Energy unit consumption optimization method and device

Abstract

The present invention provides a method and device for optimizing the consumption of energy units, the method comprising: using the Monte Carlo method to simulate the various output historical data of the energy units to be consumed and optimized, generating various output scenarios of the energy units, and clustering the various output scenarios of the energy units through a clustering algorithm to obtain various typical output scenarios of the energy units; constructing a pumped storage optimization model of the energy units according to the various typical output scenarios of the energy units, the operation information of the various pumped storage power stations corresponding to the energy units, and the operation information of the power grid where the energy units are located; solving the pumped storage optimization model through a simulation platform to obtain the operating status results of each pumped storage power station and the consumption results of the energy units. The application of this method can effectively improve the optimization efficiency, reduce the wind abandonment rate and the solar abandonment rate, and achieve the maximum consumption of energy in various typical scenarios.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a method and device for optimizing the consumption of energy units. Background Art

[0002] Modern life is increasingly inseparable from electricity. Common technologies mainly use thermal, hydro, wind and other methods to generate electricity. With the improvement of environmental protection awareness, people are more and more inclined to use renewable clean energy for power generation. In order to reduce greenhouse gas emissions, protect the ecological environment, and achieve the goal of "carbon peak and carbon neutrality", the proportion of renewable energy power generation represented by wind power generation and photovoltaic power generation in the power system will increase in the future.

[0003] However, renewable energy generation is uncertain and easily affected by environmental factors. The electricity generated cannot be effectively consumed by the power grid, resulting in absorption problems. Currently, flexible resources such as energy storage and pumped storage can be used to solve the absorption problem of renewable energy. However, the current pumped storage absorption method is inefficient and difficult to achieve the optimal absorption of renewable energy. Summary of the invention

[0004] In view of this, the present invention provides a method for optimizing the consumption of energy units, through which the optimization efficiency can be effectively improved and the maximum consumption of energy can be achieved in various typical scenarios.

[0005] The present invention also provides an energy unit consumption optimization device to ensure the implementation and application of the above method in practice.

[0006] A method for optimizing the consumption of energy units, comprising:

[0007] Obtaining the historical output data of each energy unit to be optimized, the operation information of each pumped-storage power station corresponding to the energy unit, and the operation information of the power grid where the energy unit is located;

[0008] Using the Monte Carlo method to simulate various historical output data of the energy unit to generate various output scenarios of the energy unit;

[0009] Clustering various output scenarios of the energy unit through a clustering algorithm to obtain various typical output scenarios of the energy unit;

[0010] Constructing a pumped storage optimization model of the energy unit according to each typical output scenario of the energy unit, the operation information of each pumped storage power station and the operation information of the power grid;

[0011] The pumped-storage optimization model is solved through a preset simulation platform to obtain the operating status results of each pumped-storage power station and the consumption results of the energy units.

[0012] In the above method, optionally, the Monte Carlo method is used to simulate various output historical data of the energy unit to generate various output scenarios of the energy unit, including:

[0013] Collecting the output data of the energy unit at each time point within a plurality of preset time periods to obtain each output historical data within each of the time periods;

[0014] According to each of the output history data, an output scenario of the energy source in each of the time periods is generated, and the output scenario is a sequence formed by sorting each of the output history data in the time period in chronological order.

[0015] In the above method, optionally, clustering the various output scenarios of the energy unit by a clustering algorithm to obtain various typical output scenarios of the energy unit includes:

[0016] Determine the number K of typical output scenarios of the energy unit, where K is a positive integer;

[0017] Randomly select K output scenarios from each output scenario of the energy unit as the central scenario, and perform a scenario clustering operation according to each of the central scenarios;

[0018] The scene clustering operation includes: calculating the scene distance from each output scene to each central scene according to a preset distance calculation formula; clustering each output scene according to the clustering condition with the closest scene distance according to each scene distance to obtain each cluster, and taking each cluster center as a new central scene;

[0019] If each of the central scenes does not meet the preset convergence conditions, the scene clustering operation is re-executed according to each of the central scenes until each of the central scenes obtained meets the convergence conditions, and each of the central scenes is determined to be a typical output scene of the energy unit.

[0020] The above method, optionally, constructing a pumped storage optimization model of the energy unit according to each typical output scenario of the energy unit, the operation information of each pumped storage power station and the operation information of the power grid, comprises:

[0021] According to each output data of each output scenario of the energy unit, an objective function of the pumped storage optimization model is obtained;

[0022] According to the operation information of each pumped-storage power station, an operation constraint expression of each pumped-storage power station is obtained;

[0023] According to the operation information of the power grid, the operation constraint expressions of each power grid network are obtained;

[0024] Based on the objective function, the operation constraint expressions of each pumped-storage power station and the operation constraint expressions of each power grid network, a pumped-storage optimization model of the energy unit is constructed.

[0025] The above method, optionally, solves the pumped storage optimization model through a preset simulation platform to obtain the operating status results of each pumped storage power station and the consumption results of the energy unit, including:

[0026] The pumped-storage optimization model is input into the MATLAB simulation platform, so that the MATLAB simulation platform solves the pumped-storage optimization model through the CPLEX toolbox, and outputs the operating status results of each pumped-storage power station and the consumption results of the energy units.

[0027] An energy unit consumption optimization device, comprising:

[0028] A data acquisition unit, used to acquire the output historical data of each energy unit to be optimized, the operation information of each pumped storage power station corresponding to the energy unit, and the operation information of the power grid where the energy unit is located;

[0029] A scenario generating unit, used for simulating various output historical data of the energy unit by using the Monte Carlo method to generate various output scenarios of the energy unit;

[0030] A scene clustering unit, used for clustering various output scenes of the energy unit through a clustering algorithm to obtain various typical output scenes of the energy unit;

[0031] A model building unit, used to build a pumped storage optimization model of the energy unit according to each typical output scenario of the energy unit, the operation information of each pumped storage power station and the operation information of the power grid;

[0032] The model solving unit is used to solve the pumped storage optimization model through a preset simulation platform to obtain the operating status results of each pumped storage power station and the consumption results of the energy units.

[0033] In the above device, optionally, the scene generation unit includes:

[0034] A collection subunit, used to collect the output data of the energy unit at each time point within a plurality of preset time periods, and obtain each output historical data within each of the time periods;

[0035] A generating subunit is used to generate an output scenario of the energy source in each of the time periods according to each of the output history data, wherein the output scenario is a sequence formed by sorting the output history data in the time period in chronological order.

[0036] In the above device, optionally, the scene clustering unit includes:

[0037] A determination subunit is used to determine the number K of typical output scenarios of the energy unit, where K is a positive integer;

[0038] A clustering subunit, used for randomly selecting K output scenarios from various output scenarios of the energy unit as central scenarios, and performing a scenario clustering operation according to each of the central scenarios;

[0039] The scene clustering operation includes: calculating the scene distance from each output scene to each central scene according to a preset distance calculation formula; clustering each output scene according to the clustering condition with the closest scene distance according to each scene distance to obtain each cluster, and taking each cluster center as a new central scene;

[0040] If each of the central scenes does not meet the preset convergence conditions, the scene clustering operation is re-executed according to each of the central scenes until each of the central scenes obtained meets the convergence conditions, and each of the central scenes is determined to be a typical output scene of the energy unit.

[0041] In the above device, optionally, the model building unit comprises:

[0042] A first execution subunit is used to obtain the objective function of the pumped storage optimization model according to each output data in each output scenario of the energy unit;

[0043] A second execution subunit is used to obtain an operation constraint expression of each pumped-storage power station according to the operation information of each pumped-storage power station;

[0044] A third execution subunit, configured to obtain operation constraint expressions of each power grid network according to the operation information of the power grid;

[0045] The model building subunit is used to build the pumped storage optimization model of the energy unit based on the objective function, the operation constraint expressions of each pumped storage power station and the operation constraint expressions of each power grid network.

[0046] In the above device, optionally, the model solving unit is specifically used for:

[0047] The pumped-storage optimization model is input into the MATLAB simulation platform, so that the MATLAB simulation platform solves the pumped-storage optimization model through the CPLEX toolbox, and outputs the operating status results of each pumped-storage power station and the consumption results of the energy units.

[0048] A storage medium includes stored instructions, wherein when the instructions are executed, the device where the storage medium is located is controlled to execute the above-mentioned energy unit consumption optimization method.

[0049] An electronic device includes a memory and one or more instructions, wherein the one or more instructions are stored in the memory and are configured to be executed by one or more processors to implement the above-mentioned energy unit consumption optimization method.

[0050] Compared with the prior art, the present invention has the following advantages:

[0051] Based on the embodiment provided by the present invention, in the process of optimizing the consumption of energy units, each output historical data of the energy units to be optimized, the operation information of each pumped-storage power station corresponding to the energy units, and the operation information of the power grid where the energy units are located are obtained; the Monte Carlo method is used to simulate the various output historical data of the energy units to generate various output scenarios of the energy units, and the various output scenarios of the energy units are clustered through a clustering algorithm to obtain various typical output scenarios of the energy units; according to the various typical output scenarios of the energy units, the operation information of each pumped-storage power station and the operation information of the power grid, a pumped-storage optimization model of the energy units is constructed; the pumped-storage optimization model is solved through a simulation platform to obtain the operating status results of each pumped-storage power station and the consumption results of the energy units.

[0052] By applying the embodiments provided by the present invention, the number of output scenarios considered in the optimization is reduced through a clustering method, and the calculation complexity of the optimization method is reduced, which can effectively improve the optimization efficiency. In addition, the operation constraints of the pumped-storage power station and the operation constraints of the power grid are considered when absorbing the energy units, and the operation state of the pumped-storage power station is effectively optimized for energy absorption, reducing the wind abandonment rate and the solar abandonment rate, and realizing the maximum absorption of energy in various typical scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0054] Figure 1 A method flow chart of a method for optimizing the consumption of an energy unit provided by an embodiment of the present invention;

[0055] Figure 2 Another method flow chart of a method for optimizing the consumption of an energy unit provided by an embodiment of the present invention;

[0056] Figure 3 A flowchart of another method for optimizing the consumption of an energy unit provided by an embodiment of the present invention;

[0057] Figure 4 A 30-test-node power grid structure diagram of a method for optimizing the consumption of energy units provided in an embodiment of the present invention;

[0058] Figure 5 A schematic diagram of a renewable energy output scenario of an energy unit consumption optimization method provided by an embodiment of the present invention;

[0059] Figure 6 An operating state diagram of a pumped storage power station for an energy unit consumption optimization method provided by an embodiment of the present invention;

[0060] Figure 7 A schematic diagram of renewable energy consumption in a method for optimizing consumption of an energy unit provided in an embodiment of the present invention;

[0061] Figure 8 A device structure diagram of a device for optimising the consumption of energy units provided by an embodiment of the present invention;

[0062] Fig. 9 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0063] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0064] In this application, relational terms such as first and second, etc. are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. The terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of more restrictions, the elements defined by the sentence "comprise one..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0065] The present invention can be used in many general or special computing device environments or configurations, such as personal computers, server computers, handheld or portable devices, tablet devices, multi-processor devices, distributed computing environments including any of the above devices or devices, etc.

[0066] An embodiment of the present invention provides a method for optimizing the consumption of an energy unit. The method can be applied to a variety of system platforms, and its execution subject can be a processor of a computer terminal or various mobile devices.

[0067] Optionally, in an embodiment of the present invention, the consumption optimization method of the energy unit may be an optimization method for consuming renewable energy in a pumped-storage power station.

[0068] The method flow chart of the energy unit consumption optimization method is as follows Figure 1 As shown, specifically including:

[0069] S101: Acquire the output historical data of each energy unit to be optimized, the operation information of each pumped-storage power station corresponding to the energy unit, and the operation information of the power grid where the energy unit is located.

[0070] In the embodiment provided by the present invention, the energy units to be optimized for consumption can be renewable energy generating units such as wind power and photovoltaic power generation units. In order to solve the phenomenon of wind abandonment and solar abandonment that will occur after the renewable energy units are connected to the power grid, it is necessary to optimize the consumption of renewable energy by the pumped-storage power station. Therefore, in the process of optimizing the consumption of renewable energy by the pumped-storage power station, it is necessary to obtain the output historical data of each energy unit to be optimized for consumption, the operation information of each pumped-storage power station corresponding to the energy unit, and the operation information of the power grid where the energy unit is located.

[0071] S102: Using the Monte Carlo method to simulate various historical output data of the energy unit to generate various output scenarios of the energy unit.

[0072] Based on the obtained historical output data of multiple renewable energy sources such as wind power and photovoltaic power, the output characteristics of the historical output data of renewable energy units are analyzed, and the Monte Carlo simulation method is used to simulate and generate massive uncertain output scenarios of renewable energy output.

[0073] Among them, the Monte Carlo method, also known as statistical simulation method and random sampling technology, is a random simulation method, a calculation method based on probability and statistical theory methods, and a method of using random numbers or pseudo-random numbers to solve calculation problems.

[0074] In the embodiment provided by the present invention, optionally, the Monte Carlo method is used to simulate various output historical data of the energy unit to generate various output scenarios of the energy unit, including:

[0075] Collecting the output data of the energy unit at each time point within a plurality of preset time periods to obtain each output historical data within each of the time periods;

[0076] According to each of the output history data, an output scenario of the energy source in each of the time periods is generated, and the output scenario is a sequence formed by sorting each of the output history data in the time period in chronological order.

[0077] Specifically, based on the statistics of the 24-hour output data of renewable energy units in different seasons, the output of renewable energy is sampled in sequence to generate multiple scenes containing 24-hour renewable energy output sequences. Among them, sampling in sequence means sampling according to the time series. In 24 hours a day, there are 24 output data of renewable energy, one for each hour, for example, one data at 01:00 and one data at 02:00. The output data at each time point in 24 hours are sorted in chronological order to obtain an output scene of the renewable energy unit.

[0078] By applying the embodiment provided by the present invention, the output data of each time point in each of multiple 24 hours is simulated by the Monte Carlo method to obtain various output scenarios, which can simply and intuitively reflect the output status and change trend of the renewable energy units in each 24 hours.

[0079] S103: Clustering the various output scenarios of the energy units through a clustering algorithm to obtain various typical output scenarios of the energy units.

[0080] In an embodiment of the present invention, multiple uncertain output scenarios of the renewable energy group are generated by simulating the output power of the renewable energy group in each time period, and the multiple output scenarios of the renewable energy group are reduced by the kmeans clustering algorithm (k-means clustering algorithm), so as to generate various typical output scenarios of the renewable energy group.

[0081] S104: Constructing a pumped-storage optimization model of the energy unit according to each typical output scenario of the energy unit, the operation information of each pumped-storage power station and the operation information of the power grid.

[0082] In an embodiment of the present invention, based on various typical output scenarios of the renewable energy units after reduction, the operation constraints of the pumped-storage power station and the power grid network are considered, and based on the acquired operation information of the power grid where the renewable energy units are located, as well as the operation information of each pumped-storage power station corresponding to the renewable energy units in the power grid, a pumped-storage optimization model for maximizing the absorption of renewable energy is constructed.

[0083] S105: Solving the pumped-storage optimization model through a preset simulation platform to obtain the operating status results of each pumped-storage power station and the consumption results of the energy units.

[0084] In the embodiment of the present invention, the pumped storage optimization model is calculated and solved through the simulation platform to obtain the operating status results of each pumped storage power station corresponding to the renewable energy unit and the consumption results of the renewable energy unit, thereby completing the consumption optimization of the renewable energy unit.

[0085] Based on the embodiment provided by the present invention, in the process of optimizing the consumption of energy units, each output historical data of the energy units to be optimized, the operation information of each pumped-storage power station corresponding to the energy units, and the operation information of the power grid where the energy units are located are obtained; the Monte Carlo method is used to simulate the various output historical data of the energy units to generate various output scenarios of the energy units; the various output scenarios of the energy units are clustered by a clustering algorithm to obtain various typical output scenarios of the energy units; according to the various typical output scenarios of the energy units, the operation information of each pumped-storage power station and the operation information of the power grid, a pumped-storage optimization model of the energy units is constructed; the pumped-storage optimization model is solved by a preset simulation platform to obtain the operating status results of each pumped-storage power station and the consumption results of the energy units.

[0086] By applying the embodiment provided by the present invention, the output characteristics of the energy unit are analyzed using the Monte Carlo method, and various uncertain output scenarios of the energy unit are generated. Then, the number of output scenarios considered in the optimization is reduced according to the clustering method, and the calculation complexity of the optimization method is reduced, which can effectively improve the optimization efficiency. When constructing the pumped-storage optimization model of the energy unit, the operating constraints of the pumped-storage power station and the operating constraints of the power grid are considered, and the operating state of the pumped-storage power station is effectively optimized for energy consumption, which helps to reduce the wind abandonment rate and the solar abandonment rate, and realize the maximum energy consumption in various typical scenarios.

[0087] In the embodiment of the present invention, Figure 2 As shown, optionally, clustering the various output scenarios of the energy unit by a clustering algorithm to obtain various typical output scenarios of the energy unit includes:

[0088] S201: Determine the number K of typical output scenarios of the energy unit.

[0089] Wherein, K is a positive integer.

[0090] Set the number of typical scenes k obtained after target reduction as the number of kmeans target clustering clusters.

[0091] S202: Randomly select K output scenarios from the various output scenarios of the energy unit as central scenarios.

[0092] Randomly select k output scenarios {C1, C2, C3, ..., C k} as the central scene of clustering to start clustering operations based on each central scene.

[0093] S203: Calculate the scene distance from each of the output scenes to each of the central scenes according to a preset distance calculation formula.

[0094] Specifically, through the distance calculation formula Calculate the scene distance from each output scene to each center scene. In the distance calculation formula, dis(X i ,C j ) is scene X i To the center scene C j The distance of scene X i Data dimension, scenario X i The data dimension is equal to scene C j The data dimension.

[0095] S204: Clustering the output scenes according to the scene distances and the clustering condition with the shortest scene distance to obtain clusters, and taking the centers of the clusters as new center scenes.

[0096] S205: Determine whether each of the central scenes meets a preset convergence condition.

[0097] After calculating the distance from each output scene to the central scene, cluster them according to the central scene with the closest distance to obtain the clusters, and find the center of each cluster as the new central scene.

[0098] If each of the central scenes does not satisfy the preset convergence condition, S203 is executed, and the scene clustering operation is re-executed according to each of the central scenes repeatedly until each of the central scenes satisfies the convergence condition, and S206 is executed.

[0099] S206: Determine each of the central scenes as a typical output scene of the energy unit.

[0100] When the changes of each central scene meet the convergence condition, each central scene obtained after the final clustering is used as the typical output scene of the renewable energy unit.

[0101] By applying the embodiment provided by the present invention, the kmeans clustering method is used to reduce the various output scenarios of renewable energy units generated by Monte Carlo simulation. While taking into account the uncertainty of renewable energy output, the number of output scenarios considered in the optimization is reduced, thereby reducing the calculation complexity of the optimization method.

[0102] In the embodiment of the present invention, Figure 3 As shown, optionally, the pumped storage optimization model of the energy unit is constructed according to each typical output scenario of the energy unit, the operation information of each pumped storage power station and the operation information of the power grid, including:

[0103] S301: Obtaining the objective function of the pumped storage optimization model according to the output data of each output scenario of the energy unit.

[0104] Specifically, the units involved in the embodiments of the present invention include three types: pumped storage units, thermal power units, and renewable energy units. According to the online power of the renewable energy generator units in each period, the objective function of the pumped storage optimization model is to maximize the online power of the renewable energy units in the output scenario. The objective function is specifically as described in formula (1):

[0105]

[0106] Formula (1), T is the set of all time periods under each output scenario, P g,n (t) is the grid-connected power of the nth renewable energy generator set in the tth period, and NG is the set of renewable energy generator sets.

[0107] It should be noted that the maximum value of the 24-hour online power of the renewable energy unit obtained by solving formula (1) is regarded as the maximum energy consumption of the renewable energy unit in the output scenario of the 24-hour period.

[0108] S302: Obtaining operation constraint expressions of each pumped-storage power station according to the operation information of each pumped-storage power station.

[0109] Specifically, based on the operating information of each pumped-storage power station, such as output power, water storage capacity, power generation efficiency, etc., expressions of the pumped-storage power station operating constraints, such as output constraint, storage capacity constraint and climbing constraint, are obtained.

[0110] The output constraint expression is shown in formula (2):

[0111]

[0112] In formula (2), P H (t) is the output power of the pumped storage power station in the tth period, P H (t)>0 represents the state of hydropower generation, P H (t)<0 represents the state of electricity consumption for pumping water, and They are the rated pumping capacity and rated generating capacity of the pumped-storage power station respectively.

[0113] The storage capacity constraint expressions are shown in formulas (3), (4), (5), (6) and (7):

[0114] S UR (t+1)=S UR (t)+c g ·P H (t)·Δt g -c p ·P H (t)·Δt p , (3),

[0115] S LR (t+1)=S LR (t)-c g ·P H (t)·Δt g +c p ·P H (t)·Δt p , (4),

[0116]

[0117]

[0118] Δt g +Δt p =1, (7),

[0119] In the above storage capacity constraint expression, S UR (t+1) and S LR (t+1) represents the water storage capacity of the upper and lower reservoirs of the pumped storage power station during the period t+1; S UR (t) and S LR (t) represents the water storage capacity of the upper and lower reservoirs of the pumped storage power station during period t; c g and c p They represent the pumping efficiency and power generation efficiency of the pumped storage power station, Δt g and Δt p and represent the pumping time and power generation time of the pumped storage power station in each period respectively; Δt g and Δt p are 0-1 variables, representing the pumping duration status and power generation duration status respectively; and S UR They are the upper and lower limits of the water storage capacity of the upper reservoir of the pumped-storage power station; and S LR They are respectively the upper and lower limits of the water storage capacity of the lower reservoir of a pumped-storage power station.

[0120] The climbing constraint expression is shown in formula (8):

[0121] 0≤|P H (t+1)-P H (t)|≤ΔP max , (8),

[0122] In formula (8), P H (t) represents the output power of the pumped storage power station in the tth period, P H (t+1) represents the output power of the pumped storage power station from the t+1th period, ΔP max It is the maximum climbing performance of the pumped storage power station.

[0123] S303: Obtaining network operation constraint expressions of each power grid according to the operation information of the power grid.

[0124] Specifically, according to the operating information of each node in the power grid in each time period, such as active load, voltage phase, transmission capacity and output power of thermal power units, the expressions of power grid network operation constraints such as node energy balance constraints, transmission line capacity constraints, renewable energy output constraints and thermal power unit output constraints are obtained.

[0125] The node energy balance constraint expression is shown in formula (9):

[0126]

[0127] In formula (9), P g,n (t) is the grid-connected power of the nth renewable energy unit in the tth period, NG i represents the set of renewable energy units at node i, P i,d (t) is the active load of node i in the tth period, P i,F (t) is the power generation of the thermal power unit at node i in the tth period, P i,H (t) is the power generation of the pumped storage power station at node i in the tth period, j∈i represents the set of all nodes connected to node i (where node j is one of the nodes in the set), B ij is the imaginary part of the admittance between nodes i and j; θ i (t) is the voltage phase of node i in the tth period, θ j (t) is the voltage phase of node j in the tth period, and NI is the set of grid nodes.

[0128] The transmission line capacity constraint expression is shown in formula (10):

[0129]

[0130] In formula (10), represents the maximum transmission capacity of the transmission line between node i and node j.

[0131] The renewable energy output constraint expression is shown in formula (11):

[0132]

[0133] In formula (11), It is the theoretical maximum grid-connected power of the nth renewable energy unit in the tth period, that is, the output value when there is no wind or solar power abandonment.

[0134] The output constraint expression of thermal power unit is shown in formula (12):

[0135]

[0136] In formula (12), It is the maximum allowable output of the thermal power unit.

[0137] S304: Constructing a pumped-storage optimization model of the energy unit based on the objective function, the operation constraint expressions of each pumped-storage power station and the operation constraint expressions of each power grid network.

[0138] By applying the embodiments provided by the present invention, when constructing the objective function of maximizing the grid-connected power of renewable energy units, the operating constraints of the pumped-storage power station and the operating constraints of the power grid are taken into consideration, which can effectively optimize the operating state of the pumped-storage power station to absorb renewable energy, help reduce the wind and solar power abandonment rates, and promote the large-scale development of renewable energy.

[0139] In the embodiment of the present invention, optionally, solving the pumped storage optimization model through a preset simulation platform to obtain the operating status results of each pumped storage power station and the consumption results of the energy unit include:

[0140] The pumped-storage optimization model is input into the MATLAB simulation platform, so that the MATLAB simulation platform solves the pumped-storage optimization model through the CPLEX toolbox, and outputs the operating status results of each pumped-storage power station and the consumption results of the energy units.

[0141] The embodiments provided by the present invention are as follows Figure 4 The 30 test nodes of the power grid are tested, and the three pumped storage power stations are respectively located at nodes 2, 13 and 14. The pumped storage optimization model is solved by MATLAB platform and CPLEX toolbox. Figure 5 The typical output scenarios obtained after the reduction in power consumption and the pumped storage optimization model considering the operation constraints of the pumped storage power station and the power grid network are obtained as follows: Figure 6 The operating status of each pumped storage power station shown in Figure 7 The consumption of renewable energy units is shown in Figure 2. Figure 6 , Figure 7 It can be found that renewable energy is basically absorbed: when the output of renewable energy is large, the overall output of the three pumped-storage power stations is in the pumping state; when the output of renewable energy is small, the overall output of the three pumped-storage power stations is in the generating state.

[0142] It should be noted that MATLAB is a commercial mathematical software produced by MathWorks in the United States, which is used in data analysis, wireless communications, deep learning, image processing and computer vision, signal processing, quantitative finance and risk management, robotics, control systems and other fields. CPLEX is a mathematical optimization technology. Using CPLEX, complex business problems can be expressed as mathematical programming models, and solutions to the models can be quickly found through advanced optimization algorithms.

[0143] It should also be noted that Figure 4 In the 30-test-node power grid structure diagram shown, each serial number is the node serial number, the arrow represents the load, which refers to the power outflow, H refers to the pumped-storage power station, G represents the thermal power unit, and W refers to the renewable energy unit.

[0144] Figure 5 In the schematic diagram of renewable energy output scenarios shown, the horizontal axis is the time period, and the vertical axis is the output power of renewable energy at different time points in each time period. The output power of each time period constitutes multiple uncertain output scenario curves. The kmeans clustering method is used to reduce each output scenario to obtain two typical output scenarios of renewable energy units.

[0145] Figure 6 In the operating status diagram of each pumped-storage power station shown, the horizontal axis is the time period, and the vertical axis is the power of the pumped-storage power station. A positive value on the vertical axis represents the generating power of the pumped-storage power station, and a negative value represents the pumping power of the pumped-storage power station. The difference in positive and negative power values ​​is related to the distribution location of each pumped-storage power station, the storage capacity of the pumped reservoir, and the rated pumping power parameters.

[0146] Figure 7 The renewable energy unit absorption situation diagram shown in the figure has the horizontal axis as the time period and the vertical axis as the renewable energy unit absorption capacity. The renewable energy unit with the largest absorption capacity is regarded as having the largest grid-connected power.

[0147] By solving the pumped storage optimization model through the MATLAB simulation platform, it is possible to obtain intuitive operating status results of the pumped storage power station and the consumption results of the energy units.

[0148] The specific implementation processes and derivative methods of the above-mentioned embodiments are all within the protection scope of the present invention.

[0149] and Figure 1 Corresponding to the method described above, the embodiment of the present invention also provides an energy unit consumption optimization device for Figure 1 In the specific implementation of the method, the energy unit consumption optimization device provided in the embodiment of the present invention can be applied to a computer terminal or various mobile devices, and its structural schematic diagram is as follows Figure 8As shown, specifically including:

[0150] The data acquisition unit 801 is used to acquire the output historical data of each energy unit to be optimized, the operation information of each pumped storage power station corresponding to the energy unit, and the operation information of the power grid where the energy unit is located;

[0151] A scenario generating unit 802 is used to simulate various output historical data of the energy unit using the Monte Carlo method to generate various output scenarios of the energy unit;

[0152] A scene clustering unit 803 is used to cluster the various output scenes of the energy unit through a clustering algorithm to obtain various typical output scenes of the energy unit;

[0153] A model building unit 804 is used to build a pumped storage optimization model of the energy unit according to each typical output scenario of the energy unit, the operation information of each pumped storage power station and the operation information of the power grid;

[0154] The model solving unit 805 is used to solve the pumped storage optimization model through a preset simulation platform to obtain the operating status results of each pumped storage power station and the consumption results of the energy units.

[0155] The energy unit consumption optimization device provided by the embodiment of the present invention obtains the various output historical data of the energy unit to be consumed and optimized, the operating information of each pumped-storage power station corresponding to the energy unit, and the operating information of the power grid where the energy unit is located through a data acquisition unit; then the scenario generation unit uses the Monte Carlo method to simulate the various output historical data of the energy unit to generate various output scenarios of the energy unit; then the scenario clustering unit clusters the various output scenarios of the energy unit through a clustering algorithm to obtain various typical output scenarios of the energy unit; then the model construction unit constructs a pumped-storage optimization model of the energy unit according to the various typical output scenarios of the energy unit, the operating information of each pumped-storage power station and the operating information of the power grid; finally, the model solving unit solves the pumped-storage optimization model through a simulation platform to obtain the operating status results of each pumped-storage power station and the consumption results of the energy unit.

[0156] By applying the device provided in the embodiment of the present invention, the number of output scenarios considered in the optimization is reduced through a clustering method, thereby reducing the computational complexity of the optimization method, which can effectively improve the optimization efficiency. In addition, when constructing a pumped-storage optimization model for energy units, the operating constraints of the pumped-storage power station and the operating constraints of the power grid are considered, and the operating state of the pumped-storage power station is effectively optimized for energy consumption, which helps to reduce the wind abandonment rate and the solar abandonment rate and achieve maximum energy consumption in various typical scenarios.

[0157] In the above device, optionally, the scene generation unit 802 includes:

[0158] A collection subunit, used to collect the output data of the energy unit at each time point within a plurality of preset time periods, and obtain each output historical data within each of the time periods;

[0159] A generating subunit is used to generate an output scenario of the energy source in each of the time periods according to each of the output history data, wherein the output scenario is a sequence formed by sorting the output history data in the time period in chronological order.

[0160] In the above device, optionally, the scene clustering unit 803 includes:

[0161] A determination subunit is used to determine the number K of typical output scenarios of the energy unit, where K is a positive integer;

[0162] A clustering subunit, used for randomly selecting K output scenarios from various output scenarios of the energy unit as central scenarios, and performing a scenario clustering operation according to each of the central scenarios;

[0163] The scene clustering operation includes: calculating the scene distance from each output scene to each central scene according to a preset distance calculation formula; clustering each output scene according to the clustering condition with the closest scene distance according to each scene distance to obtain each cluster, and taking each cluster center as a new central scene;

[0164] If each of the central scenes does not meet the preset convergence conditions, the scene clustering operation is re-executed according to each of the central scenes until each of the central scenes obtained meets the convergence conditions, and each of the central scenes is determined to be a typical output scene of the energy unit.

[0165] In the above device, optionally, the model building unit 804 includes:

[0166] A first execution subunit is used to obtain the objective function of the pumped storage optimization model according to each output data in each output scenario of the energy unit;

[0167] A second execution subunit is used to obtain an operation constraint expression of each pumped-storage power station according to the operation information of each pumped-storage power station;

[0168] A third execution subunit, configured to obtain operation constraint expressions of each power grid network according to the operation information of the power grid;

[0169] The model building subunit is used to build the pumped storage optimization model of the energy unit based on the objective function, the operation constraint expressions of each pumped storage power station and the operation constraint expressions of each power grid network.

[0170] In the above device, optionally, the model solving unit 805 is specifically used for:

[0171] The pumped-storage optimization model is input into the MATLAB simulation platform, so that the MATLAB simulation platform solves the pumped-storage optimization model through the CPLEX toolbox, and outputs the operating status results of each pumped-storage power station and the consumption results of the energy units.

[0172] The specific working process of each unit and sub-unit in the energy unit consumption optimization device disclosed in the above embodiment of the present invention can be referred to the corresponding content in the energy unit consumption optimization method disclosed in the above embodiment of the present invention, and will not be repeated here.

[0173] An embodiment of the present invention further provides a storage medium, which includes stored instructions, wherein when the instructions are executed, the device where the storage medium is located is controlled to execute the above-mentioned energy unit consumption optimization method.

[0174] The embodiment of the present invention further provides an electronic device, the structural diagram of which is shown in FIG. Fig. 9 As shown, it specifically includes a memory 901 and one or more instructions 902, wherein the one or more instructions 902 are stored in the memory 901 and are configured to be executed by one or more processors 903 to perform the following operations:

[0175] Obtaining the historical output data of each energy unit to be optimized, the operation information of each pumped-storage power station corresponding to the energy unit, and the operation information of the power grid where the energy unit is located;

[0176] Using the Monte Carlo method to simulate various historical output data of the energy unit to generate various output scenarios of the energy unit;

[0177] Clustering various output scenarios of the energy unit through a clustering algorithm to obtain various typical output scenarios of the energy unit;

[0178] Constructing a pumped storage optimization model of the energy unit according to each typical output scenario of the energy unit, the operation information of each pumped storage power station and the operation information of the power grid;

[0179] The pumped-storage optimization model is solved through a preset simulation platform to obtain the operating status results of each pumped-storage power station and the consumption results of the energy units.

[0180] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can refer to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system or system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can refer to the partial description of the method embodiment. The system and system embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without creative work.

[0181] Those skilled in the art may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, computer software, or a combination of both.

[0182] In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the components and steps of each example according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0183] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for optimizing the consumption of energy units, characterized in that: include: Obtaining the historical output data of each energy unit to be optimized, the operation information of each pumped-storage power station corresponding to the energy unit, and the operation information of the power grid where the energy unit is located; Using the Monte Carlo method to simulate various output historical data of the energy unit to generate various output scenarios of the energy unit, including: collecting the output data of the energy unit at various time points in multiple preset time periods to obtain various output historical data in each of the time periods; generating an output scenario of the energy in each of the time periods according to each of the output historical data, wherein the output scenario is a sequence formed by sorting the various output historical data in the time period in chronological order; The various output scenes of the energy group are clustered by a clustering algorithm to obtain various typical output scenes of the energy group, including: determining the number K of typical output scenes of the energy group, where K is a positive integer; randomly selecting K output scenes from various output scenes of the energy group as central scenes, and performing scene clustering operations according to each of the central scenes; the scene clustering operations include: according to a preset distance calculation formula Calculate the scene distance from each output scene to each center scene; where dis(X i ,C j ) is scene X i To the center scene C j The distance of scene X i Data dimension, scenario X i The data dimension is equal to scene C j data dimension; according to the distances of each of the scenes, clustering the output scenes according to the clustering condition with the closest scene distance to obtain each cluster, and taking the center of each cluster as a new center scene; if each of the center scenes does not meet the preset convergence condition, re-execute the scene clustering operation according to each of the center scenes until each of the center scenes meets the convergence condition, then determine that each of the center scenes is each typical output scene of the energy unit; According to each typical output scenario of the energy unit, the operation information of each pumped-storage power station and the operation information of the power grid, a pumped-storage optimization model of the energy unit is constructed, including: according to each output data in each output scenario of the energy unit, an objective function of the pumped-storage optimization model is obtained. Where T is the set of all time periods in each output scenario, P g,n (t) is the online power of the nth renewable energy generator set in the tth time period, and NG is the set of renewable energy generator sets; according to the operation information of each pumped-storage power station, the operation constraint expression of each pumped-storage power station is obtained; wherein, the operation constraint expression of each pumped-storage power station includes: output constraint expression, reservoir capacity constraint expression and climbing constraint expression; according to the operation information of the power grid, the operation constraint expression of each power grid network is obtained; wherein, the operation constraint expression of each power grid network includes: node energy balance constraint expression, transmission line capacity constraint expression, renewable energy output constraint expression and thermal power unit output constraint expression; based on the objective function, the operation constraint expression of each pumped-storage power station and the operation constraint expression of each power grid network, the pumped-storage optimization model of the energy unit is constructed; The pumped-storage optimization model is solved through a preset simulation platform to obtain the operating status results of each pumped-storage power station and the consumption results of the energy units.

2. The method according to claim 1 is characterized in that the pumped storage optimization model is solved by a preset simulation platform to obtain the operating status results of each pumped storage power station and the consumption results of the energy unit, including: The pumped-storage optimization model is input into the MATLAB simulation platform, so that the MATLAB simulation platform solves the pumped-storage optimization model through the CPLEX toolbox, and outputs the operating status results of each pumped-storage power station and the consumption results of the energy units.

3. An energy unit consumption optimization device, characterized in that: include: A data acquisition unit, used to acquire the output historical data of each energy unit to be optimized, the operation information of each pumped storage power station corresponding to the energy unit, and the operation information of the power grid where the energy unit is located; A scenario generating unit, used for simulating various output historical data of the energy unit by using the Monte Carlo method to generate various output scenarios of the energy unit; The scene generation unit includes: a collection subunit and a generation subunit; The collection subunit is used to collect the output data of the energy unit at each time point within a plurality of preset time periods, and obtain the output historical data of each time period; The generating subunit is used to generate an output scenario of the energy source in each of the time periods according to each of the output history data, wherein the output scenario is a sequence formed by sorting the output history data in the time period in chronological order; A scene clustering unit, used for clustering various output scenes of the energy unit through a clustering algorithm to obtain various typical output scenes of the energy unit; The scene clustering unit comprises: a determination subunit and a clustering subunit; The determination subunit is used to determine the number K of typical output scenarios of the energy unit, where K is a positive integer; The clustering subunit is used to randomly select K output scenarios from various output scenarios of the energy unit as central scenarios, and perform a scenario clustering operation according to each of the central scenarios; The scene clustering operation includes: according to a preset distance calculation formula Calculate the scene distance from each output scene to each center scene; where dis(X i ,C j ) is scene X i To the center scene C j The distance of scene X i Data dimension, scenario X i The data dimension is equal to scene C j data dimension; according to the distances of each of the scenes, clustering the output scenes according to the clustering condition with the closest scene distance to obtain each cluster, and taking the center of each cluster as a new center scene; if each of the center scenes does not meet the preset convergence condition, re-execute the scene clustering operation according to each of the center scenes until each of the center scenes meets the convergence condition, then determine that each of the center scenes is each typical output scene of the energy unit; A model building unit, used to build a pumped storage optimization model of the energy unit according to each typical output scenario of the energy unit, the operation information of each pumped storage power station and the operation information of the power grid; The model building unit comprises: a first execution subunit, a second execution subunit, a third execution subunit and a model building subunit; The first execution subunit is used to obtain the objective function of the pumped storage optimization model according to the output data of each output scenario of the energy unit. Where T is the set of all time periods in each output scenario, P g,n (t) is the grid-connected power of the nth renewable energy generator in the tth period, and NG is the set of renewable energy generators; The second execution subunit is used to obtain the operation constraint expression of each pumped storage power station according to the operation information of each pumped storage power station; wherein the operation constraint expression of each pumped storage power station includes: output constraint expression, storage capacity constraint expression and climbing constraint expression; The third execution subunit is used to obtain each power grid network operation constraint expression according to the operation information of the power grid; wherein each power grid network operation constraint expression includes: a node energy balance constraint expression, a transmission line capacity constraint expression, a renewable energy output constraint expression and a thermal power unit output constraint expression; The model building subunit is used to build the pumped storage optimization model of the energy unit based on the objective function, the operation constraint expressions of each pumped storage power station and the operation constraint expressions of each power grid network; The model solving unit is used to solve the pumped storage optimization model through a preset simulation platform to obtain the operating status results of each pumped storage power station and the consumption results of the energy units.

4. The device according to claim 3, wherein the model solving unit is specifically used for: The pumped-storage optimization model is input into the MATLAB simulation platform, so that the MATLAB simulation platform solves the pumped-storage optimization model through the CPLEX toolbox, and outputs the operating status results of each pumped-storage power station and the consumption results of the energy units.

Images