A pumped storage power station market-oriented dispatching quantity reporting and price quoting optimization method and device

Abstract

The present application relates to the technical field of power system optimization scheduling, and discloses a pumped storage power station marketization scheduling quantity reporting and pricing optimization method and device, wherein the specific steps of the method are as follows: collecting basic data as the basic input data of the pumped storage power station optimization scheduling model; establishing a pumped fixed efficiency model by using a fixed conversion efficiency ηpump, and constructing a power generation water head resistance model by establishing the corresponding relationship between the reservoir capacity variable V and the net water head change; constructing an asymmetric power characteristic model of the pumped storage power station pumping and power generation working condition; taking the maximum net income in the whole period of the scheduling period as the objective function, and establishing a pumped storage power station optimization scheduling model; linear programming of the power generation water head resistance output constraint; and generating a marketization quantity reporting and pricing curve. The present application solves the technical problems of the prior art, such as the physical constraint that the unit cannot reach the rated power due to the neglect of insufficient water head, the virtual high market quantity reporting, the high compliance risk, and the low optimization precision of the income.

Description

Technical Field

[0001] This invention relates to the field of power system optimization dispatching technology, specifically to a method and apparatus for optimizing market-based dispatching and pricing of pumped storage power stations. Background Technology

[0002] Pumped storage power stations are a core regulating resource in the new power system. They participate in the electricity market through the arbitrage model of "pumping water during off-peak hours and generating electricity during peak hours," playing a key role in promoting the consumption of new energy sources and ensuring the stability of the power grid.

[0003] Currently, the industry generally uses a fixed output model to optimize the market-based dispatch of pumped storage power stations. However, the power generation operation is highly dependent on the net head formed by the difference in water levels between upstream and downstream. The rated power output of the unit is defined under the rated head. When the water level in the upstream reservoir drops and the net head is lower than the rated head, the potential energy of the water is insufficient to drive the turbine to reach the rated output. Even with the guide vanes fully open, the maximum power that the unit can actually generate will be significantly lower than the rated value. As power generation continues, the reservoir capacity decreases and the water level drops, this "power generation head obstruction effect" becomes increasingly severe, and the upper limit of the unit's output continues to decline.

[0004] Existing fixed-efficiency modeling schemes ignore this physical essence and have two major flaws: First, they overestimate the maximum power generation capacity of units under low water level and low reservoir capacity conditions, resulting in inflated power generation caps declared in the market during certain periods. This leads to a disconnect between bids and the actual physical power generation capacity of the units, making it impossible for the units to fully fulfill their contracts after winning bids, and exposing them to high deviation assessments and revenue loss risks. Second, they cannot quantify the degree of output limitation caused by head decay, resulting in unreasonable timing configuration of optimized scheduling strategies. This often leads to resource mismatch, such as "over-scheduling power generation during low head-limited periods and insufficient reservoir capacity during high head-efficient periods," failing to achieve optimal revenue throughout the entire cycle.

[0005] In summary, existing technologies fail to differentiate the physical characteristics of pumping and power generation, lack characterization of the fundamental physical constraint that "units below rated head cannot reach rated power," and fail to match the actual upper limit of unit output in market-based quotation pricing. Furthermore, they suffer from low modeling accuracy and poor market adaptability. Therefore, there is an urgent need for an optimized scheduling and quotation pricing technology that can accurately characterize the hindering effect of power generation head on the upper limit of output and correct market quotation deviations. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a method and apparatus for optimizing the market-based dispatching quotation and pricing of pumped storage power stations. This method solves the technical problems of existing technologies, which ignore the physical constraints of insufficient water head, resulting in the inability of the generating units to reach their rated power, leading to inflated market quotation, high contract fulfillment risks, and low accuracy in revenue optimization. It achieves a precise match between the actual maximum power output of the generating units at various reservoir water levels and the market-based trading quotation and pricing strategy.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: A method for optimizing the market-based dispatching and pricing of pumped storage power stations, comprising the following steps: The system collects the day-ahead forecast time-of-use electricity price sequence, the initial reservoir capacity, maximum reservoir capacity, rated power of pumped storage power stations, and test data used to calibrate the maximum power output ratio of the units under different reservoir capacities, water levels, or net head conditions within the dispatch cycle. The collected data is then verified for completeness, consistency, and standardized to form basic data. A fixed conversion efficiency ηpump is used to establish a pumping fixed efficiency model to characterize the energy conversion relationship during pumping. The collected basic data are analyzed by data fitting and piecewise linear functions to establish the correspondence between the reservoir capacity variable V and the change in net head, and a power generation head obstruction model is constructed. Using the fixed efficiency model of pumping and the head resistance model of power generation as constraints for solving the objective function, an asymmetric power characteristic model of pumped storage power station pumping and power generation is constructed to describe the asymmetry of pumping and power generation in physical mechanism and its impact on market-based dispatch constraints. An optimal scheduling model for pumped storage power stations is established with the objective function of maximizing net revenue throughout the scheduling cycle. The aforementioned basic data serves as the basic input data for the optimal scheduling model. The decision variables in the optimal scheduling model include pumping power and power generation power in each time period. The nonlinear relationship between the maximum power output ratio coefficient and reservoir capacity in the optimal scheduling model of pumped storage power stations is processed by piecewise linearization, and the power output constraint due to head obstruction is transformed into several linear inequality constraints, thereby realizing the linear programming of the power output constraint due to head obstruction. Using the declared power during low reservoir capacity periods, after adjusting for the actual maximum output limit due to head constraints, as a constraint, the optimized model of the pumped storage power station is solved to obtain the power sequence of the optimal pumping and power generation conditions for each period, and a market-based quotation curve is generated.

[0008] Furthermore, the day-ahead predicted time-of-use electricity price sequence p t This refers to the predicted market-clearing electricity price for each time period within the dispatch cycle, used to characterize the market transaction price for each time period, where t=1,2,… T , T This represents the total number of scheduling periods; The initial reservoir capacity V0 of the pumped storage power station refers to the initial water storage of the upper reservoir at the start of the dispatch, and the maximum reservoir capacity Vmax refers to the maximum water storage that the upper reservoir of the pumped storage power station is allowed to reach. The rated power Pmax of the unit refers to the maximum rated power that the pumped storage unit can continuously and stably generate under rated head conditions. The rated power Pmax of the unit is a reference benchmark for power constraints in pumping and generating conditions. The test data used to calibrate the proportion coefficient of the unit's maximum power output under different reservoir capacities, water levels, or net water heads includes test data of the unit's actual maximum power output under different reservoir capacities, water levels, or net water heads, and is used to establish the correspondence between the reservoir capacity V and the proportion coefficient γ(V) of the unit's actual maximum power output.

[0009] Furthermore, the maximum power output ratio coefficient γ(V) of the unit is used to characterize the degree of deviation between the actual maximum power output capacity of the unit and the rated power Pmax under different reservoir water level conditions; The maximum power output ratio coefficient γ(V) decreases monotonically as the reservoir capacity V decreases. When the reservoir capacity approaches the maximum reservoir capacity Vmax, γ(V)≈1; when the reservoir capacity decreases to the low water level range, γ(V) gradually decreases to the preset lower limit value γmin.

[0010] Furthermore, the step of establishing an optimal scheduling model for pumped storage power stations with the objective function of maximizing net revenue throughout the scheduling cycle, using the aforementioned basic data as the basic input data for the optimal scheduling model, specifically involves the following steps: Based on the market time-of-use electricity price for each period within the dispatch cycle p t Based on revenue, the power generation mode generates electricity revenue, while the pumping mode generates electricity consumption costs. By unifying the modeling of power generation revenue and pumping costs, we obtain the objective function for maximizing net revenue throughout the entire scheduling cycle:

[0011] Among them, the generating power of the unit during time period t is P gen,t The pumping power is P pump,t The unit power consumption and maintenance cost under pumping conditions is C. cost The total number of scheduling periods is T; The objective function is used to characterize the operation mechanism of pumped storage power stations in the electricity market to achieve cross-time arbitrage through "low-price pumping and high-price power generation". The objective function serves as the core optimization objective that constrains the optimal scheduling model of pumped storage power stations. The objective function and the constraints of the pumped storage power station optimization scheduling model together constitute the pumped storage power station optimization scheduling model, which is used to maximize the net revenue of the scheduling cycle while satisfying the unit operation constraints.

[0012] Furthermore, the constraints of the pumped storage power station optimization scheduling model include reservoir capacity dynamic balance constraints, power generation head obstruction output constraints, reservoir capacity boundary constraints, power upper and lower limit constraints, and periodic reservoir capacity clearing constraints, as well as the physical feasible domain used to describe the operation of the pumped storage power station. The dynamic balance constraint of the storage capacity is: ; The power output constraint due to the obstruction of the power generation head is: ; The storage capacity boundary constraint is: ; The power upper and lower limit constraints are: ;

[0013] The periodic liquidation constraint is as follows: ;

[0014] Furthermore, the nonlinear relationship between the maximum output ratio coefficient and reservoir capacity in the optimized scheduling model of the pumped storage power station is processed piecewise linearly, transforming the head-resistance output constraint into several linear inequality constraints, thereby realizing the linear programming of the head-resistance output constraint. The specific steps are as follows: The nonlinear relationship between the maximum power output ratio coefficient γ(V) and the reservoir capacity V is piecewise linearized and fitted. Corresponding linear expressions are constructed according to different reservoir capacity intervals to ensure that the linear model can approximate the original nonlinear relationship well. The expression for the maximum power output ratio γ(V) after piecewise linear fitting is:

[0015] Where Vth1 and Vth2 are preset reservoir capacity thresholds; a and b are linear attenuation coefficients for higher reservoir capacity segments; c and d are linear attenuation coefficients for lower reservoir capacity segments; and the fixed pumping conversion efficiency is set to 0.75. Based on the piecewise linearized maximum power output ratio coefficient, the original power generation head constraint due to obstruction is transformed into a linear inequality constraint, which is as follows:

[0016] in, , These are respectively derived from the corresponding piecewise linear expressions and the rated power P of the unit. max A defined slope and intercept; Within each segmented linear interval, corresponding linear inequality constraints are established. Through multiple sets of linear inequality constraints, the nonlinear law of the maximum power output of the original unit as a function of the net head is displayed. By performing piecewise linearization of the ratio of maximum power output to reservoir capacity, the original nonlinear pumped storage power station optimization scheduling model is transformed into a pumped storage power station optimization scheduling model with linear programming. This improves the solution efficiency and computational stability of the optimization model while ensuring the accuracy of the calculation of the head resistance characteristics.

[0017] Furthermore, the number of segments in the nonlinear relationship between the maximum power output ratio coefficient and the reservoir capacity is determined based on the continuous full-capacity operation time of the pumped storage hydropower station / the local electricity market quotation period.

[0018] Furthermore, the reported power during low reservoir capacity periods, after adjusting for the actual maximum output limit due to head constraints, is used as a constraint to solve the transformed pumped storage power station optimization model. This yields the power sequences of the optimal pumping and power generation conditions for each period, and generates a market-based quotation curve. The specific steps are as follows: Under constraints, a linear programming solver is used to solve the objective function to obtain the optimal decision variables for each time period within the scheduling cycle, including the pumping power P. pump,t With power generation P gen,t ; Based on the solution results, the optimal scheduling and operation strategy for each time period is determined, and the corresponding theoretical maximum power generation capacity is obtained; Generates market-based quantity and price quotation curves within the scheduling cycle, and outputs the optimal extraction power sequence and corresponding market application results for use in day-ahead electricity market trading applications.

[0019] A device for optimizing the reporting and pricing of pumped storage power station market-based dispatch, used to implement an optimization method for the reporting and pricing of pumped storage power station market-based dispatch, comprising: Data acquisition module: Used to collect the day-ahead forecast time-of-use electricity price sequence, the initial reservoir capacity, maximum reservoir capacity, rated power of the pumped storage power station, and test data for calibrating the maximum power output ratio coefficient of the unit under the corresponding reservoir capacity, water level, and net head conditions, providing basic input data for the pumped storage power station optimization model; The obstruction characteristic modeling module is used to construct an asymmetric power characteristic model that dynamically decays the ratio coefficient of fixed pumping efficiency to the maximum power output of the generator, and to quantify the constraint of head obstruction effect on the maximum power output of the unit. The optimization model building module is used to build an optimization model for pumped storage power stations with the objective function of maximizing net revenue over the entire cycle, and to configure dynamic balance of reservoir capacity, power generation head under obstruction, power and reservoir capacity boundaries, and periodic clearing constraints. Linearization module: Used to perform piecewise linearization fitting on the nonlinear maximum power output ratio coefficient-reservoir capacity relationship, transforming the nonlinear head-resistance power output constraint into a linear inequality constraint, forming a standard linear programming optimization model for pumped storage power stations. The solution and trading strategy output module is used to call the optimization solver to solve the optimization model of the pumped storage power station, output the optimal scheduling plan for pumping and power generation, and correct the declared power during low reservoir periods based on the actual maximum output limit after head limitation, so as to generate accurate market-based quantity and price quotation strategies.

[0020] The beneficial effects of this invention are as follows: accurate physical modeling, directly addressing industry pain points: for the first time, it directly quantifies the core physical constraint that "the rated power cannot be achieved below the rated head," through the maximum power output ratio coefficient γ(V). It accurately depicts the attenuation of the unit's output ceiling under low water level conditions, completely solving the fundamental defect of traditional fixed-efficiency models that overestimate power generation capacity; it eliminates market reporting deviations and eliminates performance risks: the reported output price strictly matches the unit's true maximum generating capacity under the current reservoir water level, eliminating the problem of failure to fully perform after winning the bid due to insufficient water head or the actual output ceiling being lower than the reported value, significantly improving market credibility and competitiveness; it enhances arbitrage profits throughout the entire cycle: by quantifying the degree of output limitation during low water head periods, optimized scheduling can intelligently avoid ineffective power generation during low water head and low electricity price periods, reserving water resources for efficient arbitrage during high water head and high electricity price periods, maximizing net profits throughout the entire cycle; it has strong engineering applicability: the model is transformed into a linear programming problem through piecewise linearization, with fast solution speed and stable convergence, and can be directly embedded into the day-ahead or intraday electricity market batch bidding system, suitable for large-scale engineering applications. Attached Figure Description

[0021] Appendix Figure 1 This is a flowchart of a market-based dispatching and pricing optimization method for pumped storage power stations according to the present invention. Appendix Figure 2 This is a block diagram of a pumped storage power station market-based dispatching and pricing optimization device according to the present invention. Appendix Figure 3 This is a flowchart illustrating the specific implementation of the market-based dispatching and pricing optimization of pumped storage power stations according to the present invention.

[0022] Attached labels: 1. Data acquisition module; 2. Resistance characteristic modeling module; 3. Optimization model construction module; 4. Linearization processing module; 5. Solution and trading strategy output module. Detailed Implementation

[0023] The present invention will now be further described with reference to the accompanying drawings and specific embodiments: Example 1 like Figure 1and Figure 3 As shown, a method for optimizing the reporting and pricing of pumped storage power stations under market-based dispatch is presented. The technical solution of the present invention will be described in detail and completely below with reference to specific embodiments.

[0024] This embodiment sets the scheduling cycle to 24 time periods per day, and uses per-unit values ​​for both unit power and reservoir capacity, based on the rated value / maximum value. The basic parameters of the pumped storage power station are: rated unit power Pmax=1.0, maximum reservoir capacity Vmax=6.0, fixed pumping conversion efficiency ηpump=0.75, and unit power operation and maintenance cost Ccost=0.085 yuan / kWh under pumping conditions.

[0025] Step 1: Parameter Initialization and Data Acquisition. Obtain the day-ahead time-of-use electricity price forecast sequence p_t and the initial reservoir capacity V_0 of the power plant for the next 24 hours; This embodiment uses measured data points to construct a piecewise linear model. The key calibration points are as follows: When the reservoir capacity V≥3.0, the net water head is sufficient, and the unit can reach its rated power, γ=1.00; When the reservoir capacity V=2.0, the maximum power output drops to 97% of the rated value, and γ=0.97; When the reservoir capacity V=1.0, the maximum power output drops to 94% of the rated value, and γ=0.94; When the reservoir capacity V=0, the maximum power output drops to 90% of the rated value, γ=0.90, which is the lower limit of output under insufficient net head conditions.

[0026] Step 2: Perform piecewise linear processing on the maximum power output ratio coefficient: Based on the calibration points from step 1, the storage capacity interval is divided into three segments, resulting in a complete piecewise linear expression:

[0027] The corresponding power output constraint due to head resistance is:

[0028] The power output constraint due to head obstruction directly limits the maximum power output of the unit at various reservoir water levels. For example, when the reservoir capacity is 1.5, the maximum power output of the unit is only (0.03×1.5+0.91)×1.0=0.955, which cannot reach the rated power of 1.0.

[0029] Step 3: With the goal of maximizing the net profit over the entire cycle, construct an optimization model for the pumped storage power station. To match the linear programming solver, transform the objective function into minimizing the negative profit:

[0030] The constraints include: Dynamic balance of storage capacity:

[0031] Power output due to head obstruction:

[0032] Storage capacity and power boundaries:

[0033] Periodic Clearance: V 25 =V 0。

[0034] Step 4: Piecewise linearization of nonlinear constraints. Since γ(Vt) has been constructed as a piecewise linear function, directly substituting it into the resisted output force constraint yields a complete set of linear inequalities: When V t When ≥3.0, the constraint is Pgen,t≤1.00. When V t When ∈ [1.0, 3.0), the constraint is Pgen,t ≤ 0.03Vt + 0.91 When V t When ∈ [0, 1.0), the constraint is Pgen,t ≤ 0.04Vt + 0.90 The third constraint covers all low water level intervals, when V t When = 0, the constraint degenerates into P. gen,t ≤0.90, consistent with the maximum generating capacity of the unit under empty reservoir conditions, the pumped storage power station optimization model is transformed into a standard linear programming pumped storage power station optimization model.

[0035] Step 5: Use the linear programming solver to solve the above model and output the optimal pumping power for 24 time periods. Ppump,t and power generation Pgen,t Because it incorporates an upper limit constraint on output based on net head differences, the optimization results will automatically avoid excessive power generation during low head periods, allocating water resources to high electricity price and high head periods. Simultaneously, based on the calculated maximum output γ(Vt) for each time period... P max The system corrects the upper limit of power declared in the market, automatically reduces the declared power during low head periods, and generates an accurate power declaration and price curve that strictly matches the physical characteristics of the unit, thus eliminating the risk of performance assessment caused by inflated declarations from the source.

[0036] Example 2 like Figure 2 As shown, a pumped storage power station market-based dispatching quantity and price optimization device is used to implement a market-based dispatching quantity and price optimization method for pumped storage power stations. It includes five functional modules that work in sequence and in coordination: Data Acquisition Module 1: The daily forecast time-of-use electricity price sequence within the scheduling cycle, the initial reservoir capacity, maximum reservoir capacity, rated power of the pumped storage power station, and experimental data used to calibrate the maximum power output ratio coefficient of the unit under the corresponding reservoir capacity, water level, and net head conditions, provide basic input data for the pumped storage power station optimization model; Obstructed characteristic modeling module 2: used to construct an asymmetric power characteristic model of the dynamic decay of the ratio coefficient of fixed pumping efficiency to maximum power output, and to quantify the constraint of the obstructed head effect on the maximum power output of the unit. Optimization Model Building Module 3: This module is used to build an optimization model for pumped storage power stations with the objective function of maximizing net revenue over the entire cycle, and to configure dynamic balance of reservoir capacity, power generation head under obstruction, power and reservoir capacity boundaries, and periodic clearing constraints. Linearization module 4: Used to perform piecewise linearization fitting on the nonlinear maximum power output ratio coefficient-reservoir capacity relationship, transforming the nonlinear head-resistance power output constraint into a linear inequality constraint, forming a standard linear programming optimization model for pumped storage power stations. Solution and Trading Strategy Output Module 5: Used to call the optimization solver to solve the optimization model of the pumped storage power station, output the optimal pumping and power generation scheduling plan, and correct the declared power during low reservoir periods based on the actual maximum output limit after head limitation, generating accurate market-based quantity and price quotation strategies.

[0037] For those skilled in the art, various other corresponding changes and modifications can be made based on the technical solutions and concepts described above, and all such changes and modifications should fall within the protection scope of the claims of this invention.

Claims

1. A method for optimizing the market-based dispatching and pricing of pumped storage power stations, characterized in that, The specific steps are as follows: The system collects the day-ahead forecast time-of-use electricity price sequence, the initial reservoir capacity, maximum reservoir capacity, rated power of pumped storage power stations, and test data used to calibrate the maximum power output ratio of the units under different reservoir capacities, water levels, or net head conditions within the dispatch cycle. The collected data is then verified for completeness, consistency, and standardized to form basic data. A fixed conversion efficiency ηpump is used to establish a pumping fixed efficiency model to characterize the energy conversion relationship during pumping. The collected basic data are analyzed by data fitting and piecewise linear functions to establish the correspondence between the reservoir capacity variable V and the change in net head, and a power generation head obstruction model is constructed. Using the fixed efficiency model of pumping and the head resistance model of power generation as constraints for solving the objective function, an asymmetric power characteristic model of pumped storage power station pumping and power generation is constructed to describe the asymmetry of pumping and power generation in physical mechanism and its impact on market-based dispatch constraints. An optimal scheduling model for pumped storage power stations is established with the objective function of maximizing net revenue throughout the scheduling cycle. The aforementioned basic data serves as the basic input data for the optimal scheduling model. The decision variables in the optimal scheduling model include pumping power and power generation power in each time period. The nonlinear relationship between the maximum power output ratio coefficient and reservoir capacity in the optimal scheduling model of pumped storage power stations is processed by piecewise linearization, and the power output constraint due to head obstruction is transformed into several linear inequality constraints, thereby realizing the linear programming of the power output constraint due to head obstruction. Using the declared power during low reservoir capacity periods, after adjusting for the actual maximum output limit due to head constraints, as a constraint, the optimized model of the pumped storage power station is solved to obtain the power sequence of the optimal pumping and power generation conditions for each period, and a market-based quotation curve is generated.

2. The method for optimizing the market-based dispatching and pricing of pumped storage power stations as described in claim 1, characterized in that, The day-ahead forecast time-of-use electricity price sequence p t It refers to the predicted market clearing price for each time period within the scheduling cycle, used to characterize the market transaction price for each time period, where t=1,2,…,T, and T is the total number of scheduling time periods; The initial reservoir capacity V0 of the pumped storage power station refers to the initial water storage of the upper reservoir at the start of the dispatch, and the maximum reservoir capacity Vmax refers to the maximum water storage that the upper reservoir of the pumped storage power station is allowed to reach. The rated power Pmax of the unit refers to the maximum rated power that the pumped storage unit can continuously and stably generate under rated head conditions. The rated power Pmax of the unit is a reference benchmark for power constraints in pumping and generating conditions. The test data used to calibrate the proportion coefficient of the unit's maximum power output under different reservoir capacities, water levels, or net water heads includes test data of the unit's actual maximum power output under different reservoir capacities, water levels, or net water heads, and is used to establish the correspondence between the reservoir capacity V and the proportion coefficient γ(V) of the unit's actual maximum power output.

3. The method for optimizing the market-based dispatching and pricing of pumped storage power stations as described in claim 2, characterized in that... The maximum power output ratio coefficient γ(V) of the unit is used to characterize the degree of deviation between the actual maximum power output capacity of the unit and the rated power Pmax under different reservoir water level conditions. The maximum power output ratio coefficient γ(V) decreases monotonically as the reservoir capacity V decreases. When the reservoir capacity approaches the maximum reservoir capacity Vmax, γ(V)≈1; when the reservoir capacity decreases to the low water level range, γ(V) gradually decreases to the preset lower limit value γmin.

4. The method for optimizing the market-based dispatching and pricing of pumped storage power stations as described in claim 1, characterized in that, The objective function for establishing an optimal scheduling model for pumped storage power stations is to maximize the net revenue throughout the scheduling cycle. The basic data is used as the basic input data for this model. The specific steps are as follows: Based on the market time-of-use electricity price for each period within the dispatch cycle p t Based on revenue, the power generation mode generates electricity revenue, while the pumping mode generates electricity consumption costs. By unifying the modeling of power generation revenue and pumping costs, we obtain the objective function for maximizing net revenue throughout the entire scheduling cycle: Among them, the generating power of the unit during time period t is P gen,t The pumping power is P pump,t The unit power consumption and maintenance cost under pumping conditions is C. cost The total number of scheduling periods is T; The objective function is used to characterize the operation mechanism of pumped storage power stations in the electricity market to achieve cross-time arbitrage through "low-price pumping and high-price power generation". The objective function serves as the core optimization objective that constrains the optimal scheduling model of pumped storage power stations. The objective function and the constraints of the pumped storage power station optimization scheduling model together constitute the pumped storage power station optimization scheduling model, which is used to maximize the net revenue of the scheduling cycle while satisfying the unit operation constraints.

5. The method for optimizing the market-based dispatching and pricing of pumped storage power stations as described in claim 4, characterized in that... The constraints of the pumped storage power station optimization scheduling model include reservoir capacity dynamic balance constraints, power generation head obstruction output constraints, reservoir capacity boundary constraints, power upper and lower limit constraints, and periodic reservoir capacity clearing constraints, as well as the physical feasible domain used to describe the operation of the pumped storage power station. The dynamic balance constraint of the storage capacity is: ; The power output constraint due to the obstruction of the power generation head is: ; The storage capacity boundary constraint is: ; The power upper and lower limit constraints are: ; The periodic liquidation constraint is as follows: .

6. The method for optimizing the market-based dispatching and pricing of pumped storage power stations as described in claim 1, characterized in that, The nonlinear relationship between the maximum output ratio coefficient and reservoir capacity in the optimal scheduling model of pumped storage power stations is processed by piecewise linearization, transforming the head-resistance output constraint into several linear inequality constraints, thereby realizing the linear programming of the head-resistance output constraint. The specific steps are as follows: The nonlinear relationship between the maximum power output ratio coefficient γ(V) and the reservoir capacity V is piecewise linearized and fitted. Corresponding linear expressions are constructed according to different reservoir capacity intervals to ensure that the linear model can approximate the original nonlinear relationship well. The expression for the maximum power output proportionality coefficient γ(V) after piecewise linear fitting is: Where Vth1 and Vth2 are preset reservoir capacity thresholds; a and b are linear attenuation coefficients for higher reservoir capacity segments; c and d are linear attenuation coefficients for lower reservoir capacity segments; and the fixed pumping conversion efficiency is set to 0.

75. Based on the piecewise linearized maximum power output ratio coefficient, the original power generation head constraint due to obstruction is transformed into a linear inequality constraint, which is as follows: in, , These are respectively derived from the corresponding piecewise linear expressions and the rated power P of the unit. max A defined slope and intercept; Within each segmented linear interval, corresponding linear inequality constraints are established. Through multiple sets of linear inequality constraints, the nonlinear law of the maximum power output of the original unit as a function of the net head is displayed. By performing piecewise linearization of the ratio of maximum power output to reservoir capacity, the original nonlinear pumped storage power station optimization scheduling model is transformed into a pumped storage power station optimization scheduling model with linear programming. This improves the solution efficiency and computational stability of the optimization model while ensuring the accuracy of the calculation of the head resistance characteristics.

7. The method for optimizing the market-based dispatching and pricing of pumped storage power stations as described in claim 6, characterized in that, The number of segments in the nonlinear relationship between the maximum power output ratio coefficient and the reservoir capacity is determined based on the continuous full-capacity operation time of the pumped storage hydropower station / the local electricity market quotation period.

8. The method for optimizing the market-based dispatching and pricing of pumped storage power stations as described in claim 1, characterized in that, The process involves using the declared power during low reservoir capacity periods, after adjusting for the actual maximum output limit due to head constraints, as a constraint to solve the transformed pumped storage power station optimization model. This yields the power sequences for the optimal pumping and power generation conditions in each period, and generates a market-based quotation curve. The specific steps are as follows: Under constraints, a linear programming solver is used to solve the objective function to obtain the optimal decision variables for each time period within the scheduling cycle, including the pumping power P. pump,t With power generation P gen,t ; Based on the solution results, the optimal scheduling and operation strategy for each time period is determined, and the corresponding theoretical maximum power generation capacity is obtained; Generates market-based quantity and price quotation curves within the scheduling cycle, and outputs the optimal extraction power sequence and corresponding market application results for use in day-ahead electricity market trading applications.

9. A pumped storage power station market-based dispatching quantity and price optimization device, used to implement any of the pumped storage power station market-based dispatching quantity and price optimization methods described in claims 1 to 8, comprising: Data acquisition module: Used to collect the day-ahead forecast time-of-use electricity price sequence, the initial reservoir capacity, maximum reservoir capacity, rated power of the pumped storage power station, and test data for calibrating the maximum power output ratio coefficient of the unit under the corresponding reservoir capacity, water level, and net head conditions, providing basic input data for the pumped storage power station optimization model; The obstruction characteristic modeling module is used to construct an asymmetric power characteristic model that dynamically decays the ratio coefficient of fixed pumping efficiency to the maximum power output of the generator, and to quantify the constraint of head obstruction effect on the maximum power output of the unit. The optimization model building module is used to build an optimization model for pumped storage power stations with the objective function of maximizing net revenue over the entire cycle, and to configure dynamic balance of reservoir capacity, power generation head under obstruction, power and reservoir capacity boundaries, and periodic clearing constraints. Linearization module: Used to perform piecewise linearization fitting on the nonlinear maximum power output ratio coefficient-reservoir capacity relationship, transforming the nonlinear head-resistance power output constraint into a linear inequality constraint, forming a standard linear programming optimization model for pumped storage power stations. The solution and trading strategy output module is used to call the optimization solver to solve the optimization model of the pumped storage power station, output the optimal pumping and power generation scheduling plan, and correct the declared power during low reservoir periods based on the actual maximum output limit after head limitation, so as to generate accurate market-based quantity and price quotation strategies.

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