Day-ahead layered optimization scheduling method considering hybrid pumping storage and thermal power depth peak regulation

Abstract

The invention discloses a day-ahead layered optimization scheduling method considering hybrid pumping storage and thermal power depth peak regulation. The method comprises the steps that cost and power generation models of a thermal power generating unit in different peak regulation stages are established; an upper-layer scheduling model with the minimum residual load variance after hybrid pumping and storage adjustment as a target is established; establishing a lower-layer scheduling model with the goal of minimizing the peak regulation cost and the new energy power abandoning cost of the thermal power generating unit; establishing a multi-energy system day-ahead hierarchical optimization scheduling strategy considering hybrid pumping storage and thermal power depth peak regulation, and converting the model into a hybrid integer linear programming model by using a linearization technology; solving by adopting a mixed integer linear programming algorithm to obtain output of each power supply and a scheduling scheme; by establishing the hierarchical scheduling model, the deep peak regulation frequency of the thermal power generating unit is reduced, the operation cost is reduced, the output is more stable, and meanwhile, benefited from mixed pumped storage, the new energy consumption effect of the system is remarkably improved, and the power abandonment rate is reduced.

Description

Technical Field

[0001] This invention relates to the field of multi-energy complementary power generation technology, and in particular to a day-ahead hierarchical optimization scheduling method that takes into account hybrid pumped storage and deep peak shaving of thermal power. Background Technology

[0002] With the large-scale grid connection of new energy sources such as wind and solar power, the randomness and volatility of wind and solar power output, as well as their strong anti-peak-shaving characteristics, have brought extremely severe peak-shaving problems to the power system. Currently, the peak-shaving task of the power grid is mainly undertaken by thermal power units. As the installed capacity of wind and solar power continues to expand, in order to better solve the problem of new energy consumption and alleviate the peak-shaving dilemma of the power system, various power grids have carried out deep peak-shaving of thermal power units, making the units operate at about 30% to 50% of their rated power. However, deep peak-shaving of thermal power units will cause a series of changes in the operating conditions of the units, and the corresponding operating costs will also increase. How to balance peak-shaving performance and economic performance is the key to the operation of thermal power units. Energy storage has the advantages of fast response speed, bidirectional power regulation, and clean and pollution-free operation. Using energy storage to participate in auxiliary peak-shaving of thermal power units and giving full play to the role of energy storage has become one of the important means to solve the peak-shaving problem in areas with high penetration of new energy.

[0003] In new power systems, renewable energy is developing rapidly, and due to its instability, the peak-shaving pressure on thermal power units is increasing. To alleviate the peak-shaving pressure on thermal power units and the high curtailment rate of new energy sources, hybrid pumped storage has been introduced into multi-source power generation systems as an effective regulating power source.

[0004] In existing technologies, thermal power units have too many deep peak shaving operations, resulting in high operating costs and unstable power output. At the same time, the absorption of new energy sources is poor, and the curtailment rate is too high. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a day-ahead hierarchical optimization scheduling method that considers hybrid pumped storage and deep peak shaving of thermal power. This invention studies a day-ahead hierarchical optimization scheduling strategy for multi-source systems that considers hybrid pumped storage and deep peak shaving of thermal power, in order to alleviate the peak shaving pressure of thermal power, improve the absorption capacity of new energy sources, and enhance the economic efficiency of system operation.

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

[0007] A day-ahead hierarchical optimization scheduling method considering hybrid pumped storage and deep peak shaving of thermal power, specifically including the following steps:

[0008] S1. Establish cost and power generation characteristics models of thermal power units in different peak-shaving phases;

[0009] S2. Based on the cost and power generation characteristic models of the thermal power units in different peak-shaving stages, establish an upper-level dispatch model with the objective of minimizing the variance of the remaining load after hybrid pumped storage regulation.

[0010] S3. Based on the upper-level scheduling model, establish a lower-level scheduling model with the goal of minimizing the peak-shaving cost of thermal power units and the cost of curtailment of renewable energy.

[0011] S4. Integrate the upper-level scheduling model and the lower-level scheduling model to form a multi-energy system day-ahead hierarchical optimization scheduling strategy that takes into account hybrid pumped storage and deep peak shaving of thermal power, and use linearization technology to transform the day-ahead hierarchical optimization scheduling strategy into a hybrid integer linear programming model.

[0012] S5. The mixed-integer linear programming algorithm is used to solve the obtained mixed-integer linear programming model to obtain the power output of each power source and the scheduling scheme.

[0013] Step S1, which involves establishing cost and power generation characteristic models for thermal power units during different peak-shaving phases, is detailed below:

[0014] Based on the different costs and power generation characteristics of thermal power units at the time of output, peak shaving of thermal power units is divided into three categories: conventional peak shaving RPR stage, non-oil injection peak shaving DPR stage, and oil injection peak shaving DPRO stage.

[0015] During the regular peak-shaving RPR phase, the cost of thermal power units consists only of coal consumption cost, which is the product of coal consumption and coal price. The expression for coal consumption cost is:

[0016]

[0017] In the formula: Let be the coal consumption cost function; a, b, and c are the coefficients of the quadratic, linear, and constant terms, respectively. Price per unit of coal; Let t be the output of the thermal power unit;

[0018] During the non-oil-injection peak-shaving DPR phase, the service life of the turbine rotor is calculated using the low-cycle fatigue characteristics of the rotor material. The total strain amplitude is negatively correlated with the service life of the turbine rotor, quantified by the Manson-Coffin formula. The larger the total strain amplitude, the fewer the rotor cracking cycles, and the shorter the rotor service life, as shown below:

[0019]

[0020] In the formula: The total strain amplitude of the rotor at time t; This is the fatigue strength coefficient; It is the fatigue ductility coefficient; t represents the number of rotor cracking cycles at time t; d represents the fatigue strength index; e represents the fatigue ductility index; and E represents the elastic modulus.

[0021] It can be obtained from the rotor stress and centrifugal tangential stress, and then substituted into the Manson-Coffin formula to obtain... Then the unit's loss cost can be obtained, as shown below:

[0022]

[0023] In the formula: The unit loss cost at time t; For the cost of purchasing the equipment;

[0024] During the DPRO (Dynamic Power Generation and Propulsion) phase of peak shaving, the unit needs to be supplied with fuel oil to ensure its safe and stable operation. The fuel oil supply costs are as follows:

[0025]

[0026] In the formula: Let t be the cost of fuel injection at time t; For fuel prices; This refers to the amount of oil added;

[0027] The cost characteristics of thermal power units are represented by a piecewise function, as shown below:

[0028]

[0029] in, and These are the minimum and maximum output values ​​of thermal power units during the RPR phase, respectively. and These are the minimum and maximum output values ​​of thermal power units during the DPR phase, respectively. and These are the minimum and maximum output values ​​of thermal power units during the DPR phase, respectively; F t th This is a characteristic of the cost of thermal power units;

[0030] Considering that thermal power units have three operating phases, the power generation model of thermal power units is established as follows:

[0031]

[0032]

[0033]

[0034]

[0035]

[0036] In the formula: , and These represent the power output of the thermal power unit at time t during the RPR, DPR, and DPRO phases, respectively. This is a 0-1 variable representing whether a thermal power unit is operating in the RPR phase. A value of 1 indicates that it is operating in the RPR phase, and The value of is a positive real number, and conversely, is a negative real number. A value of 0 indicates that the unit is not operating in the RPR phase, and The value of is 0; A 0-1 variable representing whether a thermal power unit is operating in the DPR stage; A 0-1 variable to indicate whether the thermal power unit is operating in the DPRO stage; Let t be the output of the thermal power unit, and its value is... , and sum;

[0037] The operating stage of a thermal power unit is determined by 0-1 variables. , and It means that will , and Substituting these values, we can obtain the cost-output relationship, as shown below:

[0038] .

[0039] The specific details of step S2 are as follows:

[0040] The aforementioned upper-level scheduling model uses a hybrid pumped storage approach to adjust the volatility of the remaining load, with the objective function being to minimize the variance of the remaining load.

[0041]

[0042]

[0043] In the formula: T is the number of scheduling periods; The remaining load of the power grid during time period t; The original load of the power grid during time period t; and These represent the predicted output of photovoltaic and wind power during time period t; The objective function is 1;

[0044] The upper-level constraints consist of two parts: constraints on conventional hydropower stations and hydropower units, and constraints on hybrid pumped storage power stations and pumped storage units.

[0045] The constraints on the conventional hydropower station and hydroelectric generating units are as follows:

[0046] The water balance constraint is:

[0047]

[0048] In the formula: Let be the reservoir capacity of the i-th reservoir at the end of time period t; Let t be the interval runoff flowing into the i-th reservoir during time period t; Let be the outflow from the i-th reservoir during time period t; The resolution for scheduling;

[0049]

[0050] In the formula: Let be the power generation flow of the j-th hydropower unit of the i-th hydropower station at time t; Let be the water discharge of the i-th level hydropower station. Since the water discharge only exists during the high-water season, when the hydropower units are generally operating at full capacity, there is no scheduling significance. Therefore, the established model only considers the dry season and the normal water season.

[0051] The reservoir capacity constraint is:

[0052]

[0053] In the formula: and Let be the lower limit and upper limit of the reservoir capacity of the i-th reservoir, respectively. The initial capacity and the final capacity of the reservoir are expressed as follows:

[0054]

[0055]

[0056] In the formula: Initial storage capacity; The storage capacity at the last moment; This is the storage capacity adjustment coefficient at the last moment, which allows the storage capacity to fluctuate within a small interval at the last moment, thereby increasing the flexibility of scheduling. , These represent the initial storage capacity and the storage capacity at the end of the time step, respectively.

[0057] The power generation flow constraint is:

[0058]

[0059] In the formula: and These are the lower and upper limits of the power generation flow of the j-th hydropower unit in the i-th level hydropower station, respectively.

[0060] Water level calculation:

[0061] The upstream water level is expressed as a function of the reservoir capacity, and the downstream water level is expressed as a function of the outflow, as shown below:

[0062]

[0063]

[0064] In the formula: and These are the water level in front of the dam and the water level at the tail end, respectively. This is a function relating the reservoir's water level and its capacity. A function representing the relationship between tailwater level and outflow rate; Let be the outflow from the i-th reservoir at time t;

[0065] The net water head is expressed as the water level in front of the dam and the water level at the tail end:

[0066]

[0067] In the formula: The head of clean water for hydropower stations;

[0068] The water head meets the upper and lower limit constraints:

[0069]

[0070] In the formula: and These are the lower and upper limits of the water head, respectively;

[0071] The output-head-flow characteristics are:

[0072] Hydropower output is a nonlinear function of water head and power generation flow rate, as shown below:

[0073]

[0074] In the formula: The output of the j-th hydropower unit of the i-th hydropower station at time i; Indicates water head. Represents a nonlinear function;

[0075] Hydropower output is directly proportional to the product of water head and power generation flow, as shown below:

[0076]

[0077] In the formula, It is a proportionality coefficient;

[0078] The unit output constraint is:

[0079]

[0080] In the formula: Let be a 0-1 variable representing the operating state of the j-th hydropower unit of the i-th hydropower station at time t; and These are the lower and upper limits of the output of the j-th hydropower unit of the i-th level hydropower station, respectively.

[0081] The constraints on the hybrid pumped-storage power station and pumped-storage unit are as follows:

[0082] The water balance constraint is:

[0083]

[0084]

[0085] In the formula: The flow rate of the pumped storage unit; and These are the power generation flow rate and the pumping flow rate of the nth pumped storage unit in power generation mode and pumping mode, respectively.

[0086] The flow constraint is:

[0087]

[0088]

[0089] In the formula: and These are the lower and upper limits of the power generation flow of the nth pumped-storage unit, respectively. and These are the lower and upper limits of the pumping flow rate of the nth pumped storage unit, respectively.

[0090] The power expression for pumped storage in both power generation and pumping states is shown below:

[0091]

[0092]

[0093] In the formula: and These represent the output of the nth pumped-storage unit at time t when it is in power generation and pumping mode, respectively. and These are the output coefficients of the nth pumped-storage unit in power generation and pumping states, respectively.

[0094] The upper and lower limits of pumped storage output are constrained as follows:

[0095]

[0096]

[0097] In the formula: and These are the 0-1 variables representing the power generation and pumping states of the nth pumped-storage unit at time t, respectively. and These are the lower and upper limits of the power generation capacity of the nth pumped-storage unit, respectively. and These are the lower and upper limits of the pumping power of the nth pumped storage unit, respectively.

[0098] The start-up and shutdown constraints for pumped-storage units are:

[0099]

[0100]

[0101]

[0102]

[0103]

[0104]

[0105] In the formula: and These are the start-up and shutdown operation variables for the nth pumped storage unit's power generation operation; and These are the start-up and shutdown operation variables for the nth pumped storage unit under pumping conditions; (Equation) This indicates that the same pumped-storage unit cannot generate electricity or pump water simultaneously; This indicates that different pumped-storage units can only have one operating condition or be shut down at any given time.

[0106] The specific details of step S3 are as follows:

[0107] The lower-level objective function includes the peak-shaving cost and the curtailment cost of thermal power units, as shown below:

[0108]

[0109]

[0110] In the formula: Let be the coal consumption cost of the kth thermal power unit during time period t; The cost of power curtailment for the system during time period t; and These are the curtailment cost coefficients for photovoltaic power generation and wind power generation, respectively. and These represent the predicted output of photovoltaic power generation and wind power generation at time t, respectively. and These represent the actual output of photovoltaic power generation and wind power generation at time t, respectively.

[0111] Based on the upper-level dispatch model, the output of the cascade hydropower stations including the hybrid pumped storage is obtained. From this output and load forecast curve, the lower-level equivalent load forecast curve is obtained, as shown below:

[0112]

[0113] In the formula: This is the equivalent load prediction curve for the lower layer. , and These represent the number of cascade hydropower stations, the number of hydropower generating units in the i-th cascade hydropower station, and the number of pumped-storage units, respectively.

[0114] The lower-level constraints include power balance constraints, thermal power unit constraints, and new energy output constraints;

[0115] The power balance constraint is:

[0116]

[0117] In the formula: This refers to the number of thermal power units. Let t be the output of the k-th thermal power unit;

[0118] The operating constraints of thermal power units are:

[0119]

[0120]

[0121]

[0122]

[0123]

[0124] The ramping constraint for thermal power units is:

[0125]

[0126] In the formula: Let be the ramp rate of the kth thermal power unit.

[0127] The power output constraints for new energy sources are:

[0128]

[0129] .

[0130] The specific details of step S4 are as follows:

[0131] The day-ahead hierarchical optimization scheduling strategy is as follows:

[0132] The upper-level scheduling model aims to minimize the variance of the remaining load after regulation by hybrid pumped storage, thereby minimizing the volatility of the remaining load. The lower-level scheduling model aims to minimize the peak-shaving cost of thermal power units and the cost of curtailment of renewable energy, optimizing the output of thermal power units and renewable energy. Through the coordinated optimization of the upper-level and lower-level scheduling models, the overall economy and flexibility of the multi-source complementary system are improved.

[0133] The method of using linearization to transform the day-ahead hierarchical optimization scheduling strategy into a mixed-integer linear programming model is as follows:

[0134] Due to constraints The scores, which contain two variables, cannot be solved directly by the model. Therefore, they need to be linearized. The McCormick convex hull relaxation method is used for linearization, transforming them into a set of inequality constraints:

[0135] .

[0136] The specific details of step S5 are as follows:

[0137] The mixed-integer linear programming model was solved using the Gurobi solver via Yalmip, yielding the power output of each power source and the scheduling scheme.

[0138] A day-ahead hierarchical optimized dispatching system that considers hybrid pumped storage and deep peak shaving of thermal power includes:

[0139] Power generation model building module: Establishes cost and power generation characteristic models of thermal power units during different peak-shaving phases;

[0140] Upper-level dispatch model establishment module: Based on the cost and power generation characteristic models of the thermal power units in different peak-shaving stages, an upper-level dispatch model is established with the goal of minimizing the variance of the remaining load after hybrid pumped storage regulation.

[0141] Lower-level scheduling model establishment module: Based on the upper-level scheduling model, a lower-level scheduling model is established with the goal of minimizing the peak-shaving cost of thermal power units and the cost of curtailment of renewable energy.

[0142] Upper and lower scheduling model integration module: integrates the upper scheduling model and the lower scheduling model to form a day-ahead hierarchical optimization scheduling strategy for a multi-energy system that takes into account hybrid pumped storage and deep peak shaving of thermal power, and uses linearization technology to transform the day-ahead hierarchical optimization scheduling strategy into a mixed integer linear programming model.

[0143] Solution module: The mixed-integer linear programming algorithm is used to solve the obtained mixed-integer linear programming model to obtain the power output of each power source and the scheduling scheme.

[0144] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the day-ahead hierarchical optimization scheduling method that takes into account hybrid pumped storage and thermal power deep peak shaving.

[0145] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the day-ahead hierarchical optimization scheduling method that takes into account hybrid pumped storage and thermal power deep peak shaving.

[0146] The advantages of this invention are: by establishing a hierarchical scheduling model, this invention reduces the number of deep peak shaving operations of thermal power units, thereby reducing operating costs and making power output more stable. At the same time, thanks to the hybrid pumped storage, the system's renewable energy consumption effect is significantly improved, and the curtailment rate is reduced. Attached Figure Description

[0147] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0148] Figure 1 This is a flowchart of the method in this embodiment;

[0149] Figure 2 This is a schematic diagram of the power generation system structure in this embodiment;

[0150] Figure 3 This is a schematic diagram of the scheduling strategy in this embodiment;

[0151] Figure 4 This is a schematic diagram of the structure of a computer device. Detailed Implementation

[0152] like Figure 1-4 As shown, embodiments of the present invention include cascade hydropower stations, hybrid pumped storage power stations, wind farms, photovoltaic power stations, and thermal power plants.

[0153] First, establish cost and power generation models for thermal power units during different peak-shaving phases, including:

[0154] Based on the different costs and characteristics of thermal power units at the time of power output, peak shaving of thermal power units can be divided into three categories: regular peak regulation (RPR), deep peak regulation without oil (DPR), and deep peak regulation with oil (DPRO).

[0155] and These are the minimum and maximum values ​​for the RPR phase, respectively; and These are the minimum and maximum values ​​for the DPR stage, respectively; and These are the minimum and maximum values ​​for the DPR stage, respectively.

[0156] During the RPR (Regenerative Petroleum Processing) phase, the cost of a thermal power unit consists solely of coal consumption cost, which is the product of coal consumption and coal price. The expression for coal consumption cost is:

[0157]

[0158] In the formula: Let be the coal consumption cost function; a, b, and c are the coefficients of the quadratic, linear, and constant terms, respectively. Price per unit of coal; Let t be the output of the thermal power unit;

[0159] During the DPR (Delayed Rinse-Off) phase, reducing unit output leads to low-cycle fatigue loss and creep loss in the rotor, thereby shortening the unit's lifespan. Regarding the turbine rotor's service life, the low-cycle fatigue characteristics of the rotor material can be used to calculate low-cycle fatigue life loss. The Manson-Coffin formula can express the relationship between the total strain amplitude and the number of rotor cracking cycles, as shown below:

[0160]

[0161] In the formula: The total strain amplitude of the rotor at time t; This is the fatigue strength coefficient; It is the fatigue ductility coefficient; t represents the number of rotor cracking cycles at time t, and its value is related to the unit output; d is the fatigue strength index; e is the fatigue ductility index; and E is the elastic modulus.

[0162] The stress of the rotor and the centrifugal tangential stress are obtained, and then substituted into the Manson-Coffin formula to obtain... The unit's loss cost is then obtained as follows:

[0163]

[0164] In the formula: The unit's loss cost at time t; For the cost of purchasing the equipment;

[0165] During the DPRO phase, the unit needs to be fueled to ensure its safe and stable operation. The fuel injection costs are as follows:

[0166]

[0167] In the formula: Let t be the cost of fuel injection at time t; For fuel prices; This refers to the amount of oil added.

[0168] The cost characteristics of thermal power units can be represented by a piecewise function, as shown below:

[0169]

[0170] in, and These are the minimum and maximum output values ​​of thermal power units during the RPR phase, respectively. and These are the minimum and maximum output values ​​of thermal power units during the DPR phase, respectively. and These are the minimum and maximum output values ​​of thermal power units during the DPR phase, respectively; F t th This is a characteristic of the cost of thermal power units;

[0171] Considering that thermal power units have three operating stages, the established power generation model for thermal power units is as follows:

[0172]

[0173]

[0174]

[0175]

[0176]

[0177] In the formula: , and These represent the power output of the thermal power unit at time t during the RPR, DPR, and DPRO phases, respectively. This is a 0-1 variable representing whether a thermal power unit is operating in the RPR phase. A value of 1 indicates that it is operating in the RPR phase, and The value of is a positive real number, and conversely, is a negative real number. A value of 0 indicates that the unit is not operating in the RPR phase, and The value of is 0. A 0-1 variable representing whether a thermal power unit is operating in the DPR stage; A 0-1 variable to indicate whether the thermal power unit is operating in the DPRO stage; Let t be the output of the thermal power unit, and its value is... , and sum.

[0178] The operating stage of a thermal power unit can be determined by 0-1 variables. , and Indicate. Will , and Substituting these values, we can obtain the cost-output relationship, as shown below:

[0179]

[0180] Next, an upper-level scheduling model is established with the objective of minimizing the variance of the remaining load after hybrid pumped storage regulation, including:

[0181] (1) The upper-level scheduling model uses a hybrid pumped storage method to adjust the volatility of the remaining load, with the objective function being to minimize the variance of the remaining load.

[0182]

[0183]

[0184] In the formula: T is the number of scheduling periods; The remaining load of the power grid during time period t; The original load of the power grid during time period t; and The predicted output of photovoltaic and wind power respectively for time period t.

[0185] (2) The upper-level constraints consist of two parts: the constraints of conventional hydropower stations and hydropower units, and the constraints of mixed pumped storage power stations and pumped storage units.

[0186] The constraints for conventional hydropower stations and hydroelectric generating units are as follows:

[0187] 1) The water balance constraint is:

[0188]

[0189] In the formula: Let be the reservoir capacity of the i-th reservoir at the end of time period t; Let t be the interval runoff flowing into the i-th reservoir during time period t; Let be the outflow from the i-th reservoir during time period t; The resolution for scheduling is 1 hour; furthermore, 3600 in the formula represents 3600 seconds.

[0190]

[0191] In the formula: Let be the power generation flow of the j-th hydroelectric unit in the i-th level hydropower station; Let be the water discharge of the i-th level hydropower station. Since the water discharge only exists during the high-water season, when the hydropower units are generally operating at full capacity, there is no scheduling significance. Therefore, the established model only considers the dry season and the normal water season.

[0192] 2) The reservoir capacity constraint is:

[0193]

[0194] In the formula: and These represent the lower and upper limits of the reservoir capacity, respectively. The initial and final reservoir capacities can be expressed as:

[0195]

[0196]

[0197] In the formula: Initial storage capacity; The storage capacity at the last moment; This is the storage capacity adjustment coefficient at the last moment, which allows the storage capacity to fluctuate within a small interval at the last moment, thereby increasing the flexibility of scheduling.

[0198] 3) The power generation flow constraint is:

[0199]

[0200] In the formula: and These are the lower and upper limits of the power generation flow of the j-th hydropower unit in the i-th level hydropower station, respectively.

[0201] 4) Water level calculation:

[0202] Generally, the water level in front of the dam can be expressed as a function of the reservoir capacity, and the tailwater level can be expressed as a function of the outflow, as shown below:

[0203]

[0204]

[0205] In the formula: and These are the upstream water level and the downstream water level of the i-th reservoir during time period t, respectively. Let be the function relating the water level and capacity of the i-th reservoir. A function representing the relationship between the tailwater level and the outflow of the i-th stage hydropower station; Let be the outflow from the i-th reservoir during time period t.

[0206] The net head can be expressed as the water level in front of the dam and the tailwater level:

[0207]

[0208] In the formula: This is the head of the clean water supply for the hydropower station.

[0209] The water purification head must meet the upper and lower limit constraints:

[0210]

[0211] In the formula: and These are the lower and upper limits of the head for the i-th level hydropower station, respectively.

[0212] 5) The output-head-flow characteristics are:

[0213] Hydropower output is a nonlinear function of water head and power generation flow rate, as shown below:

[0214]

[0215] In the formula: The output of the j-th hydropower unit of the i-th hydropower station at time i; Indicates water head. Represents a nonlinear function;

[0216] Hydropower output is directly proportional to the product of water head and power generation flow, as shown below:

[0217]

[0218] In the formula, It is a proportionality coefficient;

[0219] 6) The unit output constraint is:

[0220]

[0221] In the formula: Let be a 0-1 variable representing the operating state of the j-th hydropower unit of the i-th hydropower station at time t; and These are the lower and upper limits of the output of the j-th hydropower unit of the i-th level hydropower station, respectively.

[0222] (3) The constraints on the hybrid pumped storage power station and pumped storage unit are as follows:

[0223] 1) The water balance constraint is:

[0224] Compared to conventional hydropower stations, the water balance constraints of hybrid pumped storage power stations increase the power generation and pumping flow rates of the pumped storage units, and the constraints are as follows:

[0225]

[0226]

[0227] In the formula: This represents the total flow rate of the pumped storage unit; and These are the power generation flow rate and the pumping flow rate of the nth pumped storage unit in power generation mode and pumping mode, respectively.

[0228] 2) The flow constraint is:

[0229]

[0230]

[0231] In the formula: and These are the lower and upper limits of the power generation flow of the nth pumped-storage unit, respectively. and These are the lower and upper limits of the pumping flow rate of the nth pumped storage unit, respectively.

[0232] 3) The power expression for pumped storage in both power generation and pumping states is shown below:

[0233]

[0234]

[0235] In the formula: and These represent the output of the nth pumped-storage unit at time t when it is in power generation and pumping mode, respectively. and These are the output coefficients of the nth pumped-storage unit in power generation and pumping states, respectively.

[0236] The upper and lower limits of pumped storage output are constrained as follows:

[0237]

[0238]

[0239] In the formula: and These are the 0-1 variables representing the power generation and pumping states of the nth pumped-storage unit at time t, respectively. and These are the lower and upper limits of the power generation capacity of the nth pumped-storage unit, respectively. and These are the lower and upper limits of the pumping power of the nth pumped storage unit, respectively.

[0240] 4) The start-up and shutdown constraints for pumped-storage units are:

[0241]

[0242]

[0243]

[0244]

[0245]

[0246]

[0247] In the formula: and These are the start-up and shutdown operation variables for the nth pumped-storage unit at time t; and These are the start-up and shutdown operation variables of the nth pumped storage unit at time t; (Equation) This indicates that the same pumped-storage unit cannot generate electricity or pump water simultaneously; This means that different pumped-storage units can only have one operating condition or be shut down at any given time.

[0248] Next, a lower-level scheduling model is established with the objective of minimizing the peak-shaving cost of thermal power units and the cost of curtailment of renewable energy, including:

[0249] (1) The lower-level objective function includes the peak-shaving cost and the curtailment cost of thermal power units, as shown below:

[0250]

[0251]

[0252] In the formula: Let be the coal consumption cost of the kth thermal power unit at time t; Let t be the cost of power curtailment for the system at time t; and These are the curtailment cost coefficients for photovoltaic power generation and wind power generation, respectively. and These represent the predicted output of photovoltaic power generation and wind power generation at time t, respectively. and These represent the actual output of photovoltaic power generation and wind power generation at time t, respectively.

[0253] The upper-level dispatch model can obtain the output of cascade hydropower stations including mixed pumped storage. From this output and load forecast curve, the lower-level equivalent load forecast curve can be obtained, as shown below:

[0254]

[0255] (2) Lower-level constraints include power balance constraints, thermal power unit constraints, and new energy output constraints.

[0256] 1) The power balance constraint is:

[0257]

[0258] In the formula: This refers to the number of thermal power units. Let t be the output of the kth thermal power unit.

[0259] 2) The operating constraints of thermal power units are:

[0260]

[0261]

[0262]

[0263]

[0264]

[0265] The ramping constraint for thermal power units is:

[0266]

[0267] In the formula: Let be the ramp rate of the kth thermal power unit.

[0268] 3) The power output constraint of new energy sources is:

[0269]

[0270]

[0271] Next, a day-ahead hierarchical optimization scheduling strategy for a multi-energy system considering hybrid pumped storage and deep peak shaving by thermal power is established. The model is then transformed into a mixed-integer linear programming model using linearization techniques, including:

[0272] The hierarchical optimization scheduling strategy is as follows:

[0273] The upper-level scheduling model aims to minimize the variance of the remaining load after regulation by hybrid pumped storage, thereby minimizing the volatility of the remaining load. The lower-level scheduling model aims to minimize the peak-shaving costs of thermal power units and the curtailment costs of renewable energy, optimizing the output of thermal power units and renewable energy sources. Through coordinated optimization between the upper and lower levels, the overall economy and flexibility of the multi-source complementary system are improved.

[0274] The linearization process is as follows:

[0275] Due to constraints The scores, which contain two variables, cannot be solved directly by the model and therefore require linearization. The McCormick convex hull relaxation method can be used for linearization, transforming it into a set of inequality constraints:

[0276]

[0277] Finally, a mixed-integer linear programming algorithm is used to solve the problem, obtaining the power output of each power source and the scheduling scheme, including:

[0278] The mixed-integer linear programming model was solved using the Gurobi solver via Yalmip, yielding the power output of each power source and the scheduling scheme.

[0279] Please see Figure 4 The diagram shows a structural schematic of a computer device provided in an embodiment of this application. An embodiment of this application provides a computer device 400, including a processor 410 and a memory 420. The memory 420 stores a computer program executable by the processor 410. When the computer program is executed by the processor 410, it performs the method described above.

[0280] This application embodiment also provides a storage medium 430, on which a computer program is stored, and the computer program is executed by a processor 410 to perform the above method.

[0281] The storage medium 430 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0282] The core of this invention is to construct a day-ahead scheduling system for multi-energy systems that involves "refined modeling of thermal power plants, hierarchical coordination and optimization, and linear solution". Through complementary objectives at the upper and lower levels and multi-source collaboration, the dual objectives of volatility absorption and economic dispatch can be achieved.

[0283] First, this invention addresses the peak-shaving characteristics of thermal power units, dividing them into three stages based on output level and cost structure: conventional peak shaving (RPR), non-deep oil injection peak shaving (DPR), and deep oil injection peak shaving (DPRO). A cost and power generation model covering the entire peak-shaving range is established through piecewise functions coupled with 0-1 variables. In the RPR stage, only coal consumption costs are considered, and a quadratic function is used to fit the coal consumption characteristics. The DPR stage considers rotor low-cycle fatigue and creep losses, quantifying the impact of lifespan losses on costs using the Manson-Coffin formula. The DPRO stage additionally incorporates fuel costs, achieving accurate cost calculation under different peak-shaving depths. Simultaneously, by defining the unit's operating stage using 0-1 variables, the output of each stage is constrained to be non-negative and mutually exclusive, ultimately forming a one-to-one correspondence between "stage - output - cost," providing a precise cost basis for subsequent economic optimization.

[0284] This invention employs a layered architecture that smooths out fluctuations at the upper layer and optimizes the economy at the lower layer, achieving multi-source collaborative scheduling through information exchange:

[0285] Upper-level dispatch model: Taking hybrid pumped storage and cascade hydropower stations as the core of regulation, the goal is to minimize the variance of residual load after the intervention of hybrid pumped storage, and to reduce the output fluctuations of wind power and photovoltaic power by adjusting the power source to reduce the peak-shaving pressure of lower-level thermal power. The constraint system covers the water balance, reservoir capacity, power generation flow, head-output characteristics and vibration zone avoidance constraints of conventional hydropower stations. At the same time, it supplements the pumping / power generation flow, upper and lower limits of output, vibration zone (only in the power generation stage) and pumping-generation mutual exclusion constraints for hybrid pumped storage stations to ensure the safe and stable operation of hydropower and pumped storage. In particular, the design of initial / final reservoir capacity adjustment coefficients improves dispatch flexibility and adapts to the actual operation scenarios during dry and normal water periods.

[0286] Lower-level scheduling model: Using the hydropower / pumped storage output from the upper level as input, an equivalent load curve is constructed. The goal is to minimize the peak-shaving cost of thermal power units and the cost of renewable energy curtailment throughout the entire process. The constraint system includes power balance constraints, upper and lower limits / ramp constraints for thermal power unit output, and renewable energy output constraints. This achieves economic optimization of thermal power peak shaving and renewable energy consumption, while also inheriting the volatility optimization results from the upper level, avoiding the unintended consequences of optimizing a single objective.

[0287] To address the nonlinear terms in the original model (such as the product of 0-1 variables and continuous output, and the vibration zone constraints of hydropower units), a specialized linearization technique was employed to transform it into a Mixed Integer Linear Programming (MILP) model. For the variable product terms, the McCormick convex hull relaxation method was used to transform the nonlinear constraints into a system of linear inequalities. For the vibration zone constraints, a safety zone indicator variable was introduced to ensure that the units operate only within a single safety zone, and the indicator variable is linked to the unit's operating status. Finally, the Yalmip tool was used to call the Gurobi solver, and the MILP algorithm was used for efficient solution, outputting the day-ahead output curves and scheduling schemes for each power source (hydro, thermal, wind, solar, and pumped storage), ensuring the model's engineering operability.

[0288] The invention reduces the volatility of the remaining load at the upper level by using hydropower / pumped storage to "shave peaks and fill valleys," creating a more stable load environment for the lower level of thermal power peak shaving and reducing the frequency of deep peak shaving by thermal power (especially oil injection peak shaving); at the lower level, the optimization of thermal power peak shaving costs and curtailment costs verifies the economics of the upper-level regulation scheme, forming a closed-loop synergy of "fluctuation smoothing - cost optimization."

[0289] This invention breaks through the limitations of traditional thermal power peak shaving which only considers coal consumption costs. For the first time, it incorporates the lifespan loss of deep peak shaving (DPR stage) and oil injection costs (DPRO stage) into the cost model. By using a three-segment piecewise function and 0-1 variables, it accurately distinguishes the cost characteristics of different peak shaving stages, solving the problem of underestimated deep peak shaving costs. Simultaneously, the introduction of the Manson-coffin formula enables the quantitative calculation of lifespan loss, making the cost model both consistent with theoretical mechanisms and adaptable to engineering practice, providing a reliable cost basis for economic dispatch.

[0290] This invention employs a layered design of "upper-level fluctuation mitigation + lower-level cost optimization," avoiding the contradictions of "emphasizing economics over fluctuation" or "emphasizing fluctuation over economics" in single-objective optimization. The upper level focuses on the core pain point of renewable energy volatility, reducing load fluctuations through the regulation capabilities of hydropower / pumped storage, thereby reducing the pressure on thermal power peak shaving from the source. The lower level optimizes economic indicators based on stable loads to ensure the economic efficiency of the dispatching scheme. The two work together to achieve the dual objectives of "minimizing volatility" and "minimizing costs," thereby improving the overall operational quality of the system.

[0291] This invention comprehensively integrates cascade hydropower stations, hybrid pumped storage power stations, wind farms, photovoltaic power stations, and thermal power plants to construct a multi-source complementary system for all scenarios: cascade hydropower provides clean and adjustable output, hybrid pumped storage combines pumped storage and power generation functions, thermal power provides baseload support and deep peak-shaving capabilities, and renewable energy provides clean electricity. Through coordination between the upper and lower levels, the regulation capabilities of hydropower and pumped storage are fully activated, effectively smoothing out fluctuations in renewable energy output and reducing curtailment rates; at the same time, the three-stage peak-shaving model of thermal power adapts to different fluctuation scenarios, achieving a balance between renewable energy consumption and system security, and significantly improving the renewable energy acceptance capacity of the multi-energy system.

[0292] This invention provides constraints covering the entire lifecycle operational requirements of the power source: For hydropower, it considers water balance, reservoir capacity, head-output characteristics, and vibration zone avoidance to prevent equipment damage and water waste; for hybrid pumped-storage systems, it supplements the constraints with pumping-generation mutual exclusion and pumping / generation flow constraints to adapt to their bidirectional regulation characteristics; for thermal power, it adds ramp constraints and stage mutual exclusion constraints to ensure stable unit operation; and for renewable energy, it clearly defines upper and lower output limits to adapt to forecast uncertainties. In particular, by designing the final reservoir capacity adjustment coefficient and considering only the dry / normal water period for water discharge, it enhances the flexibility and practical adaptability of the dispatching scheme, avoiding the rigidity of dispatching caused by a "one-size-fits-all" constraint design.

[0293] This invention addresses the challenge of solving nonlinear models by employing linearization techniques such as McCormick convex hull relaxation and the introduction of indicator variables. While maintaining solution accuracy, it transforms the model into a commonly used engineering MILP model, compatible with the mature Yalmip+Gurobi solution framework, resulting in high solution efficiency and reliable results. Compared to nonlinear models, the MILP model's solution speed better meets the time requirements of day-ahead scheduling, quickly outputting executable scheduling schemes and providing technical support for engineering applications.

[0294] The model of this invention is not limited to a specific region or power structure. The combined architecture of cascade hydropower, hybrid pumped storage, new energy and thermal power can be adapted to regions with different energy resource endowments in my country. The focus on dry / normal water season scenarios and the design of the final reservoir capacity adjustment coefficient enable it to meet both routine dispatching needs and special operating conditions (such as large-scale new energy generation and sudden load changes), and has a wide range of application scenarios and promotion value.

[0295] In the description of this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. "A plurality of" means two or more, unless otherwise explicitly specified.

[0296] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0297] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Furthermore, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0298] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain.

[0299] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0300] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0301] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0302] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A day-ahead hierarchical optimization scheduling method considering hybrid pumped storage and deep peak shaving of thermal power, characterized in that: Specifically, the steps include the following: S1. Establish cost and power generation characteristics models of thermal power units in different peak-shaving phases; S2. Based on the cost and power generation characteristic models of the thermal power units in different peak-shaving stages, establish an upper-level dispatch model with the objective of minimizing the variance of the remaining load after hybrid pumped storage regulation. S3. Based on the upper-level scheduling model, establish a lower-level scheduling model with the goal of minimizing the peak-shaving cost of thermal power units and the cost of curtailment of renewable energy. S4. Integrate the upper-level scheduling model and the lower-level scheduling model to form a multi-energy system day-ahead hierarchical optimization scheduling strategy that takes into account hybrid pumped storage and thermal power deep peak shaving. Then, use linearization technology to transform the day-ahead hierarchical optimization scheduling strategy into a hybrid integer linear programming model. S5. The mixed-integer linear programming algorithm is used to solve the obtained mixed-integer linear programming model to obtain the power output of each power source and the scheduling scheme.

2. The day-ahead hierarchical optimization scheduling method considering hybrid pumped storage and thermal power deep peak shaving as described in claim 1, characterized in that, Step S1, which involves establishing cost and power generation characteristic models for thermal power units during different peak-shaving phases, is detailed below: Based on the different costs and power generation characteristics of thermal power units at the time of output, peak shaving of thermal power units is divided into three categories: conventional peak shaving RPR stage, non-oil injection peak shaving DPR stage, and oil injection peak shaving DPRO stage. During the regular peak-shaving RPR phase, the cost of thermal power units consists only of coal consumption cost, which is the product of coal consumption and coal price. The expression for coal consumption cost is: ， In the formula: Let be the coal consumption cost function; a, b, and c are the coefficients of the quadratic, linear, and constant terms, respectively. Price per unit of coal; Let t be the output of the thermal power unit at time t; During the non-oil-injection peak-shaving DPR phase, the service life of the turbine rotor is calculated using the low-cycle fatigue characteristics of the rotor material. The total strain amplitude is negatively correlated with the service life of the turbine rotor, quantified by the Manson-Coffin formula. The larger the total strain amplitude, the fewer the rotor cracking cycles, and the shorter the rotor service life, as shown below: ， In the formula: The total strain amplitude of the rotor at time t; This is the fatigue strength coefficient; It is the fatigue ductility coefficient; t represents the number of rotor cracking cycles at time t; d represents the fatigue strength index. e is the fatigue ductility index; E is the elastic modulus; The stress of the rotor and the centrifugal tangential stress are obtained, and then substituted into the Manson-Coffin formula to obtain... The unit's loss cost is then obtained as follows: ， In the formula: The unit's loss cost at time t; For the cost of purchasing the equipment; During the DPRO (Dynamic Power Generation and Propulsion) phase of peak shaving, the unit needs to be supplied with fuel oil to ensure its safe and stable operation. The fuel oil supply costs are as follows: ， In the formula: Let t be the cost of fuel injection at time t; For fuel prices; This refers to the amount of oil added; The cost characteristics of thermal power units are represented by a piecewise function, as shown below: ， in, and These are the minimum and maximum output values ​​of thermal power units during the RPR phase, respectively. and These are the minimum and maximum output values ​​of thermal power units during the DPR phase, respectively. and These are the minimum and maximum output values ​​of thermal power units during the DPR phase, respectively; F t th This is a characteristic of the cost of thermal power units; Considering that thermal power units have three operating phases, the power generation model of thermal power units is established as follows: ， ， ， ， ， In the formula: , and These represent the power output of the thermal power unit at time t during the RPR, DPR, and DPRO phases, respectively. This is a 0-1 variable representing whether a thermal power unit is operating in the RPR phase. A value of 1 indicates that it is operating in the RPR phase, and The value of is a positive real number, and conversely, is a negative real number. A value of 0 indicates that the unit is not operating in the RPR phase, and The value of is 0; A 0-1 variable representing whether a thermal power unit is operating in the DPR stage; A 0-1 variable to indicate whether the thermal power unit is operating in the DPRO stage; Let t be the output of the thermal power unit, and its value is... , and sum; The operating stage of a thermal power unit is determined by 0-1 variables. , and It means that will , and Substituting these values, we can obtain the cost-output relationship, as shown below: 。 3. The day-ahead hierarchical optimization scheduling method considering hybrid pumped storage and thermal power deep peak shaving as described in claim 2, characterized in that, The specific details of step S2 are as follows: The aforementioned upper-level scheduling model uses a hybrid pumped storage approach to adjust the volatility of the remaining load, with the objective function being to minimize the variance of the remaining load. ， ， In the formula: T is the number of scheduling periods; The remaining load of the power grid during time period t; The original load of the power grid during time period t; and These represent the predicted output of photovoltaic and wind power during time period t; The objective function is 1; The upper-level constraints consist of two parts: constraints on conventional hydropower stations and hydropower units, and constraints on hybrid pumped storage power stations and pumped storage units.

4. The day-ahead hierarchical optimization scheduling method considering hybrid pumped storage and thermal power deep peak shaving as described in claim 3, characterized in that, The constraints on the conventional hydropower station and hydroelectric generating units are as follows: The water balance constraint is: ， In the formula: Let be the reservoir capacity of the i-th reservoir at the end of time period t; Let t be the interval runoff flowing into the i-th reservoir during time period t; Let be the outflow from the i-th reservoir during time period t; The resolution for scheduling; ， In the formula: Let be the power generation flow of the j-th hydroelectric unit in the i-th level hydropower station; Let be the water discharge of the i-th level hydropower station. Since the water discharge only exists during the high water season, when the hydropower units are generally operating at full capacity, there is no scheduling significance. Therefore, the established model only considers the dry season and the normal water season. The reservoir capacity constraint is: ， In the formula: and Let be the lower limit and upper limit of the reservoir capacity of the i-th reservoir, respectively. The initial capacity and the final capacity of the reservoir are expressed as follows: ， ， In the formula: Let be the initial storage capacity of the i-th reservoir; Let be the reservoir capacity at the last moment of the i-th reservoir; Let be the reservoir capacity adjustment coefficient at the last moment of the i-th reservoir, so that the reservoir capacity at the last moment can fluctuate within a small interval, which can increase the flexibility of scheduling; , These represent the initial storage capacity and the storage capacity at the end of the time step, respectively. The power generation flow constraint is: ， In the formula: and These are the lower and upper limits of the power generation flow of the j-th hydropower unit in the i-th level hydropower station, respectively. Water level calculation: The upstream water level is expressed as a function of the reservoir capacity, and the downstream water level is expressed as a function of the outflow, as shown below: ， ， In the formula: and These are the upstream water level and the downstream water level of the i-th reservoir during time period t, respectively. Let be the function relating the water level and capacity of the i-th reservoir. A function representing the relationship between the tailwater level and the outflow of the i-th stage hydropower station; Let be the outflow from the i-th reservoir during time period t; The net water head is expressed as the water level in front of the dam and the water level at the tail end: ， In the formula: Let t be the net head of the i-th stage hydropower station during time period t; The water head meets the upper and lower limit constraints: ， In the formula: and These are the lower and upper limits of the head for the i-th level hydropower station, respectively. The output-head-flow characteristics are: Hydropower output is a nonlinear function of water head and power generation flow rate, as shown below: ， In the formula: The output of the j-th hydropower unit of the i-th hydropower station at time i; Indicates water head. Represents a nonlinear function; Hydropower output is directly proportional to the product of water head and power generation flow, as shown below: ， In the formula, It is a proportionality coefficient; The unit output constraint is: ， In the formula: Let be a 0-1 variable representing the operating state of the j-th hydropower unit of the i-th hydropower station at time t; and These are the lower and upper limits of the output of the j-th hydropower unit of the i-th level hydropower station, respectively. The constraints on the hybrid pumped-storage power station and pumped-storage unit are as follows: The water balance constraint is: ， ， In the formula: This represents the total flow rate of the pumped storage unit; and These are the power generation flow rate and the pumping flow rate of the nth pumped storage unit in power generation mode and pumping mode, respectively. The flow constraint is: ， ， In the formula: and These are the lower and upper limits of the power generation flow of the nth pumped-storage unit, respectively. and These are the lower and upper limits of the pumping flow rate of the nth pumped storage unit, respectively. The power expression for pumped storage in both power generation and pumping states is shown below: ， ， In the formula: and These represent the output of the nth pumped-storage unit at time t when it is in power generation and pumping mode, respectively. and These are the output coefficients of the nth pumped-storage unit in power generation and pumping states, respectively. The upper and lower limits of pumped storage output are constrained as follows: ， ， In the formula: and These are the 0-1 variables representing the power generation and pumping states of the nth pumped-storage unit at time t, respectively. and These are the lower and upper limits of the power generation capacity of the nth pumped-storage unit, respectively. and These are the lower and upper limits of the pumping power of the nth pumped storage unit, respectively. The start-up and shutdown constraints for pumped-storage units are: ， ， ， ， ， ， In the formula: and These are the start-up and shutdown operation variables for the nth pumped-storage unit at time t; and These are the start-up and shutdown operation variables of the nth pumped storage unit at time t; (Equation) This indicates that the same pumped-storage unit cannot generate electricity or pump water simultaneously; This indicates that different pumped-storage units can only have one operating condition or be shut down at any given time.

5. The day-ahead hierarchical optimization scheduling method considering hybrid pumped storage and thermal power deep peak shaving as described in claim 4, characterized in that, The specific details of step S3 are as follows: The lower-level objective function includes the peak-shaving cost and the curtailment cost of thermal power units, as shown below: ， ， In the formula: For objective function 2, Let be the coal consumption cost of the kth thermal power unit at time t; Let t be the cost of power wastage of the system at time t; and These are the curtailment cost coefficients for photovoltaic power generation and wind power generation, respectively. and These represent the predicted output of photovoltaic power generation and wind power generation at time t, respectively. and These represent the actual output of photovoltaic power generation and wind power generation at time t, respectively. Based on the upper-level dispatch model, the output of the cascade hydropower stations including the hybrid pumped storage is obtained. From this output and load forecast curve, the lower-level equivalent load forecast curve is obtained, as shown below: ， In the formula: This is the equivalent load prediction curve for the lower layer. , and These represent the number of cascade hydropower stations, the number of hydropower units in the i-th cascade hydropower station, and the number of pumped storage units, respectively. The lower-level constraints include power balance constraints, thermal power unit constraints, and new energy output constraints; The power balance constraint is: ， In the formula: This refers to the number of thermal power units. Let t be the output of the k-th thermal power unit; The operating constraints of thermal power units are: ， ， ， ， ， The ramping constraint for thermal power units is: ， In the formula: Let be the ramp rate of the kth thermal power unit; The power output constraints for new energy sources are: ， 。 6. The day-ahead hierarchical optimization scheduling method considering hybrid pumped storage and thermal power deep peak shaving as described in claim 5, characterized in that, The specific details of step S4 are as follows: The day-ahead hierarchical optimization scheduling strategy is as follows: The upper-level scheduling model aims to minimize the variance of the remaining load after regulation by hybrid pumped storage, thereby minimizing the volatility of the remaining load. The lower-level scheduling model aims to minimize the peak-shaving cost of thermal power units and the cost of curtailment of renewable energy, optimizing the output of thermal power units and renewable energy. Through the coordinated optimization of the upper-level and lower-level scheduling models, the overall economy and flexibility of the multi-source complementary system are improved. The method of using linearization to transform the day-ahead hierarchical optimization scheduling strategy into a mixed-integer linear programming model is as follows: Due to constraints The scores, which contain two variables, cannot be solved directly by the model. Therefore, they need to be linearized. The McCormick convex hull relaxation method is used for linearization, transforming them into a set of inequality constraints: 。 7. The day-ahead hierarchical optimization scheduling method considering hybrid pumped storage and thermal power deep peak shaving as described in claim 1, characterized in that, The specific details of step S5 are as follows: The mixed-integer linear programming model was solved using the Gurobi solver via Yalmip, yielding the power output of each power source and the scheduling scheme.

8. A day-ahead hierarchical optimized dispatching system that considers hybrid pumped storage and deep peak shaving of thermal power, characterized in that: Including: Power generation model building module: Establishes cost and power generation characteristic models of thermal power units during different peak-shaving phases; Upper-level dispatch model establishment module: Based on the cost and power generation characteristic models of the thermal power units in different peak-shaving stages, an upper-level dispatch model is established with the goal of minimizing the variance of the remaining load after hybrid pumped storage regulation. Lower-level scheduling model establishment module: Based on the upper-level scheduling model, a lower-level scheduling model is established with the goal of minimizing the peak-shaving cost of thermal power units and the cost of curtailment of renewable energy. Upper and lower scheduling model integration module: integrates the upper scheduling model and the lower scheduling model to form a day-ahead hierarchical optimization scheduling strategy for multi-energy systems that takes into account hybrid pumped storage and deep peak shaving of thermal power, and uses linearization technology to transform the day-ahead hierarchical optimization scheduling strategy into a mixed integer linear programming model. Solution module: The mixed-integer linear programming algorithm is used to solve the obtained mixed-integer linear programming model to obtain the power output of each power source and the scheduling scheme.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the day-ahead hierarchical optimization scheduling method according to any one of claims 1-7, which takes into account hybrid pumped storage and thermal power deep peak shaving.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the day-ahead hierarchical optimization scheduling method according to any one of claims 1-7, which takes into account hybrid pumped storage and thermal power deep peak shaving.

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