchases and transmits power through DC interconnection channels. At different time scales, the system must meet the dynamic balance between power generation output and load demand to avoid the fluctuation risk caused by power supply and demand imbalance. If the system experiences load shedding or large-scale wind and solar curtailment within a certain time scale, it indicates that the current dispatch strategy cannot effectively adapt to the uncertainty of wind and solar power output and load fluctuations. Therefore, it is necessary to further optimize the dispatch scheme with narrow time scales. This can be achieved by adjusting the water volume regulation strategy of hydropower stations, optimizing the charging and discharging plan of energy storage units, and rationally arranging the transfer of flexible loads in local loads. These measures can improve the system's adaptability to fluctuations in wind and solar power output and ensure the stability and reliability of power supply.

4. The method according to claim 3, characterized in that, In steps 1-5), the objective function used is shown in equation (16): （16）； In equation (16), the scenario is Time scale Time period For the overall system operating cost, For the operating costs of the hydropower station, For the operating costs of energy storage units, To incur penalties for wind and solar power curtailment For flexible load transfer costs, For DC-DC interconnection channel power adjustment costs, This is the power generation cost coefficient for hydropower stations. and These are the power generation cost coefficient and the energy storage cost coefficient for energy storage units, respectively. This is the energy storage operating cost coefficient for the energy storage unit. and These are the penalty cost coefficients for wind and solar power curtailment. This is the flexible load transfer cost coefficient. and These are the cost coefficients for purchased and transmitted electricity, respectively.

5. The method according to claim 4, characterized in that, In step 2, the day-ahead scheduling phase includes the following steps: Step 2-1) Based on the historical operation data and load history data of the integrated "wind-solar-hydro-storage" base, multi-source data prediction is carried out to construct a prediction dataset including wind speed, light intensity, temperature, runoff and load. Wind power predicted output data is calculated based on wind speed, and solar power predicted output data is calculated based on light intensity and temperature. Finally, the watershed runoff prediction data, wind and solar power predicted output data and load prediction data in the day-ahead scheduling stage are used as input for Step 2. Step 2-2) Based on the watershed runoff forecast data, wind and solar power output forecast data and load forecast data generated in Step 2-1), perform statistical analysis of day-ahead forecast errors, construct the corresponding forecast error distribution, determine the error range of the input data in the day-ahead scheduling stage to determine the degree of various forecast errors, and then determine the relative error of the input data in the day-ahead scheduling stage. Step 2-3) Based on the relative error of the input data in the day-ahead scheduling phase determined in Step 2-2), in order to cope with the output fluctuations and prediction uncertainties of renewable energy sources such as wind and solar power in the day-ahead phase, a backup plan is set for the system during the scheduling process to improve the flexibility and security of the system in response to sudden fluctuations. A backup capacity setting mechanism is introduced, which utilizes the reservoir capacity of hydropower stations and energy storage units, the transmission capacity of DC interconnection channels, and the transferability of flexible loads to improve the flexibility of system scheduling. The backup plan constraints in the day-ahead scheduling phase are set as shown in Equation (17): （17）； In equation (17), the scenario is Time scale Time period Integrated power station The reserve capacity of hydroelectric power stations Integrated power station The energy storage capacity of the energy storage power station To establish the relative error of the input data error constraint, Integrated power station The various energy sources output, Integrated power station The maximum rate of ascent for the hydropower station, Integrated power station The output of the hydroelectric power station, Integrated power station hydroelectric power station Output during different time periods; During the day-ahead dispatch phase, due to the large climate prediction errors and inherent volatility of natural resources such as water, wind, and solar power, climate prediction deviations become the main source of error. The core of dispatch work is to smooth out the output fluctuations of integrated bases and improve the utilization efficiency of wind and solar resources. In day-ahead dispatch, output smoothing and efficient utilization of wind and solar resources are achieved through the pre-allocation of capacity of energy storage units and hydropower stations, as well as the optimized dispatch of DC interconnection channels. At the same time, the system's ability to cope with sudden load increases is enhanced. In addition, it is necessary to formulate a flexible load transfer plan to provide new load supply schemes for intraday dispatch, so as to further improve the overall regulation and control capabilities of the system. The strategy analyzes the load loss rate of each time period and uses a price mechanism to stimulate flexible load transfer, adjusting the load of the time period with a high load loss rate to the time period with more wind and solar curtailment, so as to optimize the load distribution and improve the level of new energy consumption. Steps 2-4) Based on the reserve plan generated in Step 2-3) for the day-ahead scheduling phase, and in conjunction with the relevant operational constraints of the system during the day-ahead scheduling phase, the corresponding operational constraints are set according to the technical characteristics of the source, grid, and load, and the equipment status, as follows: On the source side, there are constraints on the integrated "wind-solar-hydro-storage" base, including upper limit constraints on wind power and photovoltaic output, output constraints and water balance constraints of hydropower stations, and charging and discharging power constraints, state of charge constraints and charging and discharging efficiency constraints of energy storage units. Based on the constraints of relevant generating units, the day-ahead dispatching phase further introduces constraints on the start-up and shutdown characteristics and ramping capabilities of hydropower stations. The relevant constraint formulas are shown in equation (18): （18）； In equation (18), the scenario is Time scale Time period and Integrated power station The maximum and minimum rate of ascent for the hydropower station. Integrated power station hydroelectric power station Efforts during a specific time period and These are the minimum shutdown and startup times, respectively. Integrated power station The operating status of the hydropower station is 0 for shutdown and 1 for startup. Integrated power station hydroelectric power station The working status during different time periods; in order to ensure that the power ramp-up does not exceed the limit during the operation of the hydropower station, relevant limits are set on the ramp-up rate of the units; in addition, considering that frequent start-ups and shutdowns will increase operating costs and aggravate mechanical wear of equipment, affecting the service life and operational stability of the units, the start-up and shutdown times of the units are constrained to reduce unnecessary start-up and shutdown operations and improve the reliability of the units. Network security constraints are imposed on the network side, including power flow constraints and capacity limits for the local topology network, DC tie-in channels and receiving-end topology network, and power boundary constraints for DC tie-in channels; The load side is subject to elastic load transfer constraints, including total conservation constraints before and after the elastic load transfer of local loads; Based on load transfer constraints, in order to prevent large-scale load transfer from impacting the operation of the power system and causing problems such as system frequency fluctuations or dispatch instability, it is necessary to reasonably limit the amount of flexible load transfer; the corresponding constraint model is shown in equation (19): （19）； In equation (19), the scenario is Time scale Time period This represents the elastic load transfer amount in this scenario. For the original load demand, The proportion of loads that can participate in flexible load transfer. The maximum load capacity that can participate in flexible load transfer within a given time period is limited. The adjusted flexible load should be controlled within a reasonable range to ensure the feasibility of load adjustment and the stability of power grid operation. The power balance constraint applies between power generation, grid, and load. Steps 2-5), based on the relevant constraint set established in steps 2-1) to 2-4), are solved with the goal of minimizing the overall operating cost of the system. The overall operating cost of the system includes the operating cost of the hydropower station, the operating cost of the energy storage unit, the cost of wind and solar curtailment, the cost of wind and solar curtailment penalty, the cost of flexible load transfer, and the cost of DC interconnection channel power adjustment. The operating cost of the hydropower station includes the power generation cost of the hydropower station. The operating cost of the energy storage unit includes the power generation cost, the energy storage cost, and the energy storage operation cost of the energy storage unit. The cost of wind and solar curtailment includes the cost of wind power curtailment and the cost of solar power curtailment. The cost of DC interconnection channel power adjustment includes the cost of purchasing and transmitting electricity. The objective function is shown in equation (20): （20）； In equation (20), the scenario is Time scale Time period Integrated power station The start-up and shutdown cost coefficient of hydropower stations; the start-up and shutdown costs of hydropower stations are further considered during the daytime dispatch phase in order to reduce meaningless start-up and shutdown operations of hydropower stations; Steps 2-6): Based on the solution results of Step 2-5), formulate the power output configuration plan for the integrated "wind-solar-hydro-storage" base, and determine the configuration of purchased and transmitted electricity. Further, conduct a comprehensive analysis of the load shedding rate and curtailment rate for each time period, and use electricity price signals to further guide the transfer of flexible load, prompting the load to purposefully shift from the time period with a high load shedding rate to the off-peak time period with a large amount of wind and solar curtailment. Based on the adjusted load distribution, reconstruct the corrected load demand curve, conduct production time sequence simulation, and then generate an updated day-ahead scheduling plan. This provides a water allocation plan, power output constraint boundaries, and a corrected load supply curve for Step 3, thus completing the execution of Step 2.

6. The method according to claim 5, characterized in that, In step 3, the intraday scheduling phase includes the following steps: Step 3-1) Based on the historical operation data and load history data of the integrated "wind-solar-water-storage" base, multi-source data prediction is carried out to construct a prediction dataset including wind speed, light intensity, temperature, runoff and load. Wind power predicted output data is calculated based on wind speed, and photovoltaic predicted output data is calculated based on light intensity and temperature. Finally, watershed runoff prediction data, wind and solar power predicted output data and load prediction data are used as inputs for Step 3. Step 3-2) Based on the watershed runoff forecast data, wind and solar power output forecast data and load forecast data generated in Step 3-1), perform intraday forecast error statistical analysis, construct the corresponding forecast error distribution, determine the error range of the input data in the intraday scheduling stage to determine the degree of various forecast errors, and then determine the relative error of the input data in the intraday scheduling stage. Step 3-3) Based on the relative error of the input data for the intraday scheduling phase determined in Step 3-2), considering the volatility and uncertainty of the output of resources such as water, wind, and solar power during the intraday scheduling phase, as well as the possibility of short-term load surges, a backup plan is set for the system during the scheduling process to improve the system's response capability to short-term fluctuations in source load. The method utilizes the rapid adjustment characteristics of energy storage units and hydropower stations, combined with the optimized scheduling of DC interconnection channels, and sets the backup plan constraints for the intraday scheduling phase as shown in Equation (21): （21）； In equation (21), the scenario is Time scale Time period Integrated power station The reserve capacity of hydroelectric power stations Integrated power station The energy storage capacity of the energy storage unit To establish the relative error of the input data error constraint, Integrated power station The various energy sources output, This is the load fluctuation coefficient. For the original load demand, Integrated power station The maximum rate of ascent for the hydropower station, Integrated power station The output of the hydroelectric power station, Integrated power station hydroelectric power station Efforts during a specific time period Integrated power station Energy storage units in Time scale Energy storage capacity during a given period For hydropower stations and energy storage units The maximum vibration amplitude coefficient on the time scale, the main uncertainty in the intraday dispatching phase comes from short-term meteorological disturbances and sudden load increases, which puts higher demands on the real-time response capability of the dispatching system. Based on the above analysis, the intraday dispatching phase relies on the rapid response characteristics of hydropower stations and energy storage units, combined with the power transmission capacity of DC interconnection channels, to cope with the power output fluctuations on both the source and load sides, and to ensure the safety and flexibility of system operation. Steps 3-4) Based on the reserve plan generated in Step 3-3) for the day-ahead scheduling phase, and in conjunction with the relevant operational constraints of the system during the day-ahead scheduling phase, the following operational constraints are set for the technical characteristics of the source, grid, and load, and the status of the equipment: The source side includes upper limit constraints on wind and solar power output, start-up and shutdown constraints, output constraints, ramp-up rate constraints and water balance constraints of hydropower stations, and charging and discharging power constraints, state of charge constraints and charging and discharging efficiency constraints of energy storage units, which constitute the constraint set of the integrated "wind-solar-hydro-storage" base; The network side includes network security constraints, including power flow constraints and capacity limits for the local topology network, DC tie-in channels and receiving-end topology network, and power boundary constraints for DC tie-in channels; The load side includes constraints on the proportion of flexible load transfer in the local load, constraints on the time period of load transfer, constraints on the boundary of load transfer amount, and constraints on the total amount of load transfer before and after the load transfer, which constitute the flexible load transfer constraints. The power balance constraint applies between power generation, grid, and load. Steps 3-5) Based on the relevant constraint set established in steps 3-1) to 3-4), the solution is performed with the goal of minimizing the overall operating cost of the system. The overall operating cost of the system includes the operating cost of hydropower stations, the operating cost of energy storage units, the cost of wind and solar curtailment, the cost of wind and solar curtailment penalties, the cost of flexible load transfer, and the cost of DC interconnection channel power adjustment. The operating cost of hydropower stations includes the power generation cost and the start-up and shutdown cost of hydropower stations. The operating cost of energy storage units includes the power generation cost, the energy storage cost, and the energy storage operation cost of energy storage units. The cost of wind and solar curtailment includes the cost of wind power curtailment and the cost of solar power curtailment. The cost of DC interconnection channel power adjustment includes the cost of purchasing and transmitting electricity. Steps 3-6) Based on the solution results of Step 3-5), compare with the day-ahead dispatch plan, adjust the output boundaries of hydropower stations and energy storage units, generate hydropower station adjustment plans and energy storage unit adjustment output plans, combine the inertia characteristics of various power sources, determine the configuration of purchased power and transmitted power, form the intraday dispatch plan, and provide power source inertia, output boundary constraints and demand load curves for Step 4.

7. The method according to claim 6, characterized in that, In step 4, the real-time scheduling phase includes the following steps: Step 4-1) Based on the real-time monitoring data and load detection data of the integrated "wind-solar-water-storage" base, construct a real-time monitoring dataset including wind speed, light intensity, temperature, runoff and load. Calculate wind power output data based on wind speed, and calculate solar power output data based on light intensity and temperature. Finally, the watershed runoff monitoring data, wind and solar power output data, and load monitoring data are used as inputs for Step 4. Step 4-2): Based on the watershed runoff monitoring data, wind and solar power output data, and load monitoring data generated in Step 4-1), perform real-time monitoring error statistical analysis, construct the corresponding prediction error distribution, determine the error range of the input data in the real-time scheduling stage to determine the degree of various prediction errors, and then determine the relative error of the input data in the real-time scheduling stage. Step 4-3), based on the relative error of the input data in the real-time dispatch phase determined in Step 4-2), considers the minute-level wind and solar fluctuations in the real-time dispatch phase. The core of the dispatch work is to smooth out these rapid fluctuations. Considering that hydropower stations cannot quickly adjust their output on a minute-level time scale, in order to improve the system's response capability to short-term fluctuations in source load, the inertia of each unit is used to resist short-term disturbances. Based on the inertia information of each unit transmitted during the intraday dispatch phase, the system prioritizes using inertia characteristics for anti-interference adjustment. If the inertia response is insufficient to smooth out fluctuations, energy storage units are activated for further adjustment. If energy storage still cannot completely absorb the disturbance, power adjustment is carried out through the DC interconnection channel to achieve the final stability of the power grid. The inertia model of each unit is established as follows: The rotating standby model of the hydropower station is shown in equation (22): （22）； In equation (22), Time scale Time period This indicates the integrated power station during the real-time dispatch phase. Hydropower stations release or absorb power changes due to inertial response. Integrated power station The unit inertia constant of a hydroelectric power station, Indicates integrated power station The baseline capacity of the hydropower station Indicates the system frequency. This represents the change in system frequency. Indicates the rate of change of system frequency. Indicates integrated power station The kinetic energy of the rotating parts of the hydroelectric power station Indicates integrated power station The rated capacity of the hydropower station, Integrated power station The moment of inertia of the generator rotor in a hydroelectric power station Integrated power station The angular velocity of the generator rotor in the hydroelectric power station; The virtual inertia model of the wind turbine is shown in equation (23): （23）； In equation (23), Time scale Time period This indicates the integrated power station during the real-time dispatch phase. The power variation provided by the virtual inertia of the wind turbine. Indicates integrated power station The virtual inertia constant of the wind turbine unit, Indicates integrated power station The base capacity of the wind turbine unit; The virtual inertia model of the photovoltaic unit is shown in equation (24): （24）； In equation (24) Time scale Time period This indicates the integrated power station during the real-time dispatch phase. The power variation provided by the virtual inertia of the photovoltaic unit. express The virtual inertia constant of the photovoltaic units in an integrated power station. express The baseline capacity of the photovoltaic units in an integrated power station; Considering that the rotational inertia and virtual inertia of each power source are subject to certain limitations during operation, constraints are established for them, as shown in equation (25): （25）； In equation (25), Time scale Time period These are hydropower, wind power, and solar power, respectively. This represents the power variation provided by the rotational inertia or virtual inertia of each power source. and These represent the maximum and minimum net output of each power source. Provide power to each power source; Step 4-4), based on the inertia models of each unit in the real-time scheduling phase provided in Step 4-3), and combined with the relevant operational constraints provided by the system in the intraday scheduling phase, sets the corresponding operational constraints according to the technical characteristics and equipment status of the source-grid-load system as follows: The source side includes upper limit constraints on wind and solar power output, start-up and shutdown constraints, output constraints, ramp-up rate constraints and water balance constraints of hydropower stations, and charging and discharging power constraints, state of charge constraints and charging and discharging efficiency constraints of energy storage units, which constitute the constraint set of the integrated "wind-solar-hydro-storage" base; The network side includes network security constraints, including power flow constraints and capacity limits for the local topology network, DC tie-in channels and receiving-end topology network, and power boundary constraints for DC tie-in channels; The load side includes constraints on the proportion of flexible load transfer, constraints on the time period of load transfer, constraints on the boundary of load transfer amount, and constraints on the total amount of load transfer before and after the transfer, which constitute the constraints on flexible load transfer. The power balance constraint applies between power generation, grid, and load. Steps 4-5) are based on the relevant constraint set established in steps 4-1) to 4-4), and the solution is performed with the goal of minimizing the overall operating cost of the system. The overall operating cost of the system includes the operating cost of hydropower stations, the operating cost of energy storage units, the cost of wind and solar curtailment, the cost of wind and solar curtailment penalties, the cost of flexible load transfer, and the cost of DC interconnection channel power adjustment. The operating cost of hydropower stations includes the power generation cost and the start-up and shutdown cost of hydropower stations. The operating cost of energy storage units includes the power generation cost, the energy storage cost, and the energy storage operation cost of energy storage units. The cost of wind and solar curtailment includes the cost of wind power curtailment and the cost of solar power curtailment. The cost of DC interconnection channel power adjustment includes the cost of purchasing and transmitting electricity. Steps 4-6) Based on the solution results of Steps 4-5), compare the intraday scheduling plan, adjust the output boundaries of hydropower stations and energy storage units, generate hydropower station adjustment plans and energy storage unit adjustment output plans, combine the inertia characteristics of various power sources, determine the configuration of purchased power and transmitted power, form a real-time scheduling plan, and provide decision-making for real-time scheduling.

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