A step water and scenery storage multi-capability complementary base daily scheduling optimization method and system

Abstract

The present application relates to the technical field of power system dispatching optimization, and discloses a kind of cascade water wind light storage multi-energy complementary base day-ahead dispatching optimization method and system, the method obtains cascade water power, pumped storage, wind power, photovoltaic and external sending channel data, calculates interval inflow and daily available total energy;With daily total energy proportion, peak-valley difference rate, peak time and peak length generation sending curve, and carry out truncation-redistribution processing;Establish the mixed integer linear programming model with system equivalent loss of minimum as target, considering cascade water balance, flow coupling, water head piecewise linearization, pumped storage operation and wind light output constraint, obtain day-ahead dispatching scheme, and carry out constraint conflict diagnosis and parameter sensitivity analysis to scene without feasible solution.The present application is used for the day-ahead external sending curve generation, dispatching optimization and parameter value evaluation of multi-energy complementary base, provides quantitative basis for clean energy base day-ahead external sending plan making.

Description

Technical Field

[0001] This invention belongs to the field of power system dispatch optimization technology, specifically relating to a day-ahead dispatch optimization method and system for a multi-energy complementary base of cascade hydropower, wind power, solar power, and energy storage. Background Technology

[0002] Multi-energy complementary clean energy bases represent an important engineering form for the centralized development and long-distance transmission of high proportions of renewable energy. These bases typically include multiple power sources such as cascade hydropower stations, pumped storage power stations, centralized wind farms, and photovoltaic power stations. The output of these various power sources is aggregated and transmitted to the receiving-end grid through transmission channels. In bases where the installed capacity significantly exceeds the capacity of the transmission channels, the key to day-ahead dispatch is not merely determining whether the total installed capacity is sufficient, but rather coordinating the hourly output of cascade hydropower, pumped storage, wind power, and photovoltaic power under a given transmission curve to achieve power balance in external transmission, reduce wind, solar, and hydropower curtailment, and improve the efficiency of resource utilization.

[0003] The power transmission curve is a crucial boundary condition for coordinating day-ahead power transmission plans between clean energy bases and receiving-end power grids. In scenarios where the installed capacity of the base exceeds its transmission capacity, the setting of power transmission curve parameters directly affects the feasibility and operational effectiveness of day-ahead dispatching schemes.

[0004] Existing technologies for day-ahead dispatching have the following main shortcomings: First, multi-basin cascade hydropower, pumped storage, wind power, photovoltaic power, and shared transmission channels are usually modeled in partial combinations, lacking a unified day-ahead dispatching optimization framework for base-level systems; second, power transmission curves are often treated as fixed inputs, optimized outputs, or single variables, making it difficult to uniformly generate and compare parameters of different power transmission curves; third, after adjusting power transmission curve parameters, existing methods usually cannot simultaneously provide dispatching feasibility judgments, infeasibility cause diagnoses, and parameter impact rankings, resulting in power transmission curve negotiation and parameter value selection still relying on a large amount of manual trial calculations. Summary of the Invention

[0005] To address the problems of insufficient system integration, limited representation of power transmission curves, and difficulty in quantitatively identifying the degree of parameter influence in existing multi-energy complementary clean energy bases, this invention provides a day-ahead scheduling optimization method and system for cascade hydro-wind-solar-storage multi-energy complementary bases. The method uses key parameters of the power transmission curve as controllable inputs, optimizes day-ahead scheduling with the goal of minimizing the equivalent curtailment loss of the system, and further identifies the relative importance of each parameter.

[0006] The technical solution adopted in this invention is as follows:

[0007] This invention provides a day-ahead scheduling optimization method for a multi-energy complementary hydropower, wind, solar, and energy storage base, comprising the following steps:

[0008] Step S1: Based on the base's cascade hydropower, wind power, solar power, and energy storage resources, and the transmission mode of the collected resources being sent to the receiving-end power grid via external transmission channels, construct resource scenario conditions that include various resource parameters and the capacity of the external transmission channels; wherein, the resource parameters include the parameters of each cascade hydropower station in the cascade hydropower system, the installed capacity and energy storage capacity of the pumped storage power station, and the available wind power output and available solar power output of the wind farm and photovoltaic power station respectively during each scheduling period of the day-ahead scheduling cycle;

[0009] Step S2: With the objective of maximizing the power generation of the cascade hydropower system within the day-ahead scheduling cycle, and in conjunction with the constraints of the cascade hydropower system, a sub-model of the cascade hydropower system is constructed. Using the parameters of each cascade hydropower station as input, the sub-model of the cascade hydropower system is solved independently to obtain the maximum available power output of each cascade hydropower station in each scheduling period of the day-ahead scheduling cycle under the same time-series feasible solution. The available power output of wind power and photovoltaic power in the same period is superimposed to obtain the sum of cascade hydropower and wind and solar power output in each scheduling period. The sum of the cascade hydropower and wind and solar power output in all scheduling periods of the day-ahead scheduling cycle is then added together and converted to obtain the total daily available energy.

[0010] Step S3: Using the total daily available energy as the normalized benchmark for power transmission energy, set power transmission curve parameters including the proportion of total daily energy, peak-valley difference rate, peak time, and peak duration. Multiply the total daily energy proportion and the total daily available energy to obtain the total daily power transmission capacity. Based on the power transmission curve parameters, use the ring time distance and raised cosine kernel algorithm to generate a temporary power transmission curve with single-peak characteristics. Post-process the time periods in the temporary power transmission curve that exceed the capacity of the external transmission channel using the truncation-redistribution algorithm to obtain a power transmission curve that satisfies the external transmission channel capacity constraint and keeps the total daily power transmission capacity unchanged.

[0011] Step S4: Construct a day-ahead scheduling mixed integer linear programming model, including minimizing the system's equivalent curtailment loss objective and model constraints within the day-ahead scheduling cycle; the equivalent curtailment loss includes wind curtailment loss, solar curtailment loss, equivalent hydropower curtailment loss, and a pumping cycle penalty term; the model constraints include cascade hydropower system constraints, pumped storage power station constraints, upper limit constraints on wind and solar power output, and system power balance constraints; the cascade hydropower system constraints are shared with the cascade hydropower system sub-model, including water balance constraints for cascade hydropower stations, cascade flow coupling constraints, upper and lower limit constraints on output considering exogenous operating state parameters, and constraints using the second type of special ordered set SOS2 to represent the head correlation piecewise linearly; the pumped storage power station constraints include energy storage balance constraints, power generation and pumping mutual exclusion constraints, energy storage capacity constraints, and initial and end-of-day energy storage constraints;

[0012] Step S5: Call the mixed-integer linear programming solver to solve the day-ahead scheduling mixed-integer linear programming model to obtain the output process of each cascade hydropower station, pumped storage power station, wind farm, and photovoltaic power station that conforms to the power transmission curve during each scheduling period, and output it as the day-ahead scheduling scheme; when the day-ahead scheduling mixed-integer linear programming model has no feasible solution, extract the irreducible inconsistency constraint subsystem and classify the reasons for infeasibility.

[0013] The day-ahead scheduling optimization method and system for a cascade hydropower-wind-solar-storage multi-energy complementary base provided by this invention has the following advantages:

[0014] (1) This invention constructs a unified day-ahead scheduling optimization framework for multi-energy complementary bases of cascade hydropower, wind power, solar power and storage, which can coordinate the constraints of multi-basin cascade hydropower, pumped storage, wind power, photovoltaic and transmission channels in the same model, and improve the problem that existing methods only model some power combinations and are difficult to support the formulation of base-level transmission plans.

[0015] (2) The present invention uses the daily total energy ratio, peak-valley difference rate, peak time and peak duration as controllable input parameters of the power transmission curve. The power transmission curve is generated by the ring time distance and raised cosine kernel, and is truncated-redistributed under the channel capacity constraint. This makes the generated curve meet both the daily power requirements and the external transmission channel capacity constraint, which is convenient for batch scenario analysis and power transmission curve parameter comparison.

[0016] (3) The present invention aims to minimize the equivalent curtailment loss of the system. It optimizes the wind curtailment loss, solar curtailment loss, equivalent water curtailment loss and pumping cycle penalty term by unifying them into the same energy scale. It also adopts the water head piecewise linearization to process the output relationship of cascade hydropower. While taking into account the influence of water head, it maintains the mixed integer linear structure of the model, thereby improving the solvability and engineering applicability of the model.

[0017] (4) When the model has no feasible solution, the present invention extracts irreducible inconsistent constraint subsystems and classifies the reasons for infeasibility according to the physical meaning of the constraints. It can identify sources of constraint conflicts such as insufficient water volume, energy storage exceeding the limit, cascade coupling limit or power balance limit, and reduce the cost of manual case-by-case investigation.

[0018] (5) This invention calculates the influence of power transmission curve parameters and pumped storage boundary conditions on scheduling results through multi-factor scenario analysis. It can rank the importance of key parameters and distinguish between loss components that can be adjusted by intraday scheduling and loss components that are more constrained by resource conditions and physical boundaries. This provides a quantitative basis for power transmission curve negotiation, pumped storage boundary setting and operation expectation assessment. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the overall process of the day-ahead scheduling optimization method provided in an embodiment of the present invention;

[0020] Figure 2 This is a schematic diagram illustrating the data acquisition and daily available total energy calculation process provided in an embodiment of the present invention;

[0021] Figure 3 This is a schematic diagram of the parameterized generation and verification process of the power transmission curve provided in an embodiment of the present invention;

[0022] Figure 4 This is a schematic diagram of the day-ahead scheduling modeling and solution process provided in an embodiment of the present invention;

[0023] Figure 5 This is a schematic diagram of the parameter sensitivity analysis and result output process provided in the embodiments of the present invention;

[0024] Figure 6 This is a schematic diagram of the parameterization generation process of the power transmission curve provided in an embodiment of the present invention;

[0025] Figure 7 This is a schematic diagram of the power supply curve truncation-redistribution post-processing flow provided in an embodiment of the present invention;

[0026] Figure 8 A schematic diagram of the topology of a cascade hydropower-wind-solar-storage multi-energy complementary base system provided in an embodiment of the present invention;

[0027] Figure 9 This is a schematic diagram of the module composition and data flow of the day-ahead scheduling optimization system provided in an embodiment of the present invention. Detailed Implementation

[0028] To make the technical problems solved, the technical solutions, and the beneficial effects of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and are not intended to limit the invention.

[0029] This invention provides a day-ahead scheduling optimization method and system for a multi-energy complementary base of cascade hydropower, wind power, solar power, and pumped storage. Under a unified framework, it generates power transmission curves, coordinates the output of multiple power sources, diagnoses the causes of infeasibility, and evaluates the degree of influence of power transmission curve parameters and pumped storage boundary conditions, thereby providing a quantitative basis for the formulation of day-ahead power transmission plans for clean energy bases.

[0030] Please see Figures 1 to 5 This invention provides a day-ahead scheduling optimization method for a cascade hydropower, wind, solar, and storage multi-energy complementary power base. The base may include cascade hydropower stations, pumped storage power stations, and centralized wind and solar power stations belonging to at least two river basins. The power output from these various sources is collected and transmitted to the receiving-end power grid via an external transmission channel, which may be a single ultra-high voltage direct current (UHVDC) transmission channel. The method includes steps S1 to S5. Based on steps S1 to S5, step S6 can be further set according to implementation needs.

[0031] Step S1, Obtain basic data:

[0032] Based on the base's cascade hydropower, wind power, solar power, and energy storage resources, and the transmission mode where the combined output of various resources is sent to the receiving-end power grid via external transmission channels, a system is constructed that includes various resource parameters and the capacity of the external transmission channels. The resource scenario conditions; wherein, the resource parameters include the parameters of each cascade hydropower station in the cascade hydropower system, the installed capacity and storage capacity of pumped storage power stations, and the available wind power output and available photovoltaic power output of wind farms and photovoltaic power stations in each scheduling period of the day-ahead scheduling cycle;

[0033] The parameters of the cascade hydropower stations include water level-reservoir capacity curves, natural runoff, installed capacity and minimum technical output. Based on the natural runoff, the inflow of each cascade hydropower station during each scheduling period of the day-ahead scheduling cycle is obtained by introducing the cascade hydraulic propagation time delay.

[0034] The specific calculation method for inbound flow is as follows:

[0035] The cascade hydropower system includes cascade hydropower systems belonging to at least two river basins; cascade hydropower stations are referred to as hydropower stations; for non-heading hydropower stations with adjacent upstream and downstream relationships within the same river basin, the inflow of the interval is calculated based on the difference between the natural runoff of the station's control section and the natural runoff of the upstream adjacent hydropower station's control section, and the negative difference results are corrected for non-negative values.

[0036] (1)

[0037] Determine the inbound flow , indicating hydroelectric power station During the scheduling period Inbound traffic:

[0038] The inflow of each cascade hydropower station at its head end is equal to its natural runoff; the inflow of each non-head end hydropower station is propagated from the upstream adjacent hydropower station through the cascade hydraulic propagation with time lag. The outflow is obtained by superimposing the inflow of the station section, including several scheduling periods at the beginning of the daytime scheduling cycle ( The known upstream historical outflow closed hydraulic propagation relationship prior to the day-ahead scheduling cycle is adopted.

[0039] The formulas for the inflow of each non-heading hydropower station are expressed as follows:

[0040] (2)

[0041] In the formula, For hydroelectric power station During the scheduling period The water flow rate (m³ / s) in the interval. For hydroelectric power station During the scheduling period Natural runoff (m³ / s). For hydroelectric power station upstream adjacent hydropower station During the scheduling period Natural runoff (m³ / s). For the upstream adjacent hydropower station Time delay of propagation via cascade hydraulic system The outflow after that (m³ / s). The known time delay of upstream adjacent hydropower stations via cascade hydraulic propagation prior to the current dispatch cycle. The subsequent historical outflow (m³ / s).

[0042] Step S2, calculate the total daily available energy:

[0043] With the objective of maximizing the power generation of the cascade hydropower system within the day-ahead scheduling cycle, and in conjunction with the constraints of the cascade hydropower system, a sub-model of the cascade hydropower system is constructed. Using the parameters of each cascade hydropower station as input, the sub-model of the cascade hydropower system is solved independently to obtain the maximum available power output of each cascade hydropower station in each scheduling period of the day-ahead scheduling cycle under the same time-series feasible solution. The available power output of wind power and photovoltaic power in the same period is superimposed to obtain the sum of cascade hydropower and wind and solar power output in each scheduling period. The sum of the cascade hydropower and wind and solar power output in all scheduling periods of the day-ahead scheduling cycle is then added together and converted to obtain the total daily available energy.

[0044] This invention divides the day-ahead scheduling period into There are 1 scheduling period, and the scheduling period number is... , Used only to indicate the initial state of the daytime scheduling cycle; peak time. A numbering system consistent with the scheduling time period is adopted. If the input is a natural hour scale, it is first converted into the corresponding scheduling time period number; when the scheduling time period step size is... When the duration is not 1 hour, the peak duration is and peak time according to After being converted to the corresponding number of scheduling periods or scheduling period numbers, they are used in the calculation. In this embodiment of the invention, the following is taken: , h.

[0045] This invention independently solves a sub-model with the objective of maximizing the power generation of a cascade hydropower system within the day-ahead scheduling cycle, thereby obtaining the maximum available output of each cascade hydropower station. This sub-model shares with the main model in step S4 the constraints of water balance, cascade flow coupling, output upper and lower limit constraints considering exogenous operating state parameters, and piecewise linearization of the head correlation using the second type of special ordered set SOS2. The only difference is that the objective is replaced with maximizing the power generation of the cascade hydropower. It should be noted that... The instantaneous maximum output is not obtained by independently maximizing each scheduling period, but rather by a set of time-series feasible output processes obtained from a cascade hydropower system sub-model with the maximum power generation of cascade hydropower within the same daily scheduling cycle, under the constraints of water balance, cascade flow coupling, output upper and lower limits considering exogenous operating state parameters, and piecewise linearization of head-related relationships using the second type of special ordered set SOS2. Based on this, the available output of wind power and photovoltaic power is superimposed to obtain the total daily available energy, calculated using the following formula:

[0046] (3)

[0047] In the formula, Total daily available energy (MWh); It is a collection of hydropower stations within a cascade hydropower system. For hydroelectric power stations; Number of scheduling periods For scheduling periods; For the optimal solution of the sub-model of the cascade hydropower system, the hydropower station During the scheduling period The maximum available output, that is, the output of each hydropower station given by the same time-series feasible solution while satisfying the constraints of the cascade hydropower system. During each scheduling period Maximum available output; , The respective scheduling periods Available wind power output and available solar power output (MWh); The time interval for dispatching is measured in hours (h). Pumped storage power stations only move energy within the daytime dispatch cycle and do not generate new electricity, therefore they are not included in the calculation. . This represents the normalized baseline of daily available energy under given inflow, reservoir capacity boundary, and available wind and solar power output conditions. This corresponds to the critical situation where the power transmission reaches the upper limit of the available energy from water, wind, and solar power.

[0048] Step S3, Power transmission curve parameterization:

[0049] Total daily available energy To establish a normalized benchmark for transmitted power, a proportion of total daily energy is set. ( Peak-valley difference rate Peak time and peak duration The power transmission curve parameters, relative to the daily total energy ratio Total daily available energy Perform multiplication to obtain the total daily power transmission volume. Based on the power transmission curve parameters, a temporary power transmission curve with single-peak characteristics is generated using a ring time distance and raised cosine kernel algorithm. For periods in the temporary power transmission curve exceeding the capacity of the external transmission channel, a truncation-redistribution algorithm is used for post-processing to obtain a power transmission curve that satisfies the external transmission channel capacity constraint and maintains a constant total daily power transmission volume. This invention's power transmission curve generation method has clearly defined physical meanings for its parameters, produces continuous and smooth curves, and is convenient for batch generation.

[0050] Step S3 specifically includes steps S31 to S34:

[0051] Step S31, taking into account the time loop of the peak moment across zero, that is, the time loop of the peak moment across zero within the day-ahead scheduling cycle, defines the ring time distance:

[0052] (4)

[0053] In the formula, For scheduling period The annular time distance, For scheduling periods, At peak time, Number of scheduling periods;

[0054] Step S32, construct the raised cosine kernel:

[0055] (5)

[0056] In the formula, For the raised cosine kernel, This is the peak duration, and ; The half-cycle length of the core as it decays to zero from the peak plateau boundary.

[0057] Step S33: Define the peak-valley difference rate using peak power as the normalization benchmark. :

[0058]

[0059] In the formula , These represent the peak power and valley power (MW) of the temporary power transmission curve to be generated; therefore... Combined with the daily electricity conservation condition The peak power and hourly temporary power transmission curves were obtained:

[0060] (6)

[0061] (7)

[0062] In the formula, For scheduling period The power of the temporary power transmission curve, , Total daily power supply The percentage of total daily energy consumption (MWh). Total daily available energy The time interval for scheduling; For the base load component, This is the peak modulation component.

[0063] Step S34, when the total daily power delivery meets the requirements. Under the premise that, if Exceeding the capacity of the external transmission channel during certain scheduling periods If the over-limit scheduling period is cut off to the external transmission channel capacity, the excess power will be redistributed to the remaining scheduling periods according to the proportion of the remaining capacity of each un-limited scheduling period. This process is repeated until all scheduling periods do not exceed the external transmission channel capacity, thus obtaining the power transmission curve. .

[0064] The specific implementation method for this step can be as follows:

[0065] If and only if the total daily power delivery meets the requirement At this time, the cut-off-redistribution approach has a feasible outcome of maintaining a constant total daily power transmission. However, if the temporary power transmission curve generated by equation (7) exceeds the capacity of the external transmission channel during certain scheduling periods... Then, a "truncation-redistribution" post-processing method is adopted: first, the over-limit scheduling period is truncated to the capacity of the external transmission channel, and the excess electricity in this round is recorded. Then The remaining capacity of each un-limited scheduling period is allocated to the remaining scheduling periods according to the proportion of remaining capacity in each un-limited scheduling period. Remaining capacity for each scheduling period Corresponding to the redistribution increment Repeat the above truncation and redistribution process until the excess electricity consumption during all scheduling periods is less than the given threshold. This process continues until no more scheduling slots are available. It ensures that the final power transmission curve meets the capacity constraints of the external transmission channel while maintaining a constant total daily power transmission. Within the parameter range of this invention, convergence is typically achieved in one or two iterations. .

[0066] Step S4, construct the day-ahead scheduling mixed-integer linear programming model:

[0067] A day-ahead scheduling mixed-integer linear programming model is constructed, including the objective of minimizing the system's equivalent curtailment loss within the day-ahead scheduling period and model constraints. The equivalent curtailment loss includes wind curtailment loss, solar curtailment loss, equivalent hydropower curtailment loss, and a pumping cycle penalty term. The model constraints include constraints on the cascade hydropower system, constraints on pumped storage power stations, upper limits of wind and solar power output, and system power balance constraints. The constraints on the cascade hydropower system are shared with the cascade hydropower system sub-model, including water balance constraints for cascade hydropower stations, cascade flow coupling constraints, upper and lower limits of output considering exogenous operating state parameters, and constraints on using the second type of special ordered set SOS2 to represent the head correlation piecewise linearly. The constraints on pumped storage power stations include energy storage balance constraints, mutual exclusion constraints between power generation and pumping, energy storage capacity constraints, and initial and end-of-day energy storage constraints.

[0068] Specifically, for clean energy bases where installed capacity significantly exceeds transmission capacity, the marginal generation cost of hydropower, wind power, and photovoltaic power can be approximated as zero. Therefore, traditional methods that aim to minimize total operating cost cannot effectively differentiate between different dispatch schemes. To balance wind curtailment loss, solar curtailment loss, equivalent hydropower curtailment loss, and pumping cycle penalty term on a unified energy scale, while maintaining the linear structure and solvability of the model, this invention aims to minimize the system's equivalent power curtailment loss.

[0069] (8)

[0070] Among them wind curtailment losses Waste light loss Equivalent water wastage loss and water pumping and circulation penalty items They are respectively:

[0071] (9)

[0072] (10)

[0073] (11)

[0074] (12)

[0075] In the formula, The equivalent curtailment loss of the system (MWh); , The respective scheduling periods The wind power output and photovoltaic power output (MW) adopted by the dispatch. , The respective scheduling periods Available wind power output and available photovoltaic power output; in equation (10) This is the photovoltaic curtailment weighting coefficient, and ,when The penalty for curtailment loss of solar power in the objective function is lower than that of curtailment loss of wind power, which is used to prioritize wind power retention when both wind and solar power can be equivalently reduced. Wind curtailment and solar curtailment are included in the objective function with equal weight; the solar curtailment weight coefficient is used to determine the wind and solar power selection rules under the equivalent optimal solution, and does not represent the difference in physical value between wind power and solar power. The output coefficient is (kW / (m³ / s·m)). For hydroelectric power station No. Net head (m) at the segment breakpoint; For hydroelectric power station During the scheduling period The wastewater flow rate is allocated to the first The component at the segment breakpoint (m³ / s); For hydroelectric power station The number of segments; in equation (12) The pumping cycle penalty coefficient is used to suppress frequent pumping-power generation cycles lacking engineering basis at the objective function level, so that when there are multiple equivalent or approximately equivalent feasible scheduling schemes, the model will prioritize the scheme with fewer pumping cycles and more stable operation; the physical efficiency loss of pumped storage is determined by the pumping efficiency in the energy storage balance constraint of equation (19). reflect, It is not used to repeatedly represent the same physical energy loss, but is set as a scheduling preference parameter or equivalent penalty parameter; Pumped storage power station During the scheduling period The pumping power (MW); the equivalent water loss in equation (11) adopts the same segmented weights as in equations (17)-(18). The product of head and water discharge is represented piecewise linearly to account for the change of water discharge with head while maintaining the linearity of the model; It is a collection of hydropower stations within a cascade hydropower system. For hydroelectric power stations; It is a collection of pumped storage power stations. It is a pumped storage power station; For the scheduling time interval step, For scheduling periods, The segment breakpoint number, This represents the number of scheduling periods.

[0076] The constraints of the cascade hydropower system include water balance, end-of-day reservoir capacity constraints, upper and lower limits of output considering exogenous operating parameters, and coupling of outflow composition with cascade flow rate.

[0077] (13)

[0078] (14)

[0079] (15)

[0080] (16)

[0081] The inflow to the reservoir of each cascade hydropower station at its head end is equal to its natural runoff, i.e. ( ); This represents the set of cascade first-end hydropower stations; the inflow of each non-first-end hydropower station is determined according to formula (2).

[0082] The power output of a hydropower station satisfies the physical relationship between power generation flow and net head, which is related to water energy conversion. This relationship is a bilinear physical relationship between head and flow rate. To maintain the mixed-integer linear structure of the model, this invention uses a special ordered set of the second type (SOS2) to represent the head-related relationship in a piecewise linearized manner during model solving: the feasible reservoir capacity interval is... Divided into The section introduces the SOS2 weight variable, a special type of ordered set. ( The head correlation is represented piecewise linearly using the segmented breakpoint numbers as the unit.

[0083] (17)

[0084] (18)

[0085] Among them, the weight variable sequence Each hydropower station and scheduling period is declared as a special ordered set of type 2, SOS2, which is natively supported by the solver.

[0086] In the formula, For hydroelectric power station During the scheduling period The final storage capacity (100 million m³). For hydroelectric power station During the scheduling period The final storage capacity, and Hydropower stations Storage capacity at the beginning and end of the current day's scheduling cycle; , , Hydropower stations During the scheduling period Inflow, power generation and outflow (m³ / s). For hydroelectric power station During the scheduling period The discharge flow rate; For hydroelectric power station During the scheduling period Power generation output (MW). For hydroelectric power station During the scheduling period The water purifier head, This is the output coefficient; For hydroelectric power station During the scheduling period Whether to take the exogenous operating state parameters involved in operation and without explicitly giving the shutdown state. , The flow-to-storage capacity conversion factor (hundred million m³ / (m³ / s·h)). , Hydropower stations Minimum technical output and installed capacity (MW). , Hydropower stations The dead storage capacity corresponds to the normal storage level. , , Hydropower stations No. Reservoir capacity, head, and maximum power generation flow at the segmented fault points For hydroelectric power station During the scheduling period The power generation flow is allocated to the first The component (m³ / s) at the segment breakpoint. For hydroelectric power station During the scheduling period The wastewater flow rate is allocated to the first The component (m³ / s) at the segmented breakpoint. For hydroelectric power station In the The upper bound of the water discharge at the segmented discontinuity is taken as a sufficiently large constant. For the weight variables of the second type of special ordered set SOS2, For hydroelectric power station The number of segments, The segment breakpoint number, For hydroelectric power stations, For scheduling periods, This represents the number of scheduling periods.

[0087] This invention focuses on base-level day-ahead scheduling and does not explicitly optimize the start-up and shutdown combinations of units within the hydropower station. If it is necessary to consider situations such as power station maintenance, shutdown, or planned non-participation in operation, exogenous operating status parameters can be used. The upper and lower limits of hydropower station output are adjusted. In this embodiment, all hydropower stations participate in operation, so the following is taken: .

[0088] The constraints of the pumped storage power station include energy storage balance, mutual exclusion between power generation and pumping, energy storage capacity limit, and initial and end-of-day energy storage constraints:

[0089] (19)

[0090] (20)

[0091] (twenty one)

[0092] In the formula, Pumped storage power station During the scheduling period End-of-life energy storage (MWh). Pumped storage power station During the scheduling period End-of-life energy storage and Pumped storage power stations Energy storage at the beginning and end of the current day's dispatch cycle; Pumping efficiency is used to describe the physical efficiency of converting electrical energy used for pumping water into releaseable stored energy. Pumped storage power station During the scheduling period Power generation capacity (MW). Pumped storage power station During the scheduling period Pumping power, Pumped storage power station The installed capacity (MW). The variable is a binary variable representing the power generation / pumping mode and Indicates pumped storage power station During the scheduling period For power generation mode, Pumped storage power station Energy storage capacity (MWh). , These are the initial energy storage ratio and the lower limit of the energy storage at the end of the day, respectively. The initial and end-of-day energy storage parameters are not fixed constants and can be used as scenario factors to characterize the temporal adjustment flexibility of pumped storage under different daily cycle strategies. It is a pumped storage power station. For scheduling periods, Number of scheduling periods The time interval for scheduling;

[0093] In equation (19), the pumping term includes pumping efficiency. The power generation item does not explicitly include an efficiency coefficient; if power generation efficiency is further considered... Then the power generation item should be In this embodiment, That is, the pumping efficiency of pumped storage includes all round-trip losses on the pumping side. ,at this time It indicates that electrical energy can be released, and energy is conserved throughout the entire process.

[0094] The upper limit constraints on wind power and photovoltaic power output and the system power balance constraints are as follows:

[0095] (twenty two)

[0096] (twenty three)

[0097] and, ;

[0098] In the formula, , The respective scheduling periods The dispatch adopts wind power and solar power output. , The respective scheduling periods Available wind power output and available solar power output, For hydroelectric power station During the scheduling period Power generation output, , Pumped storage power stations During the scheduling period The power generation capacity and pumping capacity, The power delivery curve (MW) generated in step S3. For external transmission channel capacity; It is a collection of hydropower stations within a cascade hydropower system. For hydroelectric power stations; It is a collection of pumped storage power stations. It is a pumped storage power station. This refers to the scheduling period.

[0099] Step S5, Solve and output:

[0100] The mixed-integer linear programming solver is invoked to solve the day-ahead scheduling mixed-integer linear programming model, obtaining the power output processes of each cascade hydropower station, pumped storage power station, wind farm, and photovoltaic power station that conform to the power transmission curve during each scheduling period, which are output as the day-ahead scheduling scheme. Simultaneously, wind curtailment loss, solar curtailment loss, equivalent hydropower curtailment loss, pumping cycle penalty term, and wind and solar absorption rate are also output. When the day-ahead scheduling mixed-integer linear programming model has no feasible solution, the irreducible inconsistency constraint subsystem (IIS) is extracted, and the reasons for infeasibility are categorized according to the physical meaning of its core constraints.

[0101] Step S6, Parameter Sensitivity Analysis (Further Implementation):

[0102] To identify the impact of various parameters of the power transmission curve and pumped storage boundary conditions on the scheduling results, a system is constructed that includes resource scenario conditions and daily total energy ratio. Peak-valley difference rate Peak time Peak duration The multi-factor scenario set with pumped storage boundary conditions is used to generate power transmission curves one by one according to steps S3 to S5 and solve the day-ahead scheduling mixed integer linear programming model.

[0103] For scenarios with no feasible solution, the reasons for infeasibility are classified according to the physical meaning of the core constraints involved in the irreducible inconsistency constraint subsystem.

[0104] In one optional implementation, for scenarios with no feasible solutions, an irreducible inconsistency constraint subsystem is extracted. Based on the type, physical meaning, and role of the constraints involved in the irreducible inconsistency constraint subsystem in the scheduling model, the infeasibility causes are classified into one or more engineering causes. The engineering causes may include insufficient water, energy storage exceeding limits, wind and solar constraints binding, cascade coupling limitations, power balance limitations, and other constraint conflicts triggered by engineering boundary conditions. When the same irreducible inconsistency constraint subsystem involves multiple types of core constraints, a single type of attribution can be performed according to the dominant constraint, or multiple infeasibility labels can be output to reflect the situation where multiple engineering boundaries jointly lead to infeasibility.

[0105] For scenarios with feasible solutions, at least one of the following is selected as a response indicator: wind and solar absorption rate, equivalent power curtailment loss, dispatch feasibility index, equivalent water curtailment loss, or pumping cycle penalty term. The influence of each factor and its interaction term on the response indicator is calculated, and the importance of each power transmission curve parameter and pumped storage boundary condition is ranked according to the degree of influence.

[0106] In one alternative implementation, a type III analysis of variance is used to decompose the main effects and interaction effects of each factor in feasible scenarios, using effect sizes. Measure the proportion of variance explained by each factor, and use Statistics and The statistical significance was tested using the value test; further, the significance of differences between factor levels was tested using the Tukey HSD post-hoc multiple comparison test. The influence of parameters can also be calculated using main effect range, regression models, variance decomposition, or other sensitivity analysis methods, depending on the engineering needs. In the formula, , These are the sum of squares of the factors and the total sum of squares, respectively.

[0107] This invention also provides a day-ahead scheduling optimization system for a cascade hydropower-wind-solar-storage multi-energy complementary base, used to implement the aforementioned day-ahead scheduling optimization method for a cascade hydropower-wind-solar-storage multi-energy complementary base, comprising:

[0108] (1) Data acquisition module, used to acquire static and dynamic data and transmission channel capacity of each cascade hydropower station, pumped storage power station and wind power and photovoltaic power station in the base; for non-heading power stations with adjacent upstream and downstream relationships in the same basin, calculate the inflow of water in the interval according to formula (1), and determine the inflow flow according to formula (2) based on the outflow of the upstream adjacent hydropower station after hydraulic propagation time lag and the historical outflow before the scheduling date.

[0109] (2) Available energy calculation module, used to solve the sub-model of the cascade hydropower system with the maximum power generation of the cascade hydropower as the target and superimpose the available output of wind power and photovoltaic power, and obtain the daily available total energy as the normalization benchmark for power transmission energy according to formula (3);

[0110] (3) Power transmission curve parameterization module, which is used to generate power transmission curves according to equations (4) to (7) with the daily total energy ratio, peak-valley difference rate, peak time and peak duration as inputs and perform truncation-redistribution post-processing;

[0111] (4) Scheduling modeling and solving module, used to establish and solve the mixed integer linear programming model with the goal of minimizing the equivalent power curtailment loss of the system according to equations (8) to (23) to obtain the day-ahead scheduling scheme;

[0112] (5) Infeasibility diagnosis module, used to extract irreducible inconsistent constraint subsystems when there is no feasible solution in the model, and classify the reasons for infeasibility according to the physical meaning of the core constraints involved;

[0113] (6) Parameter sensitivity analysis module, used to perform sensitivity analysis on the batch solution results of multi-factor scenarios, calculate the influence of each factor and its interaction terms on the response index, and sort the power transmission curve parameters and pumped storage boundary conditions by importance.

[0114] (7) Result output module, used to output the day-ahead scheduling scheme, the composition of equivalent power curtailment loss, the infeasibility diagnosis results and the parameter sensitivity analysis results.

[0115] The data flow of the above modules is as follows: the output of the data acquisition module is sent to the available energy calculation module and the power transmission curve parameterization module; the outputs of the power transmission curve parameterization module and the data acquisition module are sent to the scheduling modeling and solving module; when a feasible solution is obtained, the scheduling scheme is output by the result output module; when there is no feasible solution, the infeasibility diagnosis module classifies the reasons; in multi-factor scenarios, the parameter sensitivity analysis module performs sensitivity analysis on the batch results and then the result output module outputs the conclusion.

[0116] The system described in this invention can be implemented by a computing device running a corresponding computer program. The computing device includes a processor and a memory. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above method.

[0117] The beneficial effects of this invention are as follows:

[0118] (1) This invention constructs a unified day-ahead scheduling optimization framework for multi-energy complementary bases of cascade hydropower, wind power, solar power and storage, which can coordinate the constraints of multi-basin cascade hydropower, pumped storage, wind power, photovoltaic and transmission channels in the same model, and improve the problem that existing methods only model some power combinations and are difficult to support the formulation of base-level transmission plans.

[0119] (2) The present invention uses the daily total energy ratio, peak-valley difference rate, peak time and peak duration as controllable input parameters of the power transmission curve. The power transmission curve is generated by the ring time distance and raised cosine kernel, and is truncated-redistributed under the channel capacity constraint. This makes the generated curve meet both the daily power requirements and the external transmission channel capacity constraint, which is convenient for batch scenario analysis and power transmission curve parameter comparison.

[0120] (3) The present invention aims to minimize the equivalent curtailment loss of the system. It optimizes the wind curtailment loss, solar curtailment loss, equivalent water curtailment loss and pumping cycle penalty term by unifying them into the same energy scale. It also adopts the water head piecewise linearization to process the output relationship of cascade hydropower. While taking into account the influence of water head, it maintains the mixed integer linear structure of the model, thereby improving the solvability and engineering applicability of the model.

[0121] (4) When the model has no feasible solution, the present invention extracts irreducible inconsistent constraint subsystems and classifies the reasons for infeasibility according to the physical meaning of the constraints. It can identify sources of constraint conflicts such as insufficient water volume, energy storage exceeding the limit, cascade coupling limit or power balance limit, and reduce the cost of manual case-by-case investigation.

[0122] (5) This invention calculates the influence of power transmission curve parameters and pumped storage boundary conditions on scheduling results through multi-factor scenario analysis. It can rank the importance of key parameters and distinguish between loss components that can be adjusted by intraday scheduling and loss components that are more constrained by resource conditions and physical boundaries. This provides a quantitative basis for power transmission curve negotiation, pumped storage boundary setting and operation expectation assessment.

[0123] The present invention will be further described in detail below with reference to the accompanying drawings. The embodiments described below are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0124] The overall flow of the method of the present invention is as follows: Figure 1 As shown, the process for each stage is as follows: Figures 2 to 5 As shown: First, data acquisition and calculation of total daily available energy are performed, the process is as follows. Figure 2 As shown; secondly, a power transmission curve is generated using four parameters: daily total energy ratio, peak-to-valley difference rate, peak time, and peak duration. The process is as follows: Figure 3 As shown, the parameterization generation process is as follows: Figure 6 As shown, time periods exceeding the outgoing channel capacity are processed using a truncation-redistribution method, as follows: Figure 7 As shown; subsequently, a mixed-integer linear programming model with the objective of minimizing the system's equivalent power curtailment loss is constructed and solved to obtain the day-ahead scheduling scheme, the process of which is as follows. Figure 4 As shown; in a multi-factor scenario, further infeasibility diagnosis and parameter sensitivity analysis are performed on the batch results to obtain the parameter importance ranking, the process of which is as follows: Figure 5 As shown. The module composition and data flow of the system of this invention are as follows. Figure 9 As shown.

[0125] Example 1

[0126] This embodiment uses a large-scale clean energy base in Southwest China as an example to illustrate the specific implementation process of the method of the present invention. The base includes two cascade hydropower systems (CYQ and YQH basins), two pumped storage power stations (KWBQ and TL), and a BS wind farm, a BS photovoltaic power station, and a ZG photovoltaic power station. The BS wind farm and the BS photovoltaic power station utilize wind and solar resources from the same region. The power output from all sources is collected and transmitted to the GBA receiving-end grid via a single ultra-high voltage direct current (UHVDC) transmission channel. The transmission channel capacity... MW. System topology as follows: Figure 8 As shown, the cascade structures of the two basins are as follows: the CYQ basin is GY→LM→TG→GL→ZL-CYQ, and the YQH basin is ZB→BT→ZL-YQH.

[0127] (1) Input data and parameter settings

[0128] The main parameters of each resource are shown in Table 1.

[0129] Table 1. Main parameters of various resources in the clean energy base

[0130] Resource types name Installed capacity (MW) Minimum output(MW) Adjusting storage capacity (100 million m³) Adjustment performance Energy storage capacity (MWh) hydroelectric power station GY 110 9.8 0.007 Daily Adjustment — hydroelectric power station LM 120 9 0.016 Daily Adjustment — hydroelectric power station TG 205 17 0.002 Daily Adjustment — hydroelectric power station GL 110 14.6 0.350 Seasonal adjustment — hydroelectric power station ZL-CYQ 520 55.1 0.046 Daily Adjustment — hydroelectric power station ZB 160 40 0.926 Annual adjustment — hydroelectric power station BT 292 73 0.072 Daily Adjustment — hydroelectric power station ZL-YQH 810 67.5 0.009 Daily Adjustment — Pumped storage KWBQ 2250 — — — 13500 Pumped storage TL 2700 — — — 16200 wind farm BS 560 — — — — Photovoltaic power station BS 16990 — — — — Photovoltaic power station ZG 4010 — — — —

[0131] The main parameters of the model are set as follows: output coefficient kW / (m³ / s·m), pumping circulation penalty coefficient Pumping efficiency (Power generation efficiency is taken as 1.0), photovoltaic curtailment weighting coefficient Cascade hydraulic propagation time delay h, number of scheduling periods Scheduling time interval step h, flow-to-capacity conversion factor 100 million m³ / (m³ / s·h). In SOS2 piecewise linearization, the CYQ watershed is taken as... YQH watershed Each segment node is within the feasible range of reservoir capacity. Evenly distributed within, corresponding to water head With maximum power generation flow The values ​​were obtained by linear interpolation of the water level-reservoir capacity static curve and the unit output characteristics; since all hydropower stations are involved in operation, the values ​​were taken as follows: Since the site is in the planning stage, the typical daily input of the dam site's multi-year average monthly runoff structure was adopted, and January and July were selected to represent the dry season and flood season, respectively.

[0132] (2) Generation of power transmission curve

[0133] The total daily available energy is calculated according to step S2: during the dry season. MWh, during the flood season MWh. Following step S3, a power transmission curve is generated using four parameters: daily total energy ratio, peak-to-valley difference rate, peak time, and peak duration. The parameterization process and the post-processing of cutoff-redistribution are as follows: Figure 6 , Figure 7 As shown.

[0134] (3) Day-ahead scheduling solution

[0135] Taking Case A2B2C1D2E1 as a representative scenario (corresponding to a medium power transmission energy level, a neutral curve shape, a peak time located during the midday high photovoltaic output period, and relatively relaxed pumped storage boundary conditions), a mixed integer linear programming solver was used to solve the problem, and the scheduling results during the dry season and flood season are shown in Table 2.

[0136] Table 2. Representative Scenarios A2B2C1D2E1: Dispatch Results During Dry and Flood Seasons

[0137] index Dry season flood season Wind and solar energy absorption rate 79.4% 98.3% Wasted light (MWh) 21178 830 Hydropower wastewater loss (MWh) 0 9011 Pumped storage cycle loss (MWh) 9524 4785 Total pumped storage power generation (MWh) 36436 10698 Total pumping power of pumped storage hydropower (MWh) 38097 19139

[0138] As shown in Table 2, the main limiting factors are different in the two seasons: during the dry season, the scheduling is mainly constrained by the insufficient pumping capacity of pumped storage, making it difficult to fully absorb the excess photovoltaic power at noon; during the flood season, the high natural water inflow and limited cascade power generation capacity make hydropower tracking and water abandonment management the main operational constraints.

[0139] (4) Infeasible diagnosis and parameter sensitivity analysis

[0140] Construct a multi-factor scenario set according to step S6. The level settings of each factor are shown in Table 3, and the level definitions of each level of pumped storage boundary conditions are shown in Table 4.

[0141] Table 3. Parameter Space for Full Factorial Design

[0142] factor symbol Horizontal value season - January (dry season), July (flood season) A (Daily Total Energy Ratio) A0–A4=0.40, 0.55, 0.70, 0.80, 0.90 B (Peak-Valley Difference Rate) B1–B3 = 0.15, 0.35, 0.55 C (Peak moment) C1–C3 = 12, 18, 21 D (Peak Duration) D1–D3 = 2, 4, 7 E (Pumped storage boundary condition) - E1–E5, see Table 4

[0143] Table 4 Definitions of Pumped Storage Boundary Conditions at Each Level

[0144] E level End-of-day energy storage lower limit E1 80% Unrestrained E2 80% E3 50% Unrestrained E4 50% E5 30%

[0145] For scenarios with no feasible solutions, irreducible inconsistency constraint subsystems are extracted, and the reasons for infeasibility are categorized according to the physical meaning of the constraints they contain. When multiple types of core constraints are involved simultaneously, they are categorized according to the dominant constraint or treated with multiple labels. For scenarios with feasible solutions, the influence of each factor and its interaction terms is calculated using wind and solar power absorption rates as response indicators, and the importance of power transmission curve parameters and pumped storage boundary conditions is ranked. The analysis shows that scheduling feasibility and wind and solar power absorption rates mainly depend on resource scenario conditions, daily total energy ratio, and pumped storage boundary conditions. There is a significant interaction between the daily total energy ratio and pumped storage boundary conditions, meaning that "how much electricity to transmit" and "how much regulation margin to retain for pumped storage" need to be decided jointly. In contrast, the influence of curve shape parameters such as peak-valley difference rate, peak time, and peak duration is relatively weak. Among them, the peak time retains a limited but stable effect due to changing the timing alignment of the power transmission peak with the noon photovoltaic window. The main reasons for the infeasibility are related to the over-limit of pumped storage, the over-limit of hydropower reservoir capacity, and the superposition of the two, rather than the upper limit of wind and solar power output or instantaneous power balance itself. This shows that the feasibility of the system is essentially a boundary problem determined by the regulation capacity rather than the installed capacity.

[0146] (5) Implementation results

[0147] As can be seen from this embodiment, the method of the present invention can use key parameters of the power transmission curve as controllable inputs within a unified framework, coordinate the day-ahead scheduling optimization of cascade hydropower, pumped storage, and wind and solar power, and quantitatively identify the relative importance and interaction effects of each parameter and the boundary conditions of pumped storage. For clean energy bases where the installed capacity significantly exceeds the transmission capacity, power transmission curve negotiation should prioritize coordinating the daily total energy ratio and the boundary conditions of pumped storage, rather than treating all curve shape parameters equally. At the same time, it can distinguish between loss components that can be adjusted through intraday scheduling (such as curtailment of solar power and pumped storage cycle penalties) and loss components that are more constrained by resource conditions and physical boundaries (such as equivalent water curtailment loss under high inflow conditions), providing a quantitative basis for power transmission curve values ​​and operational expectation assessment.

[0148] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications, equivalent substitutions, or variations can be made to the technical solutions of the present invention without departing from the spirit and substance of the present invention. For example, the number of cascade hydropower stations and pumped storage power stations, the number of river basins, the form of the kernel function of the power transmission curve, or the level values ​​of multi-factor scenarios can be adjusted. All technical solutions falling within the scope of the claims of the present invention and their equivalent technical features should be included within the scope of protection of the present invention.

Claims

1. A day-ahead scheduling optimization method for a cascade hydropower-wind-solar-storage multi-energy complementary base, characterized in that, Includes the following steps: Step S1: Based on the base's cascade hydropower, wind power, solar power, and energy storage resources, and the transmission mode of the collected resources being sent to the receiving-end power grid via external transmission channels, construct resource scenario conditions that include various resource parameters and the capacity of the external transmission channels; wherein, the resource parameters include the parameters of each cascade hydropower station in the cascade hydropower system, the installed capacity and energy storage capacity of the pumped storage power station, and the available wind power output and available solar power output of the wind farm and photovoltaic power station respectively during each scheduling period of the day-ahead scheduling cycle; Step S2: With the objective of maximizing the power generation of the cascade hydropower system within the day-ahead scheduling cycle, and in conjunction with the constraints of the cascade hydropower system, a sub-model of the cascade hydropower system is constructed. Using the parameters of each cascade hydropower station as input, the sub-model of the cascade hydropower system is solved independently to obtain the maximum available power output of each cascade hydropower station in each scheduling period of the day-ahead scheduling cycle under the same time-series feasible solution. The available power output of wind power and photovoltaic power in the same period is superimposed to obtain the sum of cascade hydropower and wind and solar power output in each scheduling period. The sum of the cascade hydropower and wind and solar power output in all scheduling periods of the day-ahead scheduling cycle is then added together and converted to obtain the total daily available energy. Step S3: Using the total daily available energy as the normalized benchmark for power transmission energy, set power transmission curve parameters including the proportion of total daily energy, peak-valley difference rate, peak time, and peak duration. Multiply the total daily energy proportion and the total daily available energy to obtain the total daily power transmission capacity. Based on the power transmission curve parameters, use the ring time distance and raised cosine kernel algorithm to generate a temporary power transmission curve with single-peak characteristics. Post-process the time periods in the temporary power transmission curve that exceed the capacity of the external transmission channel using the truncation-redistribution algorithm to obtain a power transmission curve that satisfies the external transmission channel capacity constraint and keeps the total daily power transmission capacity unchanged. Step S4: Construct a day-ahead scheduling mixed integer linear programming model, including minimizing the system's equivalent curtailment loss objective and model constraints within the day-ahead scheduling cycle; the equivalent curtailment loss includes wind curtailment loss, solar curtailment loss, equivalent hydropower curtailment loss, and a pumping cycle penalty term; the model constraints include cascade hydropower system constraints, pumped storage power station constraints, upper limit constraints on wind and solar power output, and system power balance constraints; the cascade hydropower system constraints are shared with the cascade hydropower system sub-model, including water balance constraints for cascade hydropower stations, cascade flow coupling constraints, upper and lower limit constraints on output considering exogenous operating state parameters, and constraints using the second type of special ordered set SOS2 to represent the head correlation piecewise linearly; the pumped storage power station constraints include energy storage balance constraints, power generation and pumping mutual exclusion constraints, energy storage capacity constraints, and initial and end-of-day energy storage constraints; Step S5: Call the mixed-integer linear programming solver to solve the day-ahead scheduling mixed-integer linear programming model to obtain the output process of each cascade hydropower station, pumped storage power station, wind farm, and photovoltaic power station that conforms to the power transmission curve during each scheduling period, and output it as the day-ahead scheduling scheme; when the day-ahead scheduling mixed-integer linear programming model has no feasible solution, extract the irreducible inconsistency constraint subsystem and classify the reasons for infeasibility.

2. The day-ahead scheduling optimization method for a cascade hydropower-wind-solar-storage multi-energy complementary base according to claim 1, characterized in that, The parameters of the cascade hydropower stations include water level-reservoir capacity curves, natural runoff, installed capacity, and minimum technical output. Based on the natural runoff, the inflow of each cascade hydropower station during each scheduling period of the day-ahead scheduling cycle is obtained by introducing the hydraulic propagation time lag of the cascade. The specific method is as follows: The cascade hydropower system includes cascade hydropower systems belonging to at least two river basins; cascade hydropower stations are referred to simply as hydropower stations; For non-heading hydropower stations with adjacent upstream and downstream relationships within the same basin, the inflow of the interval is calculated based on the difference between the natural runoff at the control section of the station and the natural runoff at the control section of the adjacent upstream hydropower station, and negative difference results are corrected for non-negative values. The inflow of each non-heading hydropower station is obtained by superimposing the outflow of the adjacent upstream hydropower station after the time lag of the cascade hydraulic propagation with the inflow of the station. The initial scheduling periods of the day-ahead scheduling cycle adopt the known closed hydraulic propagation relationship of the upstream historical outflow before the day-ahead scheduling cycle. The inflow of each cascade heading hydropower station is equal to its natural runoff.

3. The day-ahead scheduling optimization method for a cascade hydropower-wind-solar-storage multi-energy complementary base according to claim 2, characterized in that, The calculation method for the inflow of cascade hydropower stations during each scheduling period of the day-ahead scheduling cycle is as follows: Calculate the water inflow within the interval: (1) Determine the inbound flow , indicating hydroelectric power station During the scheduling period Inbound traffic; in: The inflow of each cascade hydropower station at its head end is equal to its natural runoff; Inflow rates of non-heading hydropower stations: (2) In the formula, For hydroelectric power station During the scheduling period Water coming from the area, For hydroelectric power station During the scheduling period Natural runoff, For hydroelectric power station upstream adjacent hydropower station During the scheduling period Natural runoff, For the upstream adjacent hydropower station Time delay of propagation via cascade hydraulic system The subsequent outflow, The known time delay of upstream adjacent hydropower stations via cascade hydraulic propagation prior to the current dispatch cycle. The subsequent historical outflow.

4. The day-ahead scheduling optimization method for a cascade hydropower-wind-solar-storage multi-energy complementary base according to claim 1, characterized in that, The formula for calculating the total daily usable energy is: (3) In the formula, Total daily available energy; It is a collection of hydropower stations within a cascade hydropower system. For hydroelectric power stations; Number of scheduling periods For scheduling periods; For the optimal solution of the sub-model of the cascade hydropower system, the hydropower station During the scheduling period The maximum available output, that is, the output of each hydropower station given by the same time-series feasible solution while satisfying the constraints of the cascade hydropower system. During each scheduling period Maximum available output; , Distributed to scheduling periods Available wind power output and available solar power output; The time interval for dispatching is [not specified]. Pumped storage power stations only move energy within the daytime dispatch cycle and do not generate new electricity, therefore they are not included in the calculation. .

5. The day-ahead scheduling optimization method for a cascade hydropower-wind-solar-storage multi-energy complementary base according to claim 1, characterized in that, Step S3 specifically includes: Step S31, taking into account the time wraparound of the peak moment across zero, define the ring time distance: (4) In the formula, For scheduling period The annular time distance, For scheduling periods, At peak time, Number of scheduling periods; Step S32, construct the raised cosine kernel: (5) In the formula, For the raised cosine kernel, This is the peak duration, and ; Step S33: Define the peak-valley difference rate using peak power as the normalization benchmark. In the formula , These represent the peak and valley power values ​​of the temporary power transmission curve to be generated; combined with the daily power conservation condition. The peak power and hourly temporary power transmission curves were obtained: (6) (7) In the formula, For scheduling period The power of the temporary power transmission curve, , Total daily power supply The proportion of total daily energy. Total daily available energy The time interval for scheduling; Step S34, when the total daily power delivery meets the requirements. Under the premise that, if Exceeding the capacity of the external transmission channel during certain scheduling periods If the over-limit scheduling period is cut off to the external transmission channel capacity, the excess power will be redistributed to the remaining scheduling periods according to the proportion of the remaining capacity of each un-limited scheduling period. This process is repeated until all scheduling periods do not exceed the external transmission channel capacity, thus obtaining the power transmission curve. .

6. The day-ahead scheduling optimization method for a cascade hydropower-wind-solar-storage multi-energy complementary base according to claim 1, characterized in that, In step S4, the objective of minimizing the system's equivalent power curtailment loss within the day-ahead scheduling cycle is: (8) Among them wind curtailment losses Waste light loss Equivalent water wastage loss and water pumping and circulation penalty items They are respectively: (9) (10) (11) (12) In the formula, This is the equivalent power curtailment loss of the system; , Distributed to scheduling periods The dispatch adopts wind power and solar power output. , Distributed to scheduling periods Available wind power output and available solar power output; This is the photovoltaic curtailment weighting coefficient, and ,when The penalty for curtailment of solar power in the objective function is lower than that for curtailment of wind power. This is used to prioritize wind power when both wind and solar power can be reduced equally. The weighting coefficient for solar power curtailment does not represent the difference in physical value between wind and solar power. This is the output coefficient; For hydroelectric power station No. Clean water head at the segment break point; For hydroelectric power station During the scheduling period The wastewater flow rate is allocated to the first The components at the segment breakpoints; For hydroelectric power station The number of segments; This is the pumping cycle penalty coefficient, used to suppress frequent pumping-power generation cycles lacking engineering basis at the objective function level. The physical efficiency loss of pumped storage is reflected in the pumping efficiency within its energy storage balance constraint. Not used to repeatedly represent the same physical energy loss; Pumped storage power station During the scheduling period Pumping power; It is a collection of hydropower stations within a cascade hydropower system. For hydroelectric power stations; It is a collection of pumped storage power stations. It is a pumped storage power station; For the scheduling time interval step, For scheduling periods, The segment breakpoint number, This represents the number of scheduling periods.

7. The day-ahead scheduling optimization method for a cascade hydropower-wind-solar-storage multi-energy complementary base according to claim 1, characterized in that, The constraints of the cascade hydropower system include: (13) (14) (15) (16) The power output of a hydropower station satisfies the physical relationship between power generation flow and net head, which is related to water energy conversion. To maintain the mixed-integer linear structure of the model, the feasible interval of storage capacity is... Divided into The section introduces the SOS2 weight variable, a special type of ordered set. The relationship between water head is represented by piecewise linearization: (17) (18) Among them, the weight variable sequence Each hydropower station and its dispatching period is declared as a special ordered set of type 2, SOS2; In the formula, For hydroelectric power station During the scheduling period The final storage capacity, For hydroelectric power station During the scheduling period The final storage capacity, and Hydropower stations Storage capacity at the beginning and end of the current day's scheduling cycle; , , Hydropower stations During the scheduling period Inflow, power generation, and outflow; For hydroelectric power station During the scheduling period The discharge flow rate; For hydroelectric power station During the scheduling period Power generation output, For hydroelectric power station During the scheduling period The water purifier head, This is the output coefficient; For hydroelectric power station During the scheduling period Whether to take the exogenous operating state parameter that participates in the operation and without explicitly giving the shutdown state. , This is the flow-to-capacity conversion factor. , Hydropower stations Minimum technical output and installed capacity, , Hydropower stations The dead storage capacity corresponds to the normal storage level. , , Hydropower stations No. Reservoir capacity, head, and maximum power generation flow at the segmented fault points For hydroelectric power station During the scheduling period The power generation flow is allocated to the first The components at the segment breakpoints For hydroelectric power station During the scheduling period The wastewater flow rate is allocated to the first The components at the segment breakpoints For hydroelectric power station In the The upper bound of the water discharge at the segmented discontinuity is taken as a sufficiently large constant. For the second type of special ordered set SOS2 weight variables, For hydroelectric power station The number of segments, The segment breakpoint number, For hydroelectric power stations, For scheduling periods, This represents the number of scheduling periods.

8. The day-ahead scheduling optimization method for a cascade hydropower-wind-solar-storage multi-energy complementary base according to claim 1, characterized in that, The constraints of the pumped storage power station include: (19) (20) (21) In the formula, Pumped storage power station During the scheduling period End-of-life energy storage Pumped storage power station During the scheduling period End-of-life energy storage and Pumped storage power stations Energy storage at the beginning and end of the current day's dispatch cycle; Pumping efficiency is used to describe the physical efficiency of converting electrical energy used for pumping water into releaseable stored energy. Pumped storage power station During the scheduling period Power generation capacity, Pumped storage power station During the scheduling period Pumping power, Pumped storage power station The installed capacity, The variable is a binary variable representing the power generation / pumping mode and Indicates pumped storage power station During the scheduling period For power generation mode, Pumped storage power station Energy storage capacity, , These are the initial energy storage ratio and the lower limit of energy storage at the end of the day, respectively. It is a pumped storage power station. For scheduling periods, Number of scheduling periods The time interval for scheduling; The upper limit constraints on wind power and photovoltaic power output and the system power balance constraints are as follows: (22) (23) and, ; In the formula, , Distributed to scheduling periods The dispatch adopts wind power and solar power output. , Distributed to scheduling periods Available wind power output and available solar power output, For hydroelectric power station During the scheduling period Power generation output, , Pumped storage power stations During the scheduling period The power generation capacity and pumping capacity, For the power transmission curve, For external transmission channel capacity; It is a collection of hydropower stations within a cascade hydropower system. For hydroelectric power stations; It is a collection of pumped storage power stations. It is a pumped storage power station. This refers to the scheduling period.

9. The day-ahead scheduling optimization method for a cascade hydropower-wind-solar-storage multi-energy complementary base according to claim 1, characterized in that, Also includes: Step S6, Parameter Sensitivity Analysis: The structure includes resource scenario conditions and daily total energy ratio. Peak-to-valley difference Peak time Peak duration A multi-factor scenario set with pumped storage boundary conditions is used to generate power transmission curves one by one and solve the day-ahead scheduling mixed integer linear programming model. For scenarios with no feasible solutions, the reasons for infeasibility are classified according to the physical meaning of the core constraints involved in the irreducible inconsistency constraint subsystem. For scenarios with feasible solutions, at least one of the following is selected as the response index: wind and solar absorption rate, equivalent curtailment loss, scheduling feasibility index, equivalent water curtailment loss, or pumping cycle penalty term. The influence of each factor and its interaction term on the response index is calculated, and the importance of each power transmission curve parameter and pumped storage boundary condition is ranked according to the degree of influence.

10. A day-ahead scheduling optimization system for a cascade hydropower-wind-solar-storage multi-energy complementary base, characterized in that: The day-ahead scheduling optimization system for the cascade hydropower-wind-solar-storage multi-energy complementary base is used to implement the day-ahead scheduling optimization method for the cascade hydropower-wind-solar-storage multi-energy complementary base as described in any one of claims 1 to 9.

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