A method for optimizing pump station energy efficiency scheduling considering energy consumption weighting and lake regulation.

Abstract

This invention discloses a method for optimizing the energy efficiency scheduling of pumping stations, considering energy consumption weights and lake regulation. The method includes: 1) constructing an operational model of a water-transfer pumping station system consisting of a pumping station, an open channel, and a lake; 2) constructing a medium-to-long-term and short-term time-period energy consumption weight uncertainty quantification model based on time-period energy consumption weight data; 3) constructing a medium-to-long-term and short-term energy efficiency optimization scheduling model for the water-transfer pumping station based on the operational model and the energy consumption weight uncertainty quantification model; and 4) solving the medium-to-long-term model using an adaptive particle swarm optimization algorithm and solving the short-term model using a nonlinear integer programming method. This invention improves the precision, rationality, and operational efficiency of water-transfer pumping station scheduling by employing intelligent methods, achieving intelligent scheduling and assisting scheduling personnel in making scientific decisions.

Description

Technical Field

[0001] This invention belongs to the field of system engineering operation and scheduling, and in particular, relates to a method for optimizing the scheduling of pumping station energy efficiency by considering energy consumption weight and lake regulation. Background Technology

[0002] To address the imbalance of water resources between regions, my country has constructed a large number of inter-basin water transfer and backbone water network projects, including the South-to-North Water Transfer Project and the Yangtze River-Huaihe River Water Diversion Project. On the one hand, the key pumping stations involved in these projects occupy crucial positions in water conservancy projects, characterized by large scale, high power, and advanced technology, undertaking important water resource scheduling and control tasks. Their energy consumption and efficiency are also prominent issues. On the other hand, China's installed capacity of new energy sources is continuously expanding, and the power system is undergoing significant changes. Due to the increasing penetration rate of new energy sources, the load characteristics of the power grid vary significantly across time periods, necessitating the exploration of the flexible adjustment potential on the load side to improve system stability and energy efficiency. Therefore, as an important adjustable load on the power grid side, how to effectively match the time-period energy consumption weights of key pumping stations with the storage capacity of lakes, maximizing energy conservation and consumption reduction while completing water transfer tasks and improving system energy efficiency, and achieving optimal energy efficiency operation of the pumping station and lake combined system, is the core technical problem this invention aims to solve. Summary of the Invention

[0003] To address the problems existing in the scheduling of existing pumping stations, this invention proposes a pumping station energy efficiency optimization scheduling method that considers energy consumption weight and lake regulation, aiming to obtain pumping station energy efficiency operation strategies under different time scales and energy consumption weight scenarios, thereby effectively reducing pumping station operating energy consumption and improving pumping efficiency.

[0004] To achieve the above objectives, the present invention adopts the following technical solution: The present invention provides a method for optimizing the scheduling of pumping station energy efficiency, considering energy consumption weights and lake regulation. This method is applied to a water regulation system consisting of a pumping station, an open channel, and a lake, and includes the following steps: Step 1: Construct an operational model of the water diversion system consisting of water diversion pumping stations, open channels, and lakes; Step 2: Based on energy consumption weight data, construct a prediction model for medium- and long-term monthly average energy consumption weight, in order to further construct a short-term energy consumption weight scenario set and its typical energy consumption weight scenario set, and then calculate the probability of occurrence of each typical energy consumption weight scenario set based on the number of scenarios contained in each typical energy consumption weight scenario set. Step 3: Based on Step 1 and Step 2, construct a medium- and long-term water volume scheduling model and a short-term energy efficiency optimization operation stochastic programming model for the pumping station; Step 4: Using the first Monthly water diversion volume of pumping station, Monthly pump station start / stop status quantity, the first The long-term water allocation scheme of the pumping station, which consists of the water abandoned by the lunar lake, is taken as a particle. The weighted result of the weighted total energy consumption of the long-term pumping station and the penalty value obtained by equations (11)-(15) is used as the fitness of each particle. Thus, the adaptive particle swarm algorithm is used to solve the long-term water allocation model of the pumping station and obtain the planning scheme of the long-term water allocation of the pumping station. Step 5: Use nonlinear integer programming to solve the short-term energy efficiency optimization stochastic programming model of the pump station and obtain the short-term water dispatch plan for the pump station units.

[0005] The characteristic of the pump station energy efficiency optimization scheduling method considering energy consumption weight and lake regulation described in this invention is that step 1 includes: Step 1.1: Determine all feasible operating points of the pump station and record the pump station flow rate and head corresponding to all feasible operating points. Determine the feasible range of pump station flow rate and head, and then use equation (1) to obtain Units in time-period pumping stations lift With traffic The relationship between the pump station units is established, and equation (2) is used to fit the flow-head-efficiency characteristic curves of the pump station units. : (1) (2) In equations (1) and (2), , for Water levels before and after the pumping station during specific time periods; , and For the pumping station units The three correlation coefficients; for Units in time-period pumping stations Pump efficiency; Step 1.2: Determine the storage capacity between the normal water level and the highest water level of the lake as the regulating storage capacity, and use equation (3) to obtain... Remaining storage capacity during the period Thus, equation (4) is used for fitting. Remaining storage capacity during the period and Lake water level during the period Relationship curve : (3) (4) In equations (3) and (4), for The amount of water discharged from the lake during a given period; for The lake's water supply during a given period; for The amount of water discharged from the lake during a given period; for The amount of runoff into the lake during a given time period; for The amount of water diverted into the lake during a given period is the amount of water diverted by the pumping station. This is the maximum water level of the lake; for Remaining storage capacity during the period; Step 1.3: Determine the input-output relationship between the open channel, pumping station, and lake, and construct a one-dimensional steady flow model using equation (5): (5) In equation (5), , for The water level at the pumping station and the water level flowing into the lake at the end of the open channel during the specified time period. For the Manning roughness of open channels, This refers to the length of the open channel.

[0006] Furthermore, step 2 includes: Step 2.1: Determine the division of medium- and long-term scheduling periods into short-term and medium-term periods: The medium- and long-term scheduling cycle includes a total of For each month, the index set for the scheduling month is set as follows: ,use Indicates the first moon, The short-term scheduling cycle includes a total of Hours, set the index set of scheduling hours as ,use Indicates the first Hour, ; Step 2.2: Construct a prediction model for the medium- and long-term average monthly energy consumption weight using equation (6), and process the medium- and long-term average monthly energy consumption weight to obtain the prediction results for the medium- and long-term average monthly energy consumption weight: (6) In equation (6), It is a lag operator; For seasonal cycles, express of Power; , They are respectively seasonality and A non-seasonal autoregressive polynomial of order 1; , for seasonality and non-seasonal moving average polynomial of order 1. For non-seasonal models, this refers to the order of the model. The order of the seasonal model; For the first Monthly average energy consumption weighting data; For the first Random error per month; Step 2.3: Use the K-means method to cluster the hourly energy consumption weight data, thereby dividing it into... Classify energy consumption weight levels, and denote the index set of all energy consumption weight levels as ,Right now Thus, equation (7) is used to apply the first... The data of energy consumption weight levels were fitted with KDE to obtain the first... Class energy consumption weight level in the first One estimated point Estimated density function , : (7) In equation (7), For the first The smoothing bandwidth corresponding to the energy consumption weight level; For the first The total number of data points included in the energy consumption weight level; For kernel functions; For the first In the energy consumption weight level, the first Data, of which, ; Step 2.4: Utilize For the The energy consumption weight levels are resampled, and the sampling results are reduced using the Kantorovich distance method to obtain the reduced and retained sampling result set. ;in, Indicates the first element in the retained sampling result set. One sampling result; The index set for the retained sampling result set; The retained sampling results set from various energy consumption weight levels Selected from Month Parameters of energy consumption weight level : ,in, For the first The set of all energy consumption weight levels included in the month; Step 2.5: Construct the first step using equation (8) Mid-month Short-term energy consumption weighting scenario (hours) : (8) In equation (8), For the first The month corresponds to the first The time period set of class consumption weight levels; for The Middle Does an hour belong to Indicator functions; Step 2.6: Based on the prediction results of the medium- and long-term average monthly energy consumption weights, the generated short-term energy consumption weight scenarios are screened, retaining only those scenarios where the error between the short-term average energy consumption weight and the corresponding medium- and long-term prediction results does not exceed a threshold; thus, the K-medoids clustering algorithm is used to extract the first... Mid-month Typical energy consumption weighting scenarios for hours .

[0007] Furthermore, step 3 includes: Step 3.1: Construct the objective function of the medium- and long-term water allocation model for the pumping station using equations (9) and (10): (9) (10) In equations (9) and (10), Weighted total energy consumption of pumping stations in the medium and long term; For the first Monthly pump station operating energy consumption; For the first Monthly average energy consumption weighted prediction results; The density of water; It is the acceleration due to gravity; For the first The head of the monthly pumping station; For the first Monthly water diversion volume of the pumping station; For the first The start / stop status of the monthly pumping station; This refers to the average operating efficiency of the pumping station. Step 3.2: Establish the pump station water diversion constraints for the medium- and long-term water diversion model of the pump station using equation (11): (11) In equation (12), and The minimum and maximum water volumes allowed for water diversion at the pumping station; For the first Monthly water diversion volume from pumping stations; Step 3.3: Establish lake water balance constraints for the medium- and long-term water allocation model of the pumping station using equation (12): (12) In equation (12), For the first The amount of runoff into Moon Lake; For the first The amount of water diverted from Moon Lake; For the first Water supply to Moon Lake; For the first The amount of water discarded from Moon Lake; Step 3.4: Using equations (13) and (14), establish the flood season and non-flood season lake remaining storage capacity scheduling rules for the medium- and long-term water volume scheduling model of the pumping station: (13) (14) In equations (13) and (14), and The remaining storage capacity of the largest lake and the remaining storage capacity of the smallest lake. For the first The remaining storage capacity of lakes during the flood season month This is a set of indices for the flood season months; For the first The remaining storage capacity of lakes during the non-flood season month This is a set of indexes for non-flood season months. It is the last month of the non-flood season; Step 3.5: Establish the flood season and non-flood season lake water discharge rules for the medium- and long-term water volume scheduling model of the pumping station using equation (15): (15) In equation (15), For the first The amount of water discharged from lakes during the flood season; For the first The amount of water discharged from lakes during non-flood season months; Step 3.6: Construct the objective function of the short-term pump station energy efficiency optimization stochastic programming model using equations (16) and (17): (16) (17) In equations (16) and (17), The weighted total energy consumption for short-term expected operation of the pumping station; For the first Total number of short-term periods within the month; For the first A set of indexes for typical energy consumption weighting scenarios within the month; For the first Typical energy consumption weighting scenarios within the month The probability of occurrence; For the first Typical energy consumption weighting scenarios within the month Weighted total energy consumption of the pumping station during short-term operation; For unit assembly; For the first Typical energy consumption weighting scenarios within the month Lower unit In the Hourly overcurrent flow rate; For the first Typical energy consumption weighting scenarios within the month Lower unit In the Start / stop status for each hour; For the first Typical energy consumption weighting scenarios within the month Next Pump station head per hour; Hour length; For the first Typical energy consumption weighting scenarios within the month Lower unit In the Hourly operating efficiency; Power consumption during startup; For the first Typical monthly energy consumption weighting scenarios Lower unit In the The power-on process takes about an hour; Step 3.7: Using Equation (18), establish rigid constraints for the pump station water diversion planning scheme of the short-term pump station energy efficiency optimization operation stochastic programming model: (18) Step 3.8: Establish the overcurrent capacity constraint of the pump station unit using equation (19): (19) In equation (19), and These are the minimum and maximum flow rates for the pump station unit. Step 3.9: Establish the lake's short-term remaining storage capacity scheduling rules for the short-term pump station energy efficiency optimization operation stochastic programming model using equations (20) and (21): (20) (twenty one) In equations (20) and (21), For the first Typical energy consumption weighting scenarios within the month Next The remaining storage capacity of the lake after 24 hours; For the first Typical energy consumption weighting scenarios during the flood season month Next The remaining storage capacity of the lake after 24 hours; For the first An index set of energy consumption weighting scenarios for the flood season month; For the first Typical energy consumption weighting scenarios within the month Next The remaining storage capacity of the lake after 24 hours; For the last hour of a short-term scheduling period; Step 3.10: Establish the open channel water conveyance capacity constraint for the short-term pump station energy efficiency optimization operation stochastic programming model using equation (22): (twenty two) In equation (22), and Minimum and maximum flow rates for water conveyance in open channels; Step 3.11: Establish the minimum continuous operating time constraint of the pump station unit for the short-term pump station energy efficiency optimization operation stochastic programming model using equations (23) and (24): (twenty three) (twenty four) In equations (23) and (24), For the first Typical energy consumption weighting scenarios within the month Lower unit In the Start / stop status for each hour; For the first Typical energy consumption weighting scenarios within the month Lower unit In the Start / stop status for each hour; This refers to the number of hours the pump station unit has been running since startup. This refers to the minimum continuous operating time of the pump station unit.

[0008] Furthermore, step 5 includes: Step 5.1: Use the SOS2 method to transform equation (2) into equation (25) - equation (27): (25) (26) (27) In equations (25)-(27), For the index of discrete segmentation points; A set of indices for discrete segmentation points; For the first Flow points at discrete segmentation points; for Units in time-period pumping stations In the Non-negative weighted variables at discrete segmentation points, and ; Step 5.2: Relax equation (18) to obtain equation (28): (28) In equation (28), The error coefficient; Step 5.3: Use a solver to solve the transformed short-term pump station energy efficiency optimization stochastic programming model to obtain the short-term water dispatch plan for the pump station units.

[0009] The present invention provides an electronic device, including a memory and a processor, characterized in that the memory is used to store a program supporting the processor in performing the method described therein, and the processor is configured to execute the program stored in the memory.

[0010] The present invention discloses a computer-readable storage medium storing a computer program, characterized in that the computer program is executed by a processor to perform the steps of the method described thereon.

[0011] The advantages of this invention, which differ from existing technologies, are mainly reflected in the following aspects: 1. This invention realizes multi-timescale collaborative scheduling optimization of pumping stations, improving the precision and rationality of scheduling: By constructing a "medium-long-term-short-term" hierarchical collaborative scheduling mechanism, the upper-level medium-long-term model clarifies the decision boundary of pumping volume for ten-day periods, taking into account the long-term evolution of energy consumption weights and the differences in lake regulation rules during flood season and non-flood season, providing reliable water volume constraints for the lower-level short-term scheduling; the lower-level short-term model focuses on unit start-up and shutdown control, and combines the random fluctuation of energy consumption weights during time periods with the real-time regulation space of lakes to formulate scheduling strategies adapted to multiple scenarios, effectively realizing the rational allocation of water resources in time and space, solving the problems of lack of multi-scale linkage and insufficient precision in traditional scheduling, improving the energy efficiency and energy saving of pumping station operation, and possessing significant engineering application value and technical benefits.

[0012] 2. This invention establishes a nested collaborative scheduling model, constructing a hierarchical collaborative scheduling mechanism of "medium-to-long-term and short-term," where the upper-level model is responsible for medium-to-long-term decision-making, and the lower-level model is responsible for short-term execution. This technical feature effectively solves the problems of disconnect between medium-to-long-term planning and short-term execution in traditional scheduling, and the lack of multi-scale linkage leading to unreasonable water allocation. It thus achieves vertical integration of scheduling strategies, ensures that short-term operations strictly adhere to medium-to-long-term planning objectives, and improves the systematicness and rationality of pump station scheduling.

[0013] 3. This invention innovatively incorporates the long-term evolution law of energy consumption weights. In the upper-level medium- and long-term model, it scientifically clarifies the decision boundary for pumping station pumping volume per ten-day period, taking into account the significant differences in lake regulation rules between flood season and non-flood season. This method overcomes the problem of existing technologies that only focus on seasonal hydrological changes while ignoring medium- and long-term differences in grid-side load energy consumption, resulting in a lack of foresight in water quantity constraint formulation. It not only provides reliable water quantity constraints and directional guidance for lower-level short-term scheduling but also effectively avoids flood control risks during the flood season and resource waste during the non-flood season, thus improving the robustness of decision-making.

[0014] 4. This invention closely integrates the random fluctuations of energy consumption weights over time periods with the real-time storage capacity of lakes. The lower-level short-term model formulates dynamic scheduling strategies adapted to various complex operating scenarios for refined control of unit start-up and shutdown. This feature breaks through the limitations of traditional scheduling in dealing with real-time energy consumption fluctuations and the single and rigid operation mode of units, realizing refined control of pump station operation status and improving the energy efficiency and energy saving of unit operation.

[0015] 5. This invention optimizes water resources in multiple dimensions across time and space through information transmission between upper and lower level models. This process overcomes the limitations of traditional methods in spatiotemporal resource allocation, solves the problems of inflexible scheduling schemes and global optimality, and maximizes the utilization of lake storage capacity and peak-valley electricity price differences in the power grid. While ensuring water supply security, it significantly reduces operating costs and has significant engineering application value and technical benefits. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the pump station-lake water diversion system provided by the present invention; Figure 2 This is a framework diagram of the long-term and short-term energy efficiency optimization scheduling model for pumping stations proposed in this invention. Figure 3 The flowchart for solving the medium-to-long-term and short-term optimization model provided by this invention is shown. Detailed Implementation

[0017] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0018] In this embodiment, a pump station energy efficiency optimization scheduling method considering energy consumption weight and lake regulation is applied to, for example... Figure 1 The pump station-lake water transfer system shown consists of a water transfer pump station, an open channel, and a lake. The open channel originates at the water transfer pump station and terminates at the lake. The pump station transports water from upstream to the lake via the open channel. The lake, as a storage node in the system, is responsible for receiving water from the open channel and supplying water to the surrounding areas. By employing intelligent methods, the system improves the precision, rationality, and operational efficiency of the water transfer pump station's scheduling, achieving intelligent scheduling and assisting dispatchers in making scientific decisions. Specifically, the main steps of this method are as follows: Step 1: Collect historical load data of the power grid, calculate the hourly energy consumption weight by the ratio of hourly load to total daily load, collect pump station operating condition data, lake water level and storage capacity data, determine the flow-head-efficiency characteristic curve of the pump station units and the lake water level-storage capacity relationship curve; construct an operation model of the water diversion system consisting of water diversion pump stations, open channels and lakes: Step 1.1: Determine all feasible operating points of the pump station and record the pump station flow rate and head corresponding to all feasible operating points. Determine the feasible range of pump station flow rate and head, and then use equation (1) to obtain Units in time-period pumping stations lift With traffic The relationship between the pump station units is established, and equation (2) is used to fit the flow-head-efficiency characteristic curves of the pump station units. : (1) (2) In equations (1) and (2), , for Water levels before and after the pumping station during specific time periods; , and For the pumping station units The three correlation coefficients; for Units in time-period pumping stations The efficiency of the water pump.

[0019] Step 1.2: Determine the storage capacity between the normal water level and the highest water level of the lake as the regulating storage capacity, and use equation (3) to obtain... Remaining storage capacity during the period Thus, equation (4) is used for fitting. Remaining storage capacity during the period and Lake water level during the period Relationship curve : (3) (4) In equations (3) and (4), for The amount of water discharged from the lake during a given period; for The lake's water supply during a given period; for The amount of water discharged from the lake during a given period; for The amount of runoff into the lake during a given time period; for The amount of water diverted into the lake during a given period is the amount of water diverted by the pumping station. This represents the maximum storage capacity corresponding to the normal water level. for Remaining storage capacity during the period.

[0020] Step 1.3: Determine the input-output relationship between the open channel, pumping station, and lake, and construct a one-dimensional steady flow model using equation (5): (5) In equation (5), , for The water level at the pumping station and the water level flowing into the lake at the end of the open channel during the specified time period. For the Manning roughness of open channels, This refers to the length of the open channel.

[0021] Step 2: Divide the scheduling cycle into medium- and long-term and short-term scheduling cycles, and construct a prediction model for the medium- and long-term monthly average energy consumption weight based on energy consumption weight data, which is used to further construct a short-term energy consumption weight scenario set; the SARIMA prediction model for the medium- and long-term average energy consumption weight, based on the medium- and long-term energy consumption weight prediction results, uses K-means clustering, scenario generation and reduction methods to obtain short-term energy consumption weight scenarios, and extracts typical energy consumption weight scenario sets through the K-medoid clustering algorithm and calculates the probability of occurrence of each scenario.

[0022] Step 2.1: Determine the division of medium- and long-term scheduling periods into short-term and medium-term periods: The medium- and long-term scheduling cycle includes a total of For each month, the index set for the scheduling month is set as follows: ,use Indicates the first moon, The short-term scheduling cycle includes a total of Hours, set the index set of scheduling hours as ,use Indicates the first Hour, .

[0023] Step 2.2: Calculate the weighted average of all hourly energy consumption weights included in each month to obtain the medium-to-long-term monthly average energy consumption weight. Construct a prediction model for the medium-to-long-term monthly average energy consumption weight using equation (6), and process the medium-to-long-term monthly average energy consumption weight to obtain the prediction results: (6) In equation (6), It is a lag operator; For seasonal cycles, express of Power; , They are respectively seasonality and A non-seasonal autoregressive polynomial of order 1; , for seasonality and non-seasonal moving average polynomial of order 1. For non-seasonal models, this refers to the order of the model. The order of the seasonal model; For the first Monthly average energy consumption weighting data; For the first Random error per month.

[0024] Step 2.3: Use the K-means method to cluster the hourly energy consumption weight data, thereby dividing it into... Classify energy consumption weight levels, and denote the index set of all energy consumption weight levels as ,Right now Thus, equation (7) is used to apply the first... The data of energy consumption weight levels were fitted with KDE to obtain the first... Class energy consumption weight level in the first One estimated point Estimated density function , : (7) In equation (7), For the first The smoothing bandwidth corresponding to the energy consumption weight level; For the first The total number of data points included in the energy consumption weight level; For kernel functions; For the first In the energy consumption weight level, the first Data, of which, .

[0025] Step 2.4: Utilize For the The energy consumption weight levels are resampled, and the sampling results are reduced using the Kantorovich distance method to obtain the reduced and retained sampling result set. ;in, Indicates the first element in the retained sampling result set. One sampling result; The index set for the retained sampling result set; The retained sampling results set from various energy consumption weight levels Selected from Month Parameters of energy consumption weight level : ,in, For the first The set of all energy consumption weight levels included in the month.

[0026] Step 2.5: Construct the first step using equation (8) All possible short-term energy consumption weighting scenarios in the middle of the month : (8) In equation (8), For the first The month corresponds to the first The time period set of class consumption weight levels; for any one of the middle Does an hour belong to The indicator function, i.e., when When the condition is met, the function value is 1; otherwise, it is 0.

[0027] Step 2.6: Based on the prediction results of the medium- and long-term average monthly energy consumption weights, the generated short-term energy consumption weight scenarios are screened, retaining only those scenarios where the error between the short-term average energy consumption weight and the corresponding medium- and long-term prediction results does not exceed a threshold; thus, the K-medoids clustering algorithm is used to extract the first... Mid-month Typical energy consumption weighting scenarios for hours The K-medoids clustering algorithm can extract the center of each cluster and the number of scenes contained therein. The cluster center is extracted as a typical energy consumption weight scene, and the probability of each typical energy consumption weight scene occurring is calculated by dividing the number of scenes contained in each cluster by the total number of scenes.

[0028] Step 3: Based on the prediction model of the medium- and long-term monthly average energy consumption weight and the short-term energy consumption weight scenario, construct a medium- and long-term water volume scheduling model and a short-term stochastic programming model for the energy efficiency optimization operation of the pumping station. For example... Figure 2 As shown, the upper layer is the medium-to-long-term water allocation model for the pumping station, and the lower layer is the short-term energy efficiency optimization stochastic programming model for the pumping station. The upper-layer model has a medium-to-long-term allocation cycle of one year, and the objective function is to minimize the medium-to-long-term weighted total energy consumption of the pumping station. The constraints include the pumping station's water allocation volume, lake water balance, flood season and non-flood season lake remaining storage capacity allocation rules, and water abandonment rules. The lower-layer model has a short-term allocation cycle of one day, and the objective function is to minimize the short-term expected weighted total energy consumption of the pumping station. The constraints include the pumping station's water allocation planning scheme, unit flow capacity, lake short-term lake remaining storage capacity allocation rules, open channel water conveyance capacity, and unit minimum continuous operating time.

[0029] Step 3.1: Construct the objective function of the medium- and long-term water allocation model for the pumping station using equations (9) and (10): (9) (10) In equations (9) and (10), Weighted total energy consumption of pumping stations in the medium and long term; For the first Monthly pump station operating energy consumption; For the first Monthly average energy consumption weighted prediction results; The density of water; It is the acceleration due to gravity; For the first The head of the monthly pumping station; For the first Monthly water diversion volume of the pumping station; For the first The start / stop status of the monthly pumping station; This represents the average operating efficiency of the pumping station.

[0030] Step 3.2: Establish the pump station water diversion constraints for the medium- and long-term water diversion model of the pump station using equation (11): (11) In equation (11), and The minimum and maximum water volumes allowed for water diversion at the pumping station; For the first Monthly water diversion volume of the pumping station.

[0031] Step 3.3: Establish lake water balance constraints for the medium- and long-term water allocation model of the pumping station using equation (12): (12) In equation (12), For the first The amount of runoff into Moon Lake; For the first The amount of water diverted from Moon Lake; For the first Water supply to Moon Lake; For the first The amount of water discarded from Moon Lake.

[0032] Step 3.4: Using equations (13) and (14), establish the flood season and non-flood season lake remaining storage capacity scheduling rules for the medium- and long-term water volume scheduling model of the pumping station: (13) (14) In equations (13) and (14), and The remaining storage capacity of the largest lake and the remaining storage capacity of the smallest lake. For the first The remaining storage capacity of lakes during the flood season month This is a set of indices for the flood season months; For the first The remaining storage capacity of lakes during the non-flood season month This is a set of monthly indexes outside of the flood season. It is the last month of the non-flood season.

[0033] Step 3.5: Establish the flood season and non-flood season lake water discharge rules for the medium- and long-term water volume scheduling model of the pumping station using equation (15): (15) In equation (15), For the first The amount of water discharged from lakes during the flood season; For the first The amount of water discharged from lakes during non-flood season months.

[0034] Step 3.6: Construct the objective function of the short-term pump station energy efficiency optimization stochastic programming model using equations (16) and (17): (16) (17) In equations (16) and (17), The weighted total energy consumption for short-term expected operation of the pumping station; For the first Total number of short-term periods within the month; For the first A set of indexes for typical energy consumption weighting scenarios within the month; For the first Typical energy consumption weighting scenarios within the month The probability of occurrence; For the first Typical energy consumption weighting scenarios within the month Weighted total energy consumption of the pumping station during short-term operation; For unit assembly; For the first Typical energy consumption weighting scenarios within the month Lower unit In the Hourly overcurrent flow rate; For the first Typical energy consumption weighting scenarios within the month Lower unit In the The start / stop status for each hour, 1 for unit running, 0 for unit stopped; For the first Typical energy consumption weighting scenarios within the month Next Pump station head per hour; Hour length; For the first Typical energy consumption weighting scenarios within the month Lower unit In the Hourly operating efficiency; Power consumption during startup; For the first Typical monthly energy consumption weighting scenarios Lower unit In the The number of times the unit is started up is 1, indicating that the unit has started up and 0 indicates that the unit has not started up.

[0035] Step 3.7: Using Equation (18), establish rigid constraints for the pump station water diversion planning scheme of the short-term pump station energy efficiency optimization operation stochastic programming model: (18) Step 3.8: Establish the overcurrent capacity constraint of the pump station unit using equation (19): (19) In equation (19), and These are the minimum and maximum flow rates for the pump station unit.

[0036] Step 3.9: Establish the lake's short-term remaining storage capacity scheduling rules for the short-term pump station energy efficiency optimization operation stochastic programming model using equations (20) and (21): (20) (twenty one) In equations (20) and (21), For the first Typical energy consumption weighting scenarios within the month Next The remaining storage capacity of the lake after 24 hours; For the first Typical energy consumption weighting scenarios during the flood season month Next The remaining storage capacity of the lake after 24 hours; For the first An index set of energy consumption weighting scenarios for the flood season month; For the first Typical energy consumption weighting scenarios within the month Next The remaining storage capacity of the lake after 24 hours; This refers to the last hour of a short-term scheduling period.

[0037] Step 3.10: Establish the open channel water conveyance capacity constraint for the short-term pump station energy efficiency optimization operation stochastic programming model using equation (22): (twenty two) In equation (22), and Minimum and maximum flow rates for water conveyance in open channels.

[0038] Step 3.11: Establish the minimum continuous operating time constraint of the pump station unit for the short-term pump station energy efficiency optimization operation stochastic programming model using equations (23) and (24): (twenty three) (twenty four) In equations (23) and (24), For the first Typical energy consumption weighting scenarios within the month Lower unit In the Start / stop status for each hour; For the first Typical energy consumption weighting scenarios within the month Lower unit In the Start / stop status for each hour; This refers to the number of hours the pump station unit has been running since startup. This refers to the minimum continuous operating time of the pump station unit.

[0039] Step 4: Use the adaptive particle swarm optimization algorithm to solve the medium- and long-term water allocation model of the pumping station. Obtain the medium- and long-term monthly water allocation volume of the pumping station through the optimal particles, and generate the optimal planning scheme for the medium- and long-term water allocation of the pumping station. The solution process is as follows: Figure 3 As shown.

[0040] Step 4.1: Set the initial parameters for the adaptive particle swarm optimization algorithm: population size Medium and long-term scheduling cycle Number of iterations ; Step 4.2: Initialize the particle swarm population: randomly generated The initial particles constitute the particle set. , represented as ,in, For the first The nth particle represents the nth particle. Possible medium- and long-term water allocation schemes for pumping stations ;in, For the first The first particle Monthly water diversion volume from pumping stations; For the first The first particle The start / stop status of the monthly pumping station; For the first The first particle The amount of water discarded from Moon Lake.

[0041] Step 4.3: Select equations (9)-(10) as the algorithm objective and equations (11)-(15) as the algorithm penalty terms, and the particle swarm fitness... The fitness of the initial particle swarm is calculated by multiplying the target value by the target weight and the penalty value by the penalty weight, and then adding them together. The optimal individual is then selected. and global optimal .

[0042] Step 4.4: Update the particle swarm velocity, position, inertial weights, individual acceleration, and social acceleration to generate a child particle swarm. : Step 4.5: Calculate the fitness of the offspring particle swarm and compare it with the original particle swarm. Select the particle swarm with the smaller fitness and update the individual optimum and global optimum.

[0043] Step 4.6: Determine if the preset number of iterations has been reached. If the maximum number of iterations has not been reached, repeat steps 4.3-4.5; otherwise, proceed to step 4.7. Step 4.7: Stop the iteration process and output the water diversion scheme corresponding to the optimal particle as the optimal planning scheme for the medium and long-term water volume scheduling of the pumping station.

[0044] Step 5: Use nonlinear integer programming to solve the short-term energy efficiency optimization operation stochastic programming model of the pump station, and obtain the pump station unit flow rate and start-up and shutdown scheme to form the short-term optimal water volume scheduling plan for the pump station unit; Step 5.1: Use the SOS2 method to transform equation (2) into equation (25) - equation (27): (25) (26) (27) In equations (25)-(27), For the index of discrete segmentation points; A set of indices for discrete segmentation points; For the first Flow points at discrete segmentation points; for Units in time-period pumping stations In the Non-negative weighted variables at discrete segmentation points, and ; Step 5.2: Relax equation (18) to obtain equation (28): (28) In equation (28), This is the error coefficient.

[0045] Step 5.3: Use a solver to solve the transformed short-term pump station energy efficiency optimization stochastic programming model to obtain the short-term water dispatch plan for the pump station units, specifically including the timed flow rate and start / stop status of the pump station units.

[0046] In this embodiment, an electronic device includes a memory and a processor. The memory stores a program that supports the processor in executing the methods described above, and the processor is configured to execute the program stored in the memory.

[0047] In this embodiment, a computer-readable storage medium stores a computer program, which is executed by a processor to perform the steps of the above method.

Claims

1. A method for optimizing the scheduling of pumping station energy efficiency considering energy consumption weights and lake regulation, characterized in that, It is applied to a water diversion system consisting of a pumping station, an open channel, and a lake, and includes the following steps: Step 1: Construct an operational model of the water diversion system consisting of water diversion pumping stations, open channels, and lakes; Step 2: Based on energy consumption weight data, construct a prediction model for medium- and long-term monthly average energy consumption weight, in order to further construct a short-term energy consumption weight scenario set and its typical energy consumption weight scenario set, and then calculate the probability of occurrence of each typical energy consumption weight scenario set based on the number of scenarios contained in each typical energy consumption weight scenario set. Step 3: Based on Step 1 and Step 2, construct a medium- and long-term water volume scheduling model and a short-term energy efficiency optimization operation stochastic programming model for the pumping station; Step 4: Using the first Monthly water diversion volume of pumping station, Monthly pump station start / stop status quantity, the first The long-term water allocation scheme of the pumping station, which consists of the water abandoned by the lunar lake, is taken as a particle. The weighted result of the weighted total energy consumption of the long-term pumping station and the penalty value obtained by equations (11)-(15) is used as the fitness of each particle. Thus, the adaptive particle swarm algorithm is used to solve the long-term water allocation model of the pumping station and obtain the planning scheme of the long-term water allocation of the pumping station. Step 5: Use nonlinear integer programming to solve the short-term energy efficiency optimization stochastic programming model of the pump station and obtain the short-term water dispatch plan for the pump station units.

2. The pump station energy efficiency optimization scheduling method considering energy consumption weight and lake regulation as described in claim 1, characterized in that, Step 1 includes: Step 1.1: Determine all feasible operating points of the pump station and record the pump station flow rate and head corresponding to all feasible operating points. Determine the feasible range of pump station flow rate and head, and then use equation (1) to obtain Units in time-period pumping stations lift With traffic The relationship between the pump station units is established, and equation (2) is used to fit the flow-head-efficiency characteristic curves of the pump station units. : (1) (2) In equations (1) and (2), , for Water levels before and after the pumping station during specific time periods; , and For the pumping station units The three correlation coefficients; for Units in time-period pumping stations Pump efficiency; Step 1.2: Determine the storage capacity between the normal water level and the highest water level of the lake as the regulating storage capacity, and use equation (3) to obtain... Remaining storage capacity during the period Thus, equation (4) is used for fitting. Remaining storage capacity during the period and Lake water level during the period Relationship curve : (3) (4) In equations (3) and (4), for The amount of water discharged from the lake during a given period; for The lake's water supply during a given period; for The amount of water discharged from the lake during a given period; for The amount of runoff from the lake during a given time period; for The amount of water diverted into the lake during a given period is the amount of water diverted by the pumping station. This is the maximum water level of the lake; for Remaining storage capacity during the period; Step 1.3: Determine the input-output relationship between the open channel, pumping station, and lake, and construct a one-dimensional steady flow model using equation (5): (5) In equation (5), , for The water level at the pumping station and the water level flowing into the lake at the end of the open channel during the specified time period. For the Manning roughness of open channels, This refers to the length of the open channel.

3. The pump station energy efficiency optimization scheduling method considering energy consumption weight and lake regulation as described in claim 2, characterized in that, Step 2 includes: Step 2.1: Determine the division of medium- and long-term scheduling periods into short-term and medium-term periods: The medium- and long-term scheduling cycle includes a total of For each month, the index set for the scheduling month is set as follows: ,use Indicates the first moon, The short-term scheduling cycle includes a total of Hours, set the index set for scheduling hours as ,use Indicates the first Hour, ; Step 2.2: Construct a prediction model for the medium- and long-term average monthly energy consumption weight using equation (6), and process the medium- and long-term average monthly energy consumption weight to obtain the prediction results for the medium- and long-term average monthly energy consumption weight: (6) In equation (6), It is a lag operator; For seasonal cycles, express of Power; , They are respectively seasonality and A non-seasonal autoregressive polynomial of order 1; , for seasonality and non-seasonal moving average polynomial of order 1. For non-seasonal models, this refers to the order of the model. The order of the seasonal model; For the first Monthly average energy consumption weighting data; For the first Random error per month; Step 2.3: Use the K-means method to cluster the hourly energy consumption weight data, thereby dividing it into... Classify energy consumption weight levels, and denote the index set of all energy consumption weight levels as ,Right now Thus, equation (7) is used to apply the first... The data of energy consumption weight levels were fitted with KDE to obtain the first... Class energy consumption weight level in the first One estimated point Estimated density function , : (7) In equation (7), For the first The smoothing bandwidth corresponding to the energy consumption weight level; For the first The total number of data points included in the energy consumption weight level; For kernel functions; For the first In the energy consumption weight level, the first Data, of which, ; Step 2.4: Utilize For the The energy consumption weight levels are resampled, and the sampling results are reduced using the Kantorovich distance method to obtain the reduced and retained sampling result set. ;in, Indicates the first element in the retained sampling result set. One sampling result; The index set for the retained sampling result set; The retained sampling results set from various energy consumption weight levels Selected from Month Parameters of energy consumption weight level : ,in, For the first The set of all energy consumption weight levels included in the month; Step 2.5: Construct the first step using equation (8) Mid-month Short-term energy consumption weighting scenario (hours) : (8) In equation (8), For the first The month corresponds to the first The time period set of class consumption weight levels; for The Middle Does an hour belong to Indicator functions; Step 2.6: Based on the prediction results of the medium- and long-term average monthly energy consumption weights, the generated short-term energy consumption weight scenarios are screened, retaining only those scenarios where the error between the short-term average energy consumption weight and the corresponding medium- and long-term prediction results does not exceed a threshold; thus, the K-medoids clustering algorithm is used to extract the first... Mid-month Typical energy consumption weighting scenarios for hours .

4. The pump station energy efficiency optimization scheduling method considering energy consumption weight and lake regulation as described in claim 3, characterized in that, Step 3 includes: Step 3.1: Construct the objective function of the medium- and long-term water allocation model for the pumping station using equations (9) and (10): (9) (10) In equations (9) and (10), Weighted total energy consumption of pumping stations in the medium and long term; For the first Monthly pump station operating energy consumption; For the first Monthly average energy consumption weighted prediction results; The density of water; It is the acceleration due to gravity; For the first The head of the monthly pumping station; For the first Monthly water diversion volume of the pumping station; For the first The start / stop status of the monthly pumping station; This refers to the average operating efficiency of the pumping station. Step 3.2: Establish the pump station water diversion constraints for the medium- and long-term water diversion model of the pump station using equation (11): (11) In equation (12), and The minimum and maximum water volumes allowed for water diversion at the pumping station; For the first Monthly water diversion volume from pumping stations; Step 3.3: Establish lake water balance constraints for the medium- and long-term water allocation model of the pumping station using equation (12): (12) In equation (12), For the first The amount of runoff from Moon Lake; For the first The amount of water diverted from Moon Lake; For the first Water supply to Moon Lake; For the first The amount of water discarded from Moon Lake; Step 3.4: Using equations (13) and (14), establish the flood season and non-flood season lake remaining storage capacity scheduling rules for the medium- and long-term water volume scheduling model of the pumping station: (13) (14) In equations (13) and (14), and The remaining storage capacity of the largest lake and the remaining storage capacity of the smallest lake. For the first The remaining storage capacity of lakes during the flood season month This is a set of indices for the flood season months; For the first The remaining storage capacity of lakes during the non-flood season month This is a set of indexes for non-flood season months. It is the last month of the non-flood season; Step 3.5: Establish the flood season and non-flood season lake water discharge rules for the medium- and long-term water volume scheduling model of the pumping station using equation (15): (15) In equation (15), For the first The monthly water discharge volume of lakes during the flood season; For the first The amount of water discharged from lakes during non-flood season months; Step 3.6: Construct the objective function of the short-term pump station energy efficiency optimization stochastic programming model using equations (16) and (17): (16) (17) In equations (16) and (17), The weighted total energy consumption for short-term expected operation of the pumping station; For the first Total number of short-term periods within the month; For the first A set of indexes for typical energy consumption weighting scenarios within the month; For the first Typical energy consumption weighting scenarios within the month The probability of occurrence; For the first Typical energy consumption weighting scenarios within the month Weighted total energy consumption of the pumping station during short-term operation; For unit assembly; For the first Typical energy consumption weighting scenarios within the month Lower unit In the Hourly overcurrent flow rate; For the first Typical energy consumption weighting scenarios within the month Lower unit In the Start / stop status for each hour; For the first Typical energy consumption weighting scenarios within the month Next Pump station head per hour; Hour length; For the first Typical energy consumption weighting scenarios within the month Lower unit In the Hourly operating efficiency; Power consumption during startup; For the first Typical monthly energy consumption weighting scenarios Lower unit In the The power-on process for hours; Step 3.7: Using Equation (18), establish rigid constraints for the pump station water diversion planning scheme of the short-term pump station energy efficiency optimization operation stochastic programming model: (18) Step 3.8: Establish the overcurrent capacity constraint of the pump station unit using equation (19): (19) In equation (19), and These are the minimum and maximum flow rates for the pump station unit. Step 3.9: Establish the lake's short-term remaining storage capacity scheduling rules for the short-term pumping station energy efficiency optimization operation stochastic programming model using equations (20) and (21): (20) (21) In equations (20) and (21), For the first Typical energy consumption weighting scenarios within the month Next The remaining storage capacity of the lake after 24 hours; For the first Typical energy consumption weighting scenarios during the flood season month Next The remaining storage capacity of the lake after 24 hours; For the first An index set of energy consumption weighting scenarios for the flood season month; For the first Typical energy consumption weighting scenarios within the month Next The remaining storage capacity of the lake after 24 hours; For the last hour of a short-term scheduling period; Step 3.10: Establish the open channel water conveyance capacity constraint for the short-term pump station energy efficiency optimization operation stochastic programming model using equation (22): (22) In equation (22), and Minimum and maximum flow rates for water conveyance in open channels; Step 3.11: Establish the minimum continuous operating time constraint of the pump station unit for the short-term pump station energy efficiency optimization operation stochastic programming model using equations (23) and (24): (23) (24) In equations (23) and (24), For the first Typical energy consumption weighting scenarios within the month Lower unit In the Start / stop status for each hour; For the first Typical energy consumption weighting scenarios within the month Lower unit In the Start / stop status for each hour; This refers to the number of hours the pump station unit has been running since startup. This refers to the minimum continuous operating time of the pump station unit.

5. The pump station energy efficiency optimization scheduling method considering energy consumption weight and lake regulation as described in claim 4, characterized in that, Step 5 includes: Step 5.1: Use the SOS2 method to transform equation (2) into equation (25) - equation (27): (25) (26) (27) In equations (25)-(27), For the index of discrete segmentation points; A set of indices for discrete segmentation points; For the first Flow points at discrete segmentation points; for Units in time-period pumping stations In the Non-negative weighted variables at discrete segmentation points, and ; Step 5.2: Relax equation (18) to obtain equation (28): (28) In equation (28), The error coefficient; Step 5.3: Use a solver to solve the transformed short-term pump station energy efficiency optimization stochastic programming model to obtain the short-term water dispatch plan for the pump station units.

6. An electronic device, comprising a memory and a processor, characterized in that, The memory is used to store a program that supports a processor in executing the method of any one of claims 1-5, the processor being configured to execute the program stored in the memory.

7. A computer-readable storage medium storing a computer program thereon, characterized in that, The computer program is executed by the processor to perform the steps of the method according to any one of claims 1-5.

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