Short-term wind-solar-storage combined dispatching method considering water consumption and head loss optimization

Abstract

The invention relates to the field of multi-energy complementary operation, in particular to a short-term wind-solar-storage combined dispatching method considering water consumption and head loss optimization. The method comprises the following steps of: firstly, comprehensively describing the uncertainty of a prediction error of a distributed wind-light power plant by combining a virtual power plant strategy and nonparametric kernel density estimation, and generating a wind-light input process containing random parameters; and secondly, with the purpose of minimizing the water consumption, accurate modeling is carried out on the water consumption characteristics of the pumped storage power station under opposite operation conditions and frequent start-stop operation, so that the operation economy of the power station is improved. And finally, on the basis of a water head function optimization strategy, the huge water head loss change in the shared tunnel of the pumped storage power station is represented, load distribution between the tunnels is controlled through a balance strategy, and the water head loss in the shared tunnel is effectively optimized.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of multi-energy complementary operation, and particularly relates to a short-term wind-solar-storage combined dispatching method considering water consumption and head loss optimization. TECHNICAL BACKGROUND

[0002] In the global range, with the continuous improvement of new energy penetration rate, pumped storage power stations (hereinafter referred to as "pumped storage power stations") with millisecond response speed and bidirectional regulation capacity have become the key support for stable operation of new power systems. In order to fully utilize the terrain drop and reduce the construction cost, the existing large pumped storage power stations generally adopt the layout of "long distance diversion + one tunnel multi-machine", which becomes a valuable regulation resource in the power grid by virtue of high water head, large installed capacity and rapid load tracking capability. However, when the multiple units in the tunnel switch among the three working conditions of pumping, stopping and power generation, the mutual influence and superposition effect are significant, which brings severe challenges to short-term dispatching. Firstly, the interaction of the three typical working conditions of pumping, stopping and power generation in the shared tunnel not only leads to the difference in water consumption characteristics between pumping and power generation operation modes, but also causes the start-stop loss brought by frequent working condition conversion, such as pumping-stop, stop-pumping, power generation-stop, stop-power generation, etc., making the accurate quantification of water consumption in the tunnel extremely complex. Secondly, the mutual coupling and superposition of the running flow of multiple units in the shared tunnel lead to the severe fluctuation of the water head loss in the tunnel and the difficulty in accurate evaluation, which seriously restricts the accuracy and practicality of the dispatching results of the pumped storage power station. Finally, the inherent volatility and uncertainty of wind power and photovoltaic output in clean energy base further aggravate the complexity of the dispatching decision of the pumped storage power station, and make the accurate quantification of water consumption and head loss in the shared tunnel more prominent, which poses a severe challenge to the stable operation of the clean energy base.

[0003] In summary, the above problems seriously restrict the short-term optimal dispatching of the one-tunnel multi-machine pumped storage power station, and bring significant difficulties to the joint modeling of wind-solar-storage multi-energy. Current research literature mainly focuses on the one-tunnel multi-machine layout of conventional hydropower stations or the regulation performance analysis of pumped storage power stations, and lacks joint dispatching research on one-tunnel multi-machine pumped storage power stations and wind-solar energy. SUMMARY

[0004] The technical problem solved by the present application is to provide a short-term wind-solar-storage combined dispatching method considering water consumption and head loss optimization. Based on fully considering multiple physical and operating restrictions such as grid load demand, wind-solar output characteristics, pumped storage power station operating constraints and unit operating conditions, the method realizes system optimization of load distribution between tunnels, unit start-stop operation and output process, and finally minimizes the system water consumption. The present application takes the short-term wind-solar-storage combined dispatching in a clean energy base as the background, selects a large-scale pumped storage power station P with two tunnels and eight units in a certain province in southeast China and the surrounding distributed wind power and photovoltaic power stations as the research object. The method can minimize the water consumption of the pumped storage power station operation and start-stop while effectively reducing the huge head loss in the shared tunnel, and has strong practicality and wide popularization value.

[0005] The technical scheme of the present application is:

[0006] A short-term wind-solar-storage combined dispatching method considering water consumption and head loss optimization, first, the uncertainty of the prediction error of the distributed wind-solar power plant is comprehensively described by combining the virtual power plant strategy and the non-parametric kernel density estimation, and the wind-solar input process containing random parameters is generated. Secondly, the water consumption characteristics of the pumped storage power station under opposite operating conditions and frequent start-stop operations are accurately modeled to minimize water consumption, so as to improve the operating economy of the power station. Finally, based on the head function optimization strategy, the huge head loss change in the shared tunnel of the pumped storage power station is characterized, and the load distribution between the tunnels is controlled through the balancing strategy, effectively optimizing the head loss in the shared tunnel.

[0007] Specifically includes the following steps:

[0008] Step (1) In order to reduce the prediction error, the wind energy and solar power plants in the shared power transmission channel are aggregated into virtual units W and S respectively.

[0009] Step (2) Based on the results of step (1), the non-parametric kernel density estimation method is used to describe the probability distribution characteristics of wind-solar prediction error, and the corresponding probability density function (PDF) and cumulative distribution function (CDF) are obtained.

[0010] Step (3) Based on the results of step (2), by introducing random constraints, the positive and negative errors of wind-solar prediction are adjusted and compensated by the pumped storage power station to ensure that the load demand is met.

[0011] (3.1) Positive compensation constraint: use the pumped storage power station to compensate for the insufficient output of wind-solar prediction.

[0012]

[0013] In the formula: P is the probability that the actual output at time t is greater than the grid load; N is the number of pumped storage power stations; Pn(t) is the maximum output of the nth pumped storage power station at time t, MW; Pw(t) and Ps(t) are the actual output of the virtual wind power plant W and photovoltaic power plant S at time t, MW, respectively; Pd(t) is the demand load of the power grid at time t, MW; Cn(t) is the confidence of the cumulative distribution function in positive compensation, %.

[0014] (3.2) Negative compensation constraint: using pumped storage power stations to absorb the excess output of wind and light prediction.

[0015]

[0016] In the formula: Pn(t) is the minimum output of the nth pumped storage power station at time t, MW; Pn(t) is the minimum output of the nth pumped storage power station at time t, MW; Cn(t) is the confidence of the cumulative distribution function in negative compensation, %.

[0017] Step (4) Based on the results of step (3), the random compensation constraint of wind and light is converted into a deterministic compensation constraint as a fixed constraint.

[0018] (4.1) Positive compensation constraint processing.

[0019]

[0020] In the formula: Pn(t) is the output of the nth pumped storage power station at time t, MW; Pw(t) and Ps(t) are the predicted output of the virtual wind power plant W and photovoltaic power plant S at time t, MW, respectively; Pw(t) and Ps(t) are the predicted output of the virtual wind power plant W and photovoltaic power plant S at time t, MW, respectively;

[0021] (4.2) Negative compensation constraint processing.

[0022]

[0023] Step (5) Construct the minimum water consumption target of pumped storage power station.

[0024] In order to improve the economic efficiency of pumped storage power station, under the premise of meeting the same load demand, it is desired to reduce the water consumption of the power station as much as possible, and to reduce the additional water loss caused by frequent start-stop. Therefore, combined with the actual operation characteristics of pumped storage power station, a minimum water consumption target suitable for opposite operation conditions and frequent start-stop operation is proposed to improve the accuracy of the scheduling process. The target function expression is shown in formula (5):

[0025]

[0026] In the formula: the operation water consumption of the nth pumped storage power station in the dispatch day, m³; the start-stop water consumption of the nth pumped storage power station in the dispatch day, m³.

[0027] (5.1) Construction of pumped storage power station operation water consumption

[0028] Unlike traditional hydropower stations, the operation conditions of pumped storage power stations are divided into power generation and pumping modes, with opposite water consumption characteristics. In the power generation process, its operation is similar to conventional hydropower, i.e., to reduce water consumption as much as possible during the dispatch process. In the pumping process, water needs to be pumped from the lower reservoir to the upper reservoir by a water pump driven by electricity in order to generate electricity again. Therefore, in this case, more water consumption means storing more available water for the subsequent power generation period. Finally, the operation water consumption of the pumped storage power station is defined as the water volume after the power generation and pumping water consumption are offset. The specific construction process is as follows:

[0029]

[0030] In the formula: are the power generation and pumping water consumption of the nth pumped storage power station in the dispatch day, m³, respectively; are the power generation and pumping flow of the nth pumped storage power station at time period t for unit i, m³ / s, respectively; is the calculation time period, h.

[0031] (5.2) Construction of pumped storage power station start-stop water consumption.

[0032] Generally, in the dispatch process, the start-stop loss of hydraulic units can be quantified as the corresponding volume of water to facilitate mathematical optimization. However, due to frequent start-stop and diverse operation modes, pumped storage units have multiple start-stop operations, including pumping to shutdown, shutdown to pumping, power generation to shutdown, and shutdown to power generation, and each operation exhibits different water consumption characteristics, making the quantification process very complex. Therefore, according to the start-stop characteristics of the unit, the various start-stop operations are divided into two types: power generation-shutdown and pumping-shutdown, and integer, binary, and small variables are introduced to accurately quantify them. The specific construction process is as follows:

[0033]

[0034] In the formula: are the power generation-start-stop and pumping-start-stop water consumption of the nth pumped storage power station in the dispatch day, m³, respectively.

[0035] wherein:

[0036]

[0037] In the formula: Let be the water consumption (m³) of the i-th generating unit of the n-th pumped storage power station during a single power generation and start-up / shutdown cycle. Let be the water consumption (m³) of the i-th unit of the n-th pumped storage power station during a single pumping-start-stop cycle; These represent the start-up and shutdown status of the i-th unit of the n-th pumped storage power station during time period t, where 1 represents power generation and 0 represents shutdown. These represent the start-up and shutdown status of the i-th unit of the n-th pumped storage power station during time period t, where 1 represents pumping and 0 represents shutdown.

[0038] Step (6) Construct the head function of the shared water diversion tunnel.

[0039] Because pumped-storage power stations operate differently in power generation and pumping modes, their head losses also differ significantly. In power generation mode, the head loss in the tunnels of the pumped-storage power station reduces the calculated head, thereby increasing the water consumption for power generation. However, in pumping mode, the head loss in the tunnels increases the power consumption of the pumps, effectively reducing the amount of water pumped from the lower reservoir to the upper reservoir for the same amount of power. Based on the pump characteristic curves, the head loss in pumping mode actually increases the calculated head in the tunnels. Therefore, based on the above analysis, the calculated head functions for power generation and pumping modes of the pumped-storage power station are constructed separately:

[0040]

[0041] In the formula: Let m be the power generation and pumping head of the nth pumped storage power station in the kth tunnel during time period t; Let be the upper and lower reservoir water levels of the nth pumped storage power station during time period t, respectively, in meters. Let m be the head loss of the nth pumped storage power station during time period t, generated by the kth tunnel and pumped water.

[0042] in:

[0043]

[0044] In the formula: These are the head loss coefficients for power generation and pumping operations, respectively; Let M and S be the power generation and pumping flow rates of the nth pumped storage power station in the kth tunnel during time period t, respectively; Let Y and I be the power generation and pumping flow rates of the y-th unit in the k-th tunnel of the n-th pumped storage power station during time period t, respectively, in m³ / s; Y is the number of shared tunnels; and I is the number of pumped storage units.

[0045] Step (7) Construction of head loss optimization strategy for shared water diversion tunnel.

[0046] In a multi-unit layout within a single tunnel, the flow rate variation within the tunnel is many times greater than that of a single unit in a single tunnel. Furthermore, as shown in formulas (14)-(15), the head loss in the tunnel exhibits a quadratic relationship with the flow rate variation. Therefore, the head loss in the shared water diversion tunnel is enormous and fluctuates dramatically. To avoid the concentrated outbreak of head loss caused by the traditional sequential allocation strategy and the additional flow consumption generated by the average allocation strategy, a reasonable load allocation strategy is proposed to optimize the head loss in the tunnel. The core idea of ​​this strategy is to balance the number of operating units among the tunnels as much as possible while meeting the overall scheduling requirements, thus avoiding the concentrated occurrence of head loss. The construction process of this strategy is as follows:

[0047] (7.1) First, the number of operating units in each tunnel is characterized.

[0048]

[0049] In the formula: This represents the operating condition of the i-th unit of the n-th pumped storage power station during time period t, where 1 represents power generation, 0 represents shutdown, and -1 represents pumping. The absolute value of the operating condition of the i-th unit of the n-th pumped storage power station during time period t; This represents the number of generating units operating in the k-th tunnel during time period t for the n-th pumped storage power station.

[0050] (7.2) Then, the number of operating units between tunnels is balanced and distributed so that the difference in the number of operating units between adjacent tunnels of the same power station at the same time does not exceed 1, so as to ensure that the flow variation in each tunnel is as small as possible, thereby effectively optimizing the head loss in the tunnel.

[0051]

[0052] Step (8) is based on the results of wind and solar uncertainty processing in steps (1) to (4), combined with the objective function for minimizing water consumption of pumped storage power stations constructed in step (5), the head function of the shared water diversion tunnel in step (6), the head loss optimization strategy of the shared water diversion tunnel in step (7), and the operational constraints such as water balance, unit status and power range in the joint scheduling of wind, solar and storage power, to jointly construct a mathematical model for short-term joint optimization of wind, solar and storage power. This model uses the daily output of pumped storage power stations as the decision variable and performs global optimization through a solver. It can obtain the optimal pumped storage output and unit combination scheme under different load demands and wind and solar scenarios, thereby effectively dealing with the uncertainty of wind and solar output and improving the economy of pumped storage power stations and the peak-shaving efficiency of the system.

[0053] The operational constraints of the aforementioned wind-solar-storage joint scheduling model are as follows:

[0054] (8.1) Supply and demand balance constraints.

[0055]

[0056] In the formula: Let be the power output (MW) of the nth pumped storage power station during time period t; Let m be the predicted power output of the m-th wind farm during time period t, in MW. Let L be the predicted power output of the l-th photovoltaic power plant in time period t, in MW; N be the number of pumped storage power stations; M be the number of wind power plants; and L be the number of photovoltaic power plants.

[0057] (8.2) Constraints of pumped storage power stations.

[0058] a) Water balance constraint

[0059]

[0060] In the formula: Let be the upper and lower reservoir capacities of the nth pumped storage power station during time period t, respectively, in m³. Let be the operating flow rate of the nth pumped storage power station during time period t, in m³ / s.

[0061] b) Water level constraints between upper and lower reservoirs

[0062]

[0063] In the formula: Let be the upper and lower reservoir water levels of the nth pumped storage power station at time t, respectively, in meters. Let m be the upper and lower limits of the upper reservoir water level of the nth pumped storage power station; Let m be the upper and lower limits of the lower reservoir water level of the nth pumped storage power station; Let m be the initial water level of the upper and lower reservoirs of the nth pumped storage power station.

[0064] c) Unit state constraints

[0065]

[0066] In the formula: The operating conditions of the nth pumped storage power station at time t, excluding the i-th unit; This represents the operating conditions of unit i in pumped storage power stations other than the nth pumped storage power station at time t.

[0067] d) Unit output constraints

[0068]

[0069] In the formula: Let be the output of unit i of the nth pumped storage power station at time t, in MW; Let be the power generation and pumping output of unit i at time t, respectively, in MW; These are the rated outputs (MW) of the i-th unit of the n-th pumped storage power station for power generation and pumping, respectively.

[0070] e) Unit flow constraints

[0071]

[0072] In the formula: Let be the total flow rate of the nth pumped storage power station at time t, in m³ / s; Let be the flow rate of unit i in the nth pumped storage power station at time t, in m³ / s; Let be the power generation and pumping flow rate of unit i at time t, respectively, in m³ / s; These are the rated flow rates for power generation and water pumping of the i-th unit in the n-th pumped storage power station, respectively, in m³ / s.

[0073] f) Water level and reservoir capacity curve

[0074]

[0075] In the formula: These are the water level and storage capacity curves for the upper and lower reservoirs of the nth pumped storage power station, respectively.

[0076] g) Relationship curve between unit power generation and pumping

[0077]

[0078] In the formula: This is the relationship curve of the power output P, ​​flow rate Q, and head H of the i-th unit of the n-th pumped storage power station.

[0079] (8.3) Wind power output constraints.

[0080]

[0081] In the formula: The power output of the m-th wind farm is expressed in MW. Let m be the installed capacity of the m-th wind farm, in MW.

[0082] (8.4) Photovoltaic output constraints.

[0083]

[0084] In the formula: The output of the lth photovoltaic power plant is MW; Let be the installed capacity of the l-th photovoltaic power plant, in MW.

[0085] This invention offers the following advantages over existing technologies: Addressing the complex water consumption characteristics and significant head loss issues within the water diversion tunnels of large-scale pumped-storage power stations with multiple turbines per tunnel during short-term scheduling, this invention proposes a short-term wind-solar-storage joint scheduling method that considers optimization of water consumption and head loss. This method constructs a joint scheduling model for wind-solar-storage power stations with the objective of minimizing water consumption, optimizing the start-up and shutdown of turbines and load allocation within the shared tunnels of the pumped-storage power stations. By introducing a virtual power plant strategy, this method virtually combines wind and solar power plants connected to the same grid and uses a non-parametric kernel density estimation method to model the randomness of wind and solar prediction errors, generating compensatory scheduling constraints containing random parameters. Simultaneously, considering the actual operation of the pumped-storage power station, the complex water consumption characteristics under opposite operating conditions and frequent start-up and shutdown are quantified into fixed physical formulas, serving as the objective function of the joint scheduling model. Finally, by balancing the distribution of turbine numbers among the shared tunnels, the head loss within the tunnels is effectively reduced, as well as unnecessary start-up and shutdown frequency, thereby significantly improving the resource utilization efficiency of the pumped-storage power station with multiple turbines per tunnel and ensuring the safe and stable operation of the power station. In summary, this invention can provide relatively accurate and efficient guidance for the operation and management of large-scale pumped storage power stations with multiple turbines in one tunnel, effectively solving key problems in the short-term joint dispatch of wind, solar and pumped storage, and has good practicality and engineering application prospects. Attached Figure Description

[0086] Figure 1 This is a schematic diagram of a pumped storage power station with multiple pumps in one tunnel.

[0087] Figure 2 This is the overall solution framework diagram.

[0088] Figure 3 This is a probability distribution diagram of typical winter daytime wind and light forecasting errors.

[0089] Figure 4 This is a probability distribution diagram of typical summer daytime weather forecast errors.

[0090] Figure 5 This is a typical winter daytime wind and solar allowable error and load curve result diagram.

[0091] Figure 6 This is a typical summer daytime wind and solar allowable error and load curve result diagram.

[0092] Figure 7 The following are typical results of the daily head loss variation process in a shared tunnel during winter: (a) M1 model results; (b) M2 model results; (c) M3 model results; (d) M4 model results.

[0093] Figure 8The following are the results of the typical daily head loss variation process in a shared tunnel during summer: (a) M1 model results; (b) M2 model results; (c) M3 model results; (d) M4 model results. Detailed Implementation

[0094] The invention will be further described below with reference to the accompanying drawings and examples, mainly including a single-tunnel multi-unit pumped storage power station (such as...). Figure 1 The short-term joint scheduling model (shown) is presented in three parts: solution process, and practical application.

[0095] 1. Short-term joint dispatch modeling of a pumped storage power station with multiple units in one tunnel

[0096] (1) Objective function

[0097] Objective function: Minimize water consumption

[0098]

[0099] in:

[0100]

[0101] In the formula: The intermediate variable of the start-up and shutdown status of unit i in the nth pumped storage power station at time t under power generation and pumping conditions: 1 for power generation, 0 for shutdown, and -1 for pumping. is the absolute value of the intermediate variable; m is a small variable, taken as 1 × 10. -3 .

[0102] (2) Constraints

[0103] (2.1) Load balance constraints

[0104]

[0105] (2.2) Constraints of pumped storage power stations

[0106] Water balance constraints

[0107]

[0108]

[0109] Water level constraints

[0110]

[0111] Unit operating condition constraints

[0112]

[0113] Unit output constraints

[0114]

[0115] Unit flow constraints

[0116]

[0117] Head loss constraints

[0118]

[0119] Unit characteristic curve

[0120]

[0121] (2.3) Wind power plant constraints

[0122]

[0123] (2.4) Constraints of photovoltaic power plants

[0124]

[0125] 2. Solution process for the short-term joint dispatch model of a pumped storage power station with multiple units in one tunnel.

[0126] 2.1 To reduce prediction errors, wind and solar power plants sharing the transmission channel are aggregated into virtual units W and S, respectively.

[0127] 2.2 Based on the results of step 2.1, the probability distribution characteristics of wind and solar prediction errors are described by the nonparametric kernel density estimation method, and the corresponding probability density function (PDF) and cumulative distribution function (CDF) are obtained.

[0128] 2.3 Based on the results of step 2.2, by introducing random constraints, the positive and negative errors of wind and solar forecasts are adjusted and compensated by pumped storage power stations to ensure that load demand is met.

[0129] 2.4 Based on the results of step 2.3, the stochastic compensation constraints of the wind and light are transformed into deterministic compensation constraints, which are used as fixed constraints in the model.

[0130] 2.5 To establish a target of minimizing water consumption during the operation and start-up / shutdown of pumped storage power stations.

[0131] 2.6 Construct the head function for the shared water diversion tunnel.

[0132] 2.7 Construct a head loss optimization strategy for shared water diversion tunnels.

[0133]

[0134] 2.8 Based on the objectives, constraints, and scheduling strategies proposed above, the research model (M4) of this invention is constructed. Simultaneously, M1 (which does not consider water consumption and head loss optimization methods), M2 (which only considers water consumption optimization methods), and M3 (which considers water consumption and average allocation optimization methods) are constructed as comparative models.

[0135] 2.9 Based on the research and comparison models constructed in step 2.8, typical winter days with lower grid demand and typical summer days with higher load are selected as research cases. Taking the wind-solar-storage complementary operation problem of a multi-unit pumped storage power station as the background, a unified solution is performed on the short-term joint dispatch model. The detailed process is as follows: Figure 2 As shown.

[0136] 3. Practical Application of Short-Term Joint Dispatch Model for Multi-Unit Pumped Storage Power Stations

[0137] Taking a clean energy base in southeastern China as a case study, this base includes a large pumped-storage power station P with a "two-tunnel, eight-unit" layout, as well as a virtual wind power plant W and a virtual photovoltaic power plant S connected to the same main grid. Power station P has a design head of 400 meters and a water diversion tunnel with a total length of 4342 meters. It primarily undertakes the regulation tasks under the conditions of huge power fluctuations and high load demand in the power grid of a province in southeastern my country. The scheduling model adopts a day-ahead scheduling mode, with a scheduling cycle set to 24 hours and a calculation period of 1 hour. The optimization model is solved using Gurobi version 11.0.1. The basic parameters of each power source in the clean energy base are shown in Table 1.

[0138] Table 1

[0139]

[0140] By employing a nonparametric kernel density estimation method and stochastic parameter constraints, the uncertainty arising from the prediction errors of virtual wind power plant W and virtual photovoltaic power plant S was quantified, generating... Figure 3 and Figure 4 The probability distribution of wind and solar forecast errors for different typical days is shown. Based on this, the 5% to 95% confidence interval is extracted, and combined with the bidirectional regulation capability of pumped storage power stations, the following is determined: Figure 5 and Figure 6 The allowable error range of wind and solar power output forecasts in different typical day-to-day plans, as well as the main input conditions of the model composed of grid load demand and pumped storage power station regulation output, are discussed.

[0141] In a typical winter day scenario, wind power output is high, solar power output is relatively low, and the overall grid load demand is small. Under this condition, not all units of the pumped storage power station need to be started to meet the uncertainties of wind and solar power output and grid load requirements. Therefore, there is considerable room for optimization in the unit start-up and shutdown combinations. According to the data analysis in Table 2, under the same load demand conditions, the M4 model significantly reduces the water consumption for operation and start-up / shutdown due to the introduction of start-up / shutdown optimization and load allocation strategies. Compared with the M1 model, the M4 model increases pumping water consumption by 2.36%, reduces power generation water consumption by 5.55%, and reduces start-up / shutdown water consumption by 31.82%, effectively ensuring the economical operation of a multi-unit pumped storage power station. As shown in Table 3, under the effect of start-up / shutdown optimization, the M4 model only requires 40 unit starts during the scheduling process, significantly lower than the 58 starts for M1 and the 52 starts for M3. Meanwhile, due to the introduction of the load sharing strategy, the number of operating units in the two tunnels in the M4 model is more evenly distributed, with the difference in the number of units always controlled within one unit. Especially during the midday peak (11:00–13:00) and evening peak (17:00–21:00), this effectively avoids the situation in the M1 and M2 models where head loss is concentrated in a single tunnel, and also prevents the additional head loss problem caused by over-balancing in the M3 model. Figure 7 As shown, during the early morning pumping phase, due to the constraints of constant-speed unit operation and full-load limitations, the head loss generated by the four models in the two tunnels was almost identical. However, during the evening peak load period, the M4 model, due to its load distribution strategy, consistently minimized the head loss between the two tunnels, avoiding both significant concentration of head loss in a single tunnel and additional losses caused by excess flow. The overall head loss during peak periods was controlled within 10 meters, with a maximum value of only 9.2 meters. This result provides effective practical experience for the economical dispatching of multi-unit pumped storage power stations in a single tunnel and further ensures the safety of equipment operation within the tunnel.

[0142] In a typical summer day scenario, due to a significant increase in grid load demand, most pumped-storage units need to operate continuously for extended periods, leaving relatively little room for start-up and shutdown optimization. However, the massive operating flow during peak load periods significantly exacerbates head loss within the water diversion tunnels, indicating substantial optimization potential in the load allocation process. Therefore, the data in Table 4 shows that the M4 model demonstrates a particularly outstanding optimization effect in terms of power generation water consumption, improving it by 15.6% compared to the M1 model. Further analysis in Table 5 shows that the M4 model still manages to control the number of unit start-ups and shutdowns as much as possible and optimizes load allocation between tunnels, especially during the lower load period from noon to afternoon (11:00–17:00), reducing the number of start-ups and shutdowns on a typical summer day from 23 in the M1 model to 18, resulting in a reduction of approximately 6000 m³ of water consumption and effectively lowering system operating costs. Figure 8The study demonstrates the changes in calculated head and head loss for each model within the tunnel during a typical summer day. Similar to a typical winter day, during the early morning pumping phase, the four models exhibit similar head changes due to limitations such as the fixed-speed turbines. However, during the evening peak load period, model M4, by dynamically optimizing the difference in the number of operating turbines between tunnels, keeps the head loss within a small range, with a maximum head loss of only 8.1 meters, significantly improving the power station's operational stability and dispatch safety under high load conditions.

[0143] In summary, compared to traditional short-term joint dispatch models for wind, solar, and pumped storage power stations, the method proposed in this invention not only effectively quantifies the water consumption characteristics of pumped storage power stations under opposite operating conditions and frequent start-stop scenarios, but also optimizes load distribution within shared tunnels, significantly reducing head loss and the number of start-stop cycles during dispatch, thus ensuring the safe, stable, and economically efficient operation of clean energy bases. This method has significant practical application value and promising prospects for the dispatch and operation management of numerous large-scale multi-unit pumped storage power stations in my country.

[0144] Table 2

[0145]

[0146] Table 3

[0147]

[0148] Table 4

[0149]

[0150] Table 5

[0151]

Claims

1. A short-term wind-solar-storage joint scheduling method considering water consumption and head loss optimization, characterized in that, First, by combining virtual power plant strategy and nonparametric kernel density estimation, the uncertainty of prediction error of distributed wind and solar power plants is comprehensively described, and a wind and solar input process containing random parameters is generated. Secondly, with the goal of minimizing water consumption, the water consumption characteristics of pumped storage power stations under opposite operating conditions and frequent start-stop operations are accurately modeled to improve the operating economy of the power station. Finally, based on the head function optimization strategy, the huge head loss variation in the shared tunnel of the pumped storage power station is characterized, and the load distribution between tunnels is controlled by the balancing strategy.

2. The short-term wind-solar-storage joint scheduling method considering water consumption and head loss optimization according to claim 1, characterized in that, Specifically, the steps include the following: Step (1) In order to reduce prediction errors, wind and solar power plants sharing the transmission channel are aggregated into virtual units W and S, respectively; Step (2) Based on the results of step (1), the probability distribution characteristics of the wind and solar prediction error are described by the nonparametric kernel density estimation method, and the corresponding probability density function and cumulative distribution function are obtained. Step (3) Based on the results of step (2), by introducing random constraints, the positive and negative errors of wind and solar forecasts are adjusted and compensated by pumped storage power stations to ensure that load demand is met. (3.1) Positive compensation constraint: Use pumped storage power stations to compensate for insufficient power output from wind and solar forecasts; (1) In the formula: t represents the probability that the actual power output exceeds the grid load during time period t; N represents the number of pumped storage power stations. Let be the maximum output of the nth pumped storage power station during time period t, in MW; Let W and S be the actual power outputs (MW) of the virtual wind power plant W and the photovoltaic power plant S during time period t, respectively. Let t be the demand load of the power grid during time period t, in MW; The confidence level of the cumulative distribution function in positive compensation is % (3.2) Negative compensation constraint: Use pumped storage power stations to absorb excess power from wind and solar forecasts; (2) In the formula: This represents the probability that the actual power output is less than the grid load during time period t. Let be the minimum output (MW) of the nth pumped storage power station during time period t; The confidence level of the cumulative distribution function in negative compensation is % Step (4) Based on the results of step (3), the random compensation constraints of the scenery are transformed into deterministic compensation constraints as fixed constraints; (4.1) Positive compensation constraint handling; (3) In the formula: Let be the power output (MW) of the nth pumped storage power station during time period t; Let W and S be the predicted power outputs (MW) of the virtual wind farm W and photovoltaic farm S respectively during time period t. These are the inverse functions of the cumulative distribution functions of wind and solar energy at time t, respectively. (4.2) Handling of negative compensation constraints; (4) Step (5) Construct the target of minimizing water consumption for pumped storage power stations; The objective function expression is shown in equation (5): (5) In the formula: Let m³ be the water consumption of the nth pumped storage power station during the dispatch day. The water consumption during the start-up and shutdown of the nth pumped storage power station on the dispatch day, in m³; (5.1) Construction of water consumption for pumped storage power station operation The operating water consumption of a pumped storage power station is defined as the water volume after offsetting the water consumption for power generation and pumping; the specific construction process is as follows: (6) (7) (8) In the formula: Let m³ represent the power generation and water consumption for pumping of the nth pumped storage power station during the dispatch day. Let be the power generation and pumping flow rate of unit i in the nth pumped storage power station during time period t, respectively, in m³ / s; The time period for calculation is h; (5.2) Construction of water consumption for start-up and shutdown of pumped storage power stations; Based on the start-up and shutdown characteristics of the generating units, various start-up and shutdown operations are divided into two types: generation-shutdown and pumping-shutdown. Integer, binary, and micro-variables are introduced to accurately quantify these operations. The specific construction process is as follows: (9) In the formula: Let m³ represent the water consumption of the nth pumped storage power station during the dispatch day for power generation-start-stop and pumping-start-stop. in: (10) (11) In the formula: Let be the water consumption (m³) of the i-th generating unit of the n-th pumped storage power station during a single power generation and start-up / shutdown cycle. Let be the water consumption (m³) of the i-th unit of the n-th pumped storage power station during a single pumping-start-stop cycle; These represent the start-up and shutdown status of the i-th unit of the n-th pumped storage power station during time period t, where 1 represents power generation and 0 represents shutdown. These represent the start-up and shutdown status of the i-th unit of the n-th pumped storage power station during time period t, where 1 represents pumping and 0 represents shutdown. Step (6) Construct the head function of the shared water diversion tunnel; The calculation head function for pumped storage power stations under power generation and pumping conditions is constructed separately: (12) (13) In the formula: Let m be the power generation and pumping head of the nth pumped storage power station in the kth tunnel during time period t; Let be the upper and lower reservoir water levels of the nth pumped storage power station during time period t, respectively, in meters. Let m be the head loss of the nth pumped storage power station during time period t, which is the head loss for power generation and pumping in the kth tunnel. in: (14) (15) (16) (17) In the formula: These are the head loss coefficients for power generation and pumping operations, respectively; Let M and S be the power generation and pumping flow rates of the nth pumped storage power station in the kth tunnel during time period t, respectively; Let Y and I be the power generation and pumping flow rates of the y-th unit in the k-th tunnel of the n-th pumped storage power station during time period t, respectively, in m³ / s; Y is the number of shared tunnels; and I is the number of pumped storage units. Step (7) Construction of head loss optimization strategy for shared water diversion tunnel; (7.1) First, the number of operating units in each tunnel is characterized; (18) (19) In the formula: This represents the operating condition of the i-th unit of the n-th pumped storage power station during time period t, where 1 represents power generation, 0 represents shutdown, and -1 represents pumping. The absolute value of the operating condition of the i-th unit of the n-th pumped storage power station during time period t; This represents the number of generating units operating in the k-th tunnel during time period t for the n-th pumped storage power station. (7.2) Then, the number of operating units between tunnels is balanced and distributed so that the difference in the number of operating units between adjacent tunnels of the same power station at the same time does not exceed 1, so as to ensure that the flow rate change in each tunnel is as small as possible, thereby effectively optimizing the head loss in the tunnel. (20) (21) Step (8) is based on the results of wind and solar uncertainty processing in steps (1) to (4), combined with the objective function of minimizing water consumption of pumped storage power station constructed in step (5), the head function of shared water diversion tunnel in step (6), the head loss optimization strategy of shared water diversion tunnel in step (7), and the operation constraints in wind-solar-storage joint scheduling, to jointly construct a mathematical model for short-term wind-solar-storage joint optimization; the model takes the daily output of pumped storage power station as the decision variable, and performs global optimization solution through solver to obtain the optimal pumped storage output and unit combination scheme under different load demand and wind-solar scenarios.

3. The short-term wind-solar-storage joint scheduling method considering water consumption and head loss optimization according to claim 1, characterized in that, In step (8), the operational constraints are as follows: (8.1) Supply and demand balance constraints; (22) In the formula: Let be the power output (MW) of the nth pumped storage power station during time period t; Let m be the predicted power output of the m-th wind farm during time period t, in MW. Let L be the predicted power output of the l-th photovoltaic power plant in time period t, in MW; N be the number of pumped storage power stations; M be the number of wind power plants; and L be the number of photovoltaic power plants. (8.2) Constraints on pumped storage power stations; a) Water balance constraint (23) (24) In the formula: Let be the upper and lower reservoir capacities of the nth pumped storage power station during time period t, respectively, in m³. Let be the operating flow rate of the nth pumped storage power station during time period t, in m³ / s; b) Water level constraints between upper and lower reservoirs (25) (26) In the formula: Let be the upper and lower reservoir water levels of the nth pumped storage power station at time t, respectively, in meters. Let m be the upper and lower limits of the upper reservoir water level of the nth pumped storage power station; Let m be the upper and lower limits of the lower reservoir water level of the nth pumped storage power station; Let m be the initial water level of the upper and lower reservoirs of the nth pumped storage power station; c) Unit state constraints (27) (28) (29) In the formula: The operating conditions of the nth pumped storage power station at time t, excluding the i-th unit; The operating conditions of unit i in pumped storage power stations other than the nth pumped storage power station at time t; d) Unit output constraints (30) (31) In the formula: Let be the output of unit i of the nth pumped storage power station at time t, in MW; Let be the power generation and pumping output of unit i at time t, respectively, in MW; These are the rated power generation and pumping outputs (MW) of the i-th unit of the n-th pumped storage power station, respectively. e) Unit flow constraints (32) (33) In the formula: Let be the total flow rate of the nth pumped storage power station at time t, in m³ / s; Let be the flow rate of unit i in the nth pumped storage power station at time t, in m³ / s; Let be the power generation and pumping flow rate of unit i at time t, respectively, in m³ / s; These are the rated flow rates for power generation and water pumping of the i-th unit in the n-th pumped storage power station, respectively, in m³ / s; f) Water level and reservoir capacity curve (34) (35) In the formula: These are the water level and storage capacity curves for the upper and lower reservoirs of the nth pumped storage power station, respectively. g) Relationship curve between unit power generation and pumping (36) In the formula: The curve showing the relationship between the output P, ​​flow rate Q, and head H of the i-th unit of the n-th pumped storage power station; (8.3) Wind power output constraints; (37) In the formula: The power output of the m-th wind farm is expressed in MW. Let m be the installed capacity of the m-th wind farm, in MW; (8.4) Photovoltaic power output constraints; (38) In the formula: The output of the lth photovoltaic power plant is MW; Let be the installed capacity of the l-th photovoltaic power plant, in MW.

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