Wind-storage-pumping-storage combined optimization scheduling method considering wind power uncertainty and related device

Abstract

The invention belongs to the technical field of power grid dispatching, and relates to a wind-storage-pumping-storage combined optimization dispatching method considering wind power uncertainty and a related device. The method comprises the following steps: according to a selected wind power output reference scene, establishing a scheduling model based on the reference scene; solving the scheduling model based on the reference scene to obtain an optimal objective function value in the reference scene; considering the deterioration degree of the optimal objective function value in the reference scene, and establishing an optimal scheduling model containing the wind power electric power system based on weak robust optimization; and solving the optimal scheduling model of the wind power-containing power system based on weak robust optimization to obtain a wind-storage-pumping-storage combined optimal scheduling scheme considering the wind power uncertainty. The problem that the coordination optimization potential of the power supply side and the power grid side is neglected in the aspect of wind storage complementation at present is solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power grid dispatching, and particularly relates to a wind-storage-pumped storage combined optimal dispatching method considering the uncertainty of wind power and related devices. Background Art

[0002] The grid connection of a high proportion of wind power has brought profound changes to the power grid and also posed huge challenges. On the one hand, the grid connection of large-scale wind power makes the operation of the power system more energy-saving and environmentally friendly. On the other hand, the inherent volatility, reverse peak-shaving characteristics, and low dispatchability of wind power generation itself have also brought huge challenges to the power system. The uncertainty shown in the wind power output brings certain difficulties to the prediction of wind power. Therefore, when analyzing the optimal dispatching problem of a power system with a high proportion of wind power access, the prediction error of wind power must be considered.

[0003] Divided from the perspective of time and space, the consumption methods of wind power are divided into two types: intra-regional consumption and cross-regional consumption. Within the region, the energy storage system can be introduced to improve the consumption level of wind power. Compared with other energy storage methods, pumped storage has the advantages of large energy storage capacity and fast operation speed. The application technology of pumped storage units is also relatively mature and is suitable for joint operation with wind farms.

[0004] Although there are some studies on the complementarity of wind power and energy storage at present, they mainly focus on the analysis of the complementary characteristics of energy sources on the power generation side and optimal dispatching, and the research on its long-distance transmission and consumption is not deep enough, ignoring the coordination and optimization potential between the power supply side and the power grid side. Summary of the Invention

[0005] The purpose of the present invention is to provide a wind-storage-pumped storage combined optimal dispatching method considering the uncertainty of wind power and related devices, which solves the problem of ignoring the coordination and optimization potential between the power supply side and the power grid side existing in the current wind power and energy storage complementarity.

[0006] The present invention is realized through the following technical solutions: The wind-storage-pumped storage combined optimal dispatching method considering the uncertainty of wind power includes the following processes: According to the selected benchmark scenario of wind power output, a dispatching model based on the benchmark scenario is established; the dispatching model based on the benchmark scenario includes an objective function and constraint conditions; Solve the dispatching model based on the benchmark scenario to obtain the optimal objective function value under the benchmark scenario; Considering the deterioration degree of the optimal objective function value under the benchmark scenario, an optimal dispatching model of a wind power-integrated power system based on weak robust optimization is established; Solve the optimal dispatching model of the wind power-integrated power system based on weak robust optimization to obtain a wind-storage-pumped storage combined optimal dispatching scheme considering the uncertainty of wind power.

[0007] Furthermore, the objective function of the scheduling model under the benchmark scenario is as follows: (5) In the formula: represents the set of pumped-storage units in Region A; represents the pumped-storage unit at the operating state of the motor during the period; = 1 indicates that the motor is in the pumping state; = 0 indicates that the motor is in the shutdown state; represents the pumped-storage unit at the operating state of the generator during the period; = 1 indicates that the generator is in the power generation state; = 0 indicates that the generator is in the shutdown state; and respectively represent the starting cost coefficient of the motor when the pumped-storage unit is about to enter the pumping state and the starting cost coefficient of the generator when the pumped-storage unit is about to enter the power generation state; and respectively represent the power supply price of the main grid to the pumped-storage unit and the power generation feed-in price of the pumped-storage unit; and respectively represent the pumped-storage unit at the pumping power and the power generation power during the period; is the set of thermal power units in Region A; is the total number of periods within the scheduling cycle. One period is taken as 1 h, and there are 24 periods in total; is the thermal power unit at the power generation cost during the period; is the thermal power unit at the start-stop cost during the period; is the thermal power unit at the environmental cost during the period; is the set of wind turbines in Region A; is the wind farm at the curtailment penalty cost during the period; and respectively represent the charging cost coefficient and the discharging revenue coefficient of the energy storage; and The energy storage is Charging power and discharging power in a period of time; The constraints of the dispatch model based on the benchmark scenario include power balance constraints, thermal power unit output constraints, thermal power unit ramp constraints, power grid dispatch wind power constraints, positive spinning reserve constraints, negative spinning reserve constraints, DC tie line operation constraints and related constraints on pumped storage power station operation; The relevant constraints on the operation of pumped-storage power stations are divided into pure pumped-storage power station operation constraints and hybrid pumped-storage power station operation constraints.

[0008] Furthermore, thermal power units exist Generation cost during the period The expression is as follows: (6) Where: For thermal power units exist Output during the time period; , and Thermal power units Coal consumption coefficient; Thermal power units exist Start and stop costs during the period The expression is as follows: (7) (8) Where: For thermal power units exist The operating status during the period, =0 means the unit is shut down. =1 means the unit is running; For thermal power units exist The startup cost of the time period; and Represents thermal power units hot start and cold start costs; Represents thermal power unit Minimum downtime; Represents thermal power unit arrive Continuous downtime up to the end of the period; Represents thermal power unit cold start time.

[0009] Furthermore, thermal power units exist Environmental costs during a period The expression is as follows: (10) In the formula: is the environmental pollution penalty coefficient; is the thermal power unit during output during the period; wind farm during the curtailment penalty cost during the period The expression is as follows: (11) (12) In the formula: is the curtailment penalty coefficient; is the curtailment power of the wind turbine unit during the period; is the output prediction value of the wind turbine unit during the period; is the wind power actually called by the power grid of the wind turbine unit during the period.

[0010] Furthermore, the operation constraints of a pure pumped-storage power station include upper and lower output limits, operation condition constraints, reservoir capacity constraints, continuous change of reservoir capacity, and balance constraints of water pumping and generation, which are specifically as follows: (13) (14) (15) (16) (17) (18) In the formula: and respectively represent the pumping power and generating power of the pumped-storage unit during the period; 、 respectively represent the pumping and generating operation condition states of the pumped-storage unit, which are 0-1 variables; 、 respectively represent the lower and upper limits of the operation power of the pumped-storage unit; is the upper reservoir capacity of the pumped-storage power station at time t; 、 are the upper and lower limits of the upper reservoir storage capacity of the pumped - storage power station respectively; is the state during power generation, taking 1 during power generation and 0 otherwise; is the state during pumping, taking 1 during pumping and 0 otherwise; are the upper reservoir storage capacities at the beginning and end of the scheduling period respectively; is the lower limit of the operating power when the pumped - storage unit is in the pumping operation mode; is the upper limit of the operating power when the pumped - storage unit is in the pumping operation mode.

[0011] Furthermore, in addition to satisfying the output constraints and operating condition constraints of equations (13) - (15), the pumped - storage units in the hybrid - storage power station also satisfy the power - generation flow constraint, the reservoir capacity constraint, and the power - generation flow constraint of the hydropower station; The power - generation flow of the hybrid - storage power station is the sum of the power - generation flow of the conventional units and the flow of the pumped - storage units. Assuming that the flow of the pumped - storage unit is positive during power generation and negative during pumping, the power - generation flow of the hybrid - storage power station is expressed as: (19) In the formula: is the power - generation flow of the hybrid - storage power station k at time t; is the power - generation flow of the conventional units of the hybrid - storage power station k at time t; 、 are the power - generation flow and pumping flow of the pumped - storage units of the hybrid - storage power station k at time t respectively; (20) (21) (22) In the formula: is the reservoir capacity of hydropower station i at time t; 、 are the upper and lower limits of the reservoir capacity of hydropower station i respectively; 、 are the upper and lower limits of the power - generation flow of hydropower station i respectively; is the power - generation flow of hydropower station i at time t; is the inflow of hydropower station i at time t.

[0012] Furthermore, the optimal scheduling model of the power system with wind power based on weak - robust optimization includes an objective function and constraint conditions; The objective function is as follows:

[0013] In the formula: represents the set of pumped - storage units in region A; represents the pumped - storage unit During the operating state of the motor in a time period; = 1 indicates that the motor is in the pumping state; = 0 indicates that the motor is in the shutdown state; Indicates the pumped-storage unit During the operating state of the generator in a time period; = 1 indicates that the generator is in the power generation state; = 0 indicates that the generator is in the shutdown state; And respectively represent the starting cost coefficient of the motor when the pumped-storage unit is about to enter the pumping state and the starting cost coefficient of the generator when the pumped-storage unit is about to enter the power generation state; And respectively represent the power supply price of the main grid to the pumped-storage unit and the power generation on-grid price of the pumped-storage unit; And respectively represent the pumped-storage unit During the pumping power and power generation power in a time period; is the set of thermal power units in region A; is the total number of time periods in the dispatching cycle, one time period is 1 h, and there are 24 time periods in total; is the thermal power unit During the power generation cost in a time period; is the thermal power unit During the start-stop cost in a time period; is the thermal power unit During the environmental cost in a time period; is the set of wind turbines in region A; And respectively represent the charging cost coefficient and discharging income coefficient of the energy storage; And respectively are the charging power and discharging power of the energy storage in the th time period; is the wind turbine During the output prediction value in a time period; is the penalty coefficient for curtailed wind; is the wind turbine During the actual output value in a time period; is the wind turbine During the time period, the wind power actually called by the power grid; The constraint conditions of the optimal scheduling model of the power system with wind power based on weak robust optimization are added as follows on the basis of the constraint conditions of the scheduling model based on the benchmark scenario: (23) In the formula: is the optimal objective function value under the benchmark scenario, is the deterioration degree of the optimal objective function value under the benchmark scenario; According to formula (23), the actual output value of the wind turbine During the time period is obtained, and then the joint optimal scheduling scheme of wind-storage-pumped storage considering the uncertainty of wind power is obtained.

[0014] The present invention also discloses a joint optimal scheduling system of wind-storage-pumped storage considering the uncertainty of wind power, including: A scheduling model construction module, configured to establish a scheduling model based on a benchmark scenario according to a selected benchmark scenario of wind power output; the scheduling model based on the benchmark scenario includes an objective function and constraint conditions; A first solving module, configured to solve the scheduling model based on the benchmark scenario to obtain the optimal objective function value under the benchmark scenario; An optimal scheduling model construction module, configured to establish an optimal scheduling model of a power system with wind power based on weak robust optimization considering the deterioration degree of the optimal objective function value under the benchmark scenario; A second solving module, configured to solve the optimal scheduling model of the power system with wind power based on weak robust optimization to obtain a joint optimal scheduling scheme of wind-storage-pumped storage considering the uncertainty of wind power.

[0015] The present invention also discloses a computer device, including a memory, a processor, and a computer program stored in the memory and running on the processor, and when the processor executes the computer program, the steps of the joint optimal scheduling method of wind-storage-pumped storage considering the uncertainty of wind power are implemented.

[0016] The present invention also discloses a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the joint optimal scheduling method of wind-storage-pumped storage considering the uncertainty of wind power are implemented.

[0017] Compared with the prior art, the present invention has the following beneficial technical effects: The present invention discloses a combined optimal scheduling method for wind power storage and pumped storage considering the uncertainty of wind power. Through the scheduling model under the benchmark scenario, an optimal solution under ideal conditions is obtained as a reference for subsequent robust optimization. Then, weak robust optimization is introduced, allowing the objective function to deteriorate within a certain range to cope with the uncertainty of wind power. Weak robust optimization may be more flexible than traditional robust optimization and not overly conservative, thus finding a more economical scheduling scheme in an uncertain environment.

[0018] Different from strong robust optimization and sub-conservative robust optimization in describing the uncertainty of wind power output, weak robust optimization will select a benchmark scenario in advance. After introducing uncertain parameters, it solves the problem by considering both the perturbation range of the uncertain parameters and the degree of deterioration of the objective function. The weak robust optimization method comprehensively considers the feasibility of the solution and the economy of the model, and is closer to the actual application environment.

[0019] Furthermore, the established scheduling model comprehensively considers the operating costs and revenues of thermal power units, pumped storage units, wind turbines, and energy storage, optimizes the output and operating states of each unit, and while meeting the power demand, minimizes the total generation cost as much as possible, including the coal consumption cost and start-stop cost of thermal power units, as well as the related costs of pumped storage units, etc. The model also considers the penalty cost for wind curtailment in wind farms, which promotes the scheduling scheme to make full use of wind power as much as possible, reduces wind curtailment, improves the utilization rate of wind power, thereby reducing the economic losses caused by wind curtailment, and at the same time improving the utilization efficiency of renewable energy.

[0020] Furthermore, the constraint conditions of the established optimal scheduling model of a power system with wind power based on weak robust optimization cover power balance constraints, thermal power unit output and ramp constraints, grid dispatching wind power constraints, spinning reserve constraints, DC tie line operation constraints, and related constraints for the operation of pumped storage power stations, etc., ensuring that the system can meet the requirements of safe and stable operation under various operating conditions, avoiding safety problems such as power imbalance, unit overload, and grid congestion, and guaranteeing the safe and reliable operation of the power system. Reasonably arranging the charging, discharging, pumping, and generating states of energy storage and pumped storage units can not only smooth the fluctuations of wind power output, but also provide necessary power support or store excess power during peak or low load periods of the system, playing a role in regulating the power balance and stabilizing the frequency of the system, and further improving the operation safety and stability of the system. Description of the Drawings

[0021] Figure 1 It is a schematic diagram of the solution space of strong robust optimization; Figure 2 It is a schematic diagram of the solution spaces of strong robust optimization and sub-conservative robust optimization; Figure 3 It is a relationship diagram of the solution space of weak robust optimization and other solution spaces; Figure 4 Flow chart of the wind - storage - pumped storage combined optimal scheduling method considering the uncertainty of wind power in the present invention; Figure 5 Block diagram of the wind - storage - pumped storage combined optimal scheduling system considering the uncertainty of wind power in the present invention. Detailed implementation manners

[0022] In order to make the objectives, technical solutions and advantages of the present invention clearer, the following further detailed description is given in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention, rather than to limit the present invention, that is, the described embodiments are only a part of the embodiments of the present invention, rather than all embodiments.

[0023] The detailed description of the embodiments of the present invention provided in the following accompanying drawings is not intended to limit the scope of the claimed present invention, but merely represents a selected embodiment of the present invention. Based on the accompanying drawings and embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts belong to the protection scope of the present invention.

[0024] The features and performance of the present invention are further described in detail in conjunction with the following embodiments.

[0025] When using the robust optimization method to solve the uncertainty optimization problem, the solution of the model has a certain degree of conservatism. According to the different degrees of conservatism in solving the uncertainty optimization problem, the robust optimization method of the present invention is divided into three types: strong robust optimization, sub - conservative robust optimization, and weak robust optimization.

[0026] 1. Strong robust optimization For a deterministic optimization problem, that is, when not considering the perturbation of the parameters in the model, it can generally be expressed in the following mathematical form: (1) In the formula: is the decision variable of this model; is the objective function to be optimized in the model; is the - th constraint condition in the model.

[0027] However, in practical applications, the influence brought by the perturbation of uncertain parameters is often faced. At this time, the deterministic model is no longer applicable. When the perturbation of the uncertain parameters to the solution of the model is large, the influence of the uncertain parameters must be considered. At this time, when using the traditional robust optimization method to describe the mathematical programming model containing uncertain parameters, the representation form is as follows: (2) In the formula: is the uncertain parameter in this model; is the uncertainty set, and for any scenario in the set , a feasible solution can be obtained by solving this model.

[0028] Since the solution in this robust optimization model is feasible for any scenario , the model has strong robustness. Therefore, this type of robust optimization method is called a strong robust optimization method. The schematic diagram of the solution space of strong robust optimization is shown in Figure 1 .

[0029] 2. Sub - conservative robust optimization Sub - conservative robust optimization reduces the conservatism of the problem - solving by relaxing the constraint conditions in the model. It can be regarded as an improvement of the strong robust optimization method. Its specific mathematical expression is as follows: (3) In the formula: is the relaxation amount of the th constraint condition in the model .

[0030] After relaxing the constraint conditions in the model with uncertain variables, the solution space of the corresponding optimization problem will be enlarged. At this time, the solution space of the sub - conservative robust optimization method and the solution space of the strong robust optimization method are shown in Figure 2 .

[0031] After relaxing some constraint conditions in the model, the solution space of the obtained sub - conservative robust optimization is larger than that of the strong robust optimization. However, relaxing some constraint conditions in the model may cause some other constraint conditions to be no longer satisfied.

[0032] 2. Weak robust optimization Sub - conservative robust optimization obtains a robust solution by relaxing some constraint conditions in the model. However, in the actual application environment, only considering the robustness of the solution is not advisable. For the economic dispatch problem of a power system with wind power integration, while paying attention to the robustness of the solution under the uncertainty of wind power output, the dispatcher will also consider the economy of the power system operation, that is, the objective function in the model.

[0033] The following gives the representation form of the weak robust optimization method. First, select the benchmark scenario of the robust optimization problem. Then, seek the robustness of the solution in the model without seriously deteriorating the objective function in the benchmark scenario.

[0034] When studying issues related to wind power output, etc., the benchmark scenario is generally the system operating state determined according to historical data or normal operating conditions. For example, when analyzing the stability of a power system containing wind power, the benchmark scenario may be the operating state on a typical day, where each generator in the system outputs power according to the plan, the load is at a normal level, and the wind power output is based on the long-term average power or the typical seasonal power. It serves as a reference standard for comparing the performance and behavior of the system under different wind power output variations, different dispatching strategies, or different system disturbances, etc.

[0035] The solution obtained by using the weak robust optimization method needs to meet the following conditions: (1) The obtained solution Must be feasible under the benchmark scenario, that is, it must satisfy . Is the benchmark scenario.

[0036] (2) Compared with the optimal value under the benchmark scenario, the solution Has a certain tolerance for the deterioration degree of the objective function and satisfies .

[0037] At this time, the relationship diagram between the solution space of weak robust optimization and other solution spaces is as shown in Figure 3 Shown.

[0038] Among the conditions that the weak robust optimization method needs to meet, Represents the objective function value obtained by the optimization model under the selected benchmark scenario; Is the deterioration degree of the objective function value, ; In Figure 3 It means that after introducing the uncertain parameters, Represents the maximum fluctuation range near the optimal value under the benchmark scenario.

[0039] Considering both the conservatism of the solution and the deterioration degree of the objective function under the benchmark scenario, a weak robust optimization model can be established as shown in the following formula: (4) In the formula: Is the objective function value optimized under the benchmark scenario , Represents the constraint condition after introducing the uncertain parameters, Is the uncertain set considering the adjustable conservatism.

[0040] Therefore, the present invention finally selects the weak robust optimization method to solve the uncertainty problem.

[0041] Example 1 Based on the characteristics of the weak robust optimization method, when solving the economic dispatch problem of a power system with wind power integrated into the grid, the present invention discloses a joint optimal scheduling method for wind power, energy storage, and pumped storage considering the uncertainty of wind power, as follows Figure 4 shown, including the following steps: According to the selected reference scenario of wind power output, establish a scheduling model based on the reference scenario; Solve the scheduling model based on the reference scenario to obtain the optimal objective function value under the reference scenario; Considering the deterioration degree of the optimal objective function value under the reference scenario, establish an optimal scheduling model for a wind power-integrated power system based on weak robust optimization; Solve the optimal scheduling model for a wind power-integrated power system based on weak robust optimization to obtain a joint optimal scheduling plan for wind power, energy storage, and pumped storage considering the uncertainty of wind power.

[0042] Through the phased modeling of "reference scenario optimization + weak robust optimization", the present invention realizes the decoupling of economic analysis and robust decision-making. The reference model provides a theoretical optimal cost benchmark, and the weak robust model quantifies the impact of uncertainty, avoiding the scheduling failure caused by excessive conservatism or optimism in traditional methods.

[0043] Example 2 Based on Example 1, mainly introduce the scheduling model based on the reference scenario in S2. The scheduling model based on the reference scenario includes an objective function and constraint conditions.

[0044] First, introduce the objective function, which is as follows: The objective function includes the start-up cost when the pumped storage unit pumps water and the start-up cost when it generates electricity, the charge and discharge operation cost of the pumped storage unit, the generation cost of the thermal power unit, the environmental pollution penalty cost, the wind curtailment penalty cost, and the operation cost of the battery energy storage. The specific objective function is as follows: (5) In the formula: represents the set of pumped storage units in region A; represents the pumped storage unit at the operating state of the motor in the time period; = 1 indicates that the motor is in the pumping state; = 0 indicates that the motor is in the shut-down state; represents the pumped storage unit at the operating state of the generator in the time period; = 1 indicates that the generator is in the power generation state; = 0 indicates that the generator is in the shut-down state; and respectively represent the starting cost coefficient of the motor when the pumped - storage unit is about to enter the pumping state and the starting cost coefficient of the generator when the pumped - storage unit is about to enter the generating state; and respectively represent the power supply price of the main grid to the pumped - storage unit and the power generation grid - connection price of the pumped - storage unit; and respectively represent the pumping power and generating power of the pumped - storage unit at time period; is the set of thermal power units in region A; is the total number of time periods within the dispatching cycle. One time period is 1 h, with a total of 24 time periods; is the thermal power unit at time period of the generating cost; is the thermal power unit at time period of the start - stop cost; is the thermal power unit at time period of the environmental cost; is the set of wind turbines in region A; is the wind farm at time period of the curtailment penalty cost; and respectively represent the charging cost coefficient and discharging revenue coefficient of the energy storage; and respectively are the charging and discharging powers of the energy storage in the th time period.

[0045] The expressions for the generating cost and start - stop cost of the thermal power unit are as follows: (6) In the formula: is the output of the thermal power unit at time period; , and respectively are the coal consumption coefficients of the thermal power unit .

[0046] (7) (8) In the formula: The start - stop cost of the thermal power unit at time period , which can be uniformly converted into the start-up cost of the unit; is the operating state of the thermal power unit, = 0 indicates that the unit is out of service, = 1 indicates that the unit is operating; is the thermal power unit at the start-up cost during the period; and respectively represent the hot start-up and cold start-up costs of the thermal power unit ; represents the minimum downtime of the thermal power unit ; represents the continuous downtime of the thermal power unit from to the period; represents the cold start-up time of the thermal power unit ;

[0047] For the start-stop cost of the thermal power unit, which contains a non-linear product term in the form of , in order to linearize it, new integer variables and corresponding constraint conditions need to be introduced. Here, let , and it can be seen that is a new integer variable, which can be equivalently represented by the following linear constraint: (9) The environmental cost of the thermal power unit during the period is expressed as follows: (10) (10) In the formula: is the environmental pollution penalty coefficient; is the thermal power unit at the output during the period;

[0048] The curtailment cost of the wind farm during the period is expressed as follows: (11) (11) (12) In the formula: is the curtailment penalty coefficient; is the wind turbine at the curtailment power during the period; is the wind turbine at Output prediction value for a time period; For a wind turbine During The wind power actually called by the power grid within the time period.

[0049] Constraints are as follows: The constraints include power balance constraint, thermal power unit output constraint, thermal power unit ramp constraint, grid dispatching wind power constraint, positive spinning reserve constraint, negative spinning reserve constraint, and DC tie line operation constraint, which will not be elaborated here.

[0050] There are also relevant constraints for the operation of pumped storage power stations, which are as follows: 1) Operation constraints of a pure pumped storage power station. The operation constraints of a pure pumped storage power station include upper and lower output limits, operation condition constraints, reservoir capacity constraints, continuous change of reservoir capacity, and water pumping and generation balance constraints.

[0051] (13) (14) (15) (16) (17) (18) In the formula: And Respectively represent the pumping power and generating power of the pumped storage unit During Time period; 、 Respectively represent the pumping and generating operation condition states of the pumped storage unit, which are 0-1 variables; 、 Respectively represent the lower and upper limits of the operating power of the pumped storage unit; Is the upper reservoir capacity of the pumped storage power station at time t; 、 Respectively represent the upper and lower limits of the upper reservoir capacity of the pumped storage power station. Is the state during power generation, taking 1 when generating power, otherwise taking 0; Is the state during pumping, taking 1 when pumping, otherwise taking 0; Respectively represent the upper reservoir capacities at the beginning and end of the dispatching period; Is the lower limit of the operating power when the pumped storage unit is in the pumping operation condition state; Is the upper limit of the operating power when the pumped storage unit is in the pumping operation condition state.

[0052] 2) Operation constraints of a hybrid pumped storage power station In addition to meeting the output constraints and operating condition constraints of equations (13) - (15), the pumped - storage units in the hybrid energy storage power station also need to meet the following constraints.

[0053] The power generation flow of the hybrid energy storage power station is the sum of the power generation flows of the conventional units and the pumped - storage units. Assuming that the flow of the pumped - storage unit is positive during power generation and negative during pumping, the power generation flow of the hybrid energy storage power station can be expressed as: (19) Where: is the power generation flow of the k - th hybrid energy storage power station at time t; is the power generation flow of the conventional units of the k - th hybrid energy storage power station at time t; is the flow of the pumped - storage unit of the k - th hybrid energy storage power station at time t; 、 are the power generation flow and pumping flow of the pumped - storage unit of the k - th hybrid energy storage power station at time t, respectively.

[0054] (20) (21) (22) Where: is the reservoir capacity of the i - th hydropower station at time t; 、 are the upper and lower limits of the reservoir capacity of the i - th hydropower station, respectively; 、 are the upper and lower limits of the power generation flow of the i - th hydropower station, respectively; is the power generation flow of the i - th hydropower station at time t; is the inflow of the i - th hydropower station at time t.

[0055] Example 3 On the basis of Example 1, an optimal dispatching model of a wind - power - integrated power system based on weak robust optimization is introduced, including the objective function and constraint conditions.

[0056] Replace all the in equation (12) with . is the actual output value of the wind turbine at time period.

[0057] The objective function is as follows:

[0058] Constraint conditions: Considering that when the objective function value in the benchmark scenario deteriorates to a certain extent, the following criterion needs to be added when solving the optimal dispatching model of the wind - power - integrated power system based on weak robust optimization: (23) In the formula: is the optimal objective function value under the reference scenario, that is, the optimal solution under the reference scenario, is the deterioration degree of the optimal objective function value under the reference scenario, that is, considering the uncertainty of wind power output, a certain amount of economy needs to be sacrificed, making the model less conservative.

[0059] The forms of the remaining constraint conditions are the same as those in the constraint conditions of Embodiment 2, except that all the contained in the constraint conditions of Embodiment 2 are replaced with , that is, considering the uncertainty of wind power output.

[0060] Finally, the weakly robust optimization solution is obtained through formula (23),

[0061] which is the joint optimal scheduling scheme of wind power - energy storage - pumped storage considering wind power uncertainty.

[0062] Embodiment 4 As Figure 5 shown, the present invention also discloses a joint optimal scheduling system of wind power - energy storage - pumped storage considering wind power uncertainty, including: A scheduling model construction module, used to establish a scheduling model based on the selected reference scenario of wind power output; A first solution module, used to solve the scheduling model based on the reference scenario to obtain the optimal objective function value under the reference scenario; An optimal scheduling model construction module, used to establish an optimal scheduling model of a wind - power - containing power system based on weakly robust optimization considering the deterioration degree of the optimal objective function value under the reference scenario; A second solution module, used to solve the optimal scheduling model of a wind - power - containing power system based on weakly robust optimization to obtain the joint optimal scheduling scheme of wind power - energy storage - pumped storage considering wind power uncertainty.

[0063] Embodiment 5 The present invention also discloses a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the wind-storage-pumped storage combined optimal scheduling method considering wind power uncertainty are implemented. Among them, the memory may include internal memory, such as high-speed random access memory, and may also include non-volatile memory, such as at least one disk memory, etc.; the processor, network interface, and memory are interconnected through an internal bus, which can be an Industry Standard Architecture bus, a Peripheral Component Interconnect standard bus, an Extended Industry Standard Architecture bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. The memory is used to store programs. Specifically, the program may include program code, and the program code includes computer operation instructions. The memory can include internal memory and non-volatile memory and provide instructions and data to the processor.

[0064] Embodiment 8 The present invention also discloses a computer-readable storage medium storing a computer program, which when executed by a processor implements the steps of the wind-storage-pumped storage combined optimal scheduling method considering wind power uncertainty. Specifically, the computer-readable storage medium includes, but is not limited to, for example, volatile memory and / or non-volatile memory. The volatile memory may include random access memory and / or cache memory, etc. The non-volatile memory may include read-only memory, hard disk, flash memory, optical disc, magnetic disk, etc.

[0065] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, optical memory, etc.) containing computer-usable program code.

[0066] The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate for implementing in the process Figure 1 one process or multiple processes and / or blocks Figure 1A device for the functions specified in one or more boxes.

[0067] These computer program instructions can also be stored in a computer-readable memory capable of guiding a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured article including an instruction device, and the instruction device implements the functions specified in one Figure 1 process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0068] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one Figure 1 process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0069] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: the specific implementation manners of the present invention can still be modified or equivalently replaced, and any modification or equivalent replacement without departing from the spirit and scope of the present invention shall be covered by the protection scope of the claims of the present invention.

Claims

1. A wind-storage-pumped storage integrated optimal scheduling method considering the uncertainty of wind power, characterized in that Including: Based on the selected reference scenario of wind power output, a scheduling model based on the reference scenario is established; the scheduling model based on the reference scenario includes an objective function and constraint conditions; Solve the scheduling model based on the reference scenario to obtain the optimal objective function value under the reference scenario; Considering the deterioration degree of the optimal objective function value under the reference scenario, an optimal scheduling model of a wind power integrated power system based on weak robust optimization is established; Solve the optimal scheduling model of the wind power integrated power system based on weak robust optimization to obtain a joint optimal scheduling scheme of wind energy-storage-pumped storage considering the uncertainty of wind power.

2. The wind-storage-pumped storage combined optimal scheduling method considering the uncertainty of wind power according to claim 1, wherein The objective function of the scheduling model based on the reference scenario is as follows: （5） In the formula: represents the set of pumped-storage units in area A; Indicates the operating state of the motor of the pumped-storage unit during the operation period of the motor; = 1 indicates that the motor is in the pumping state; = 0 indicates that the motor is in the shutdown state; Indicates the operating status of the generator of the pumped-storage unit during the operation period of the generator; = 1 indicates that the generator is in the power generation state; = 0 indicates that the generator is in the outage state; and respectively represent the starting cost coefficient of the motor when the pumped-storage unit is about to enter the pumping state and the starting cost coefficient of the generator when the pumped-storage unit is about to enter the generating state; and respectively represent the power supply price from the main grid to the pumped - storage unit and the power generation price of the pumped - storage unit for grid connection; and respectively represent the pumping power and the generating power of the pumped - storage unit during period; is the set of thermal power units in Region A; is the total number of periods within the dispatching cycle. One period is 1 h, with a total of 24 periods; is the generation cost of the thermal power unit during period; is the start - up and shut - down cost of the thermal power unit during period; is the environmental cost of the thermal power unit during period; is the set of wind turbines in Region A; is the curtailment penalty cost of the wind farm during period; and respectively represent the charging cost coefficient and the discharging revenue coefficient of the energy storage; and respectively are the charging power and the discharging power of the energy storage during the th period; The constraint conditions of the scheduling model based on the reference scenario include power balance constraint, thermal power unit output constraint, thermal power unit ramp rate constraint, grid dispatching wind power constraint, positive spinning reserve constraint, negative spinning reserve constraint, DC tie line operation constraint and relevant constraints for the operation of pumped storage power stations; The relevant constraints for the operation of pumped storage power stations are divided into the operation constraints of pure pumped storage power stations and the operation constraints of hybrid pumped storage power stations.

3. The wind-storage-pumped storage combined optimal scheduling method considering the uncertainty of wind power according to claim 2, characterized in that Thermal power unit During The power generation cost during the period The expression is as follows: (6) Where: is the output of the thermal power unit at time period; , and are respectively the coal consumption coefficients of the thermal power unit ; Thermal power unit During The start-stop cost during the period The expression is as follows: (7) (8) Wherein: is the operating state of the thermal power unit during the time period; = 0 indicates that the unit is out of service, = 1 indicates that the unit is operating; is the start-up cost of the thermal power unit during the time period; and respectively represent the hot start-up and cold start-up costs of the thermal power unit ; represents the minimum downtime of the thermal power unit ; represents the continuous downtime of the thermal power unit from to the time period ; represents the cold start-up time of the thermal power unit.

4. The wind-storage-pumped storage combined optimal scheduling method considering the uncertainty of wind power according to claim 2, characterized in that Thermal power unit During the environmental cost within the time period is expressed as follows: (10) Wherein: is the environmental pollution penalty coefficient; is the thermal power unit at output during the period; Wind farm During the curtailment penalty cost is expressed as follows: (11) (12) Wherein: is the curtailment penalty coefficient; is the curtailment power of the wind turbine during period; is the predicted output value of the wind turbine during period; is the wind power actually dispatched by the power grid for the wind turbine during period.

5. The wind-storage-pumped storage combined optimal scheduling method considering the uncertainty of wind power according to claim 2, characterized in that The operation constraints of pure pumped storage power stations include upper and lower output limits constraint, operation condition constraint, reservoir capacity constraint, continuous change of reservoir capacity and water pumping and generation balance constraint, which are specifically as follows: （13） （14） (15) (16) (17) (18) Wherein: and respectively represent the pumping power and the generating power of the pumped-storage unit during time period; 、 are respectively the operating conditions of the pumped-storage unit during pumping and generating, and are 0-1 variables; 、 are respectively the lower limit and the upper limit of the operating power of the pumped-storage unit; is the storage capacity of the upper reservoir of the pumped-storage power station at time t; 、 are respectively the lower limit and the upper limit of the storage capacity of the upper reservoir of the pumped-storage power station; is the state during power generation, taking 1 when generating power, otherwise taking 0; is the state during pumping, taking 1 when pumping, otherwise taking 0; are respectively the storage capacities of the upper reservoir at the beginning and the end of the scheduling period; is the lower limit of the operating power when the pumped-storage unit is in the pumping condition; is the upper limit of the operating power when the pumped-storage unit is in the pumping condition.

6. The wind-storage-pumped storage integrated optimal scheduling method considering the uncertainty of wind power according to claim 5, characterized in that In addition to meeting the output constraints and operation condition constraints of equations (13)-(15), the pumped storage units in the hybrid storage power station also meet the power generation flow constraint of the hybrid storage power station, the reservoir capacity constraint of the hydropower station, and the power generation flow constraint of the hydropower station; The power generation flow of the hybrid storage power station is the sum of the power generation flow of the conventional units and the flow of the pumped storage units. Assuming that the flow of the pumped storage units is positive during power generation and negative during pumping, the power generation flow of the hybrid storage power station is expressed as: （19） In the formula: is the power generation flow rate of the hybrid energy storage power station k in the t period; is the power generation flow rate of the conventional unit of the hybrid energy storage power station k in the t period; , are respectively the power generation flow rate and the pumping flow rate of the pumped storage unit of the hybrid energy storage power station k in the t period; （20） （21） （22） Where: is the reservoir capacity of hydropower station i at time t; and are the upper and lower limits of the reservoir capacity of hydropower station i, respectively; and are the upper and lower limits of the power generation flow of hydropower station i, respectively; is the power generation flow of hydropower station i at time t; is the inflow of hydropower station i at time t.

7. The wind-storage-pumped storage combined optimal scheduling method considering the uncertainty of wind power according to claim 1, wherein The optimal scheduling model of a wind power integrated power system based on weak robust optimization includes an objective function and constraint conditions; The objective function is as follows: In the formula: represents the set of pumped-storage units in area A; Indicates the operating state of the motor of the pumped-storage unit during the operation period of the motor; = 1 indicates that the motor is in the pumping state; = 0 indicates that the motor is in the shutdown state; Indicates the operating state of the pumped-storage unit during the operating state of the generator in a certain period; = 1 indicates that the generator is in the power generation state; = 0 indicates that the generator is in the shutdown state; and respectively represent the starting cost coefficient of the motor when the pumped-storage unit is about to enter the pumping state and the starting cost coefficient of the generator when the pumped-storage unit is about to enter the power generation state; and respectively represent the power supply price of the main grid to the pumped-storage unit and the power generation price of the pumped-storage unit for grid connection; and respectively represent the pumping power and the power generation power of the pumped-storage unit during period; is the set of thermal power units in Region A; is the total number of periods within the scheduling cycle. One period is taken as 1 h, and there are 24 periods in total; is the power generation cost of the thermal power unit during period; is the start-stop cost of the thermal power unit during period; is the environmental cost of the thermal power unit during period; is the set of wind turbines in Region A; and respectively represent the charging cost coefficient and the discharging revenue coefficient of the energy storage; and respectively are the charging power and the discharging power of the energy storage during the th period; is the predicted output value of the wind turbine during period; is the curtailment penalty coefficient; is the wind turbine at actual output value during the period; is the wind turbine at actual wind power dispatched by the power grid during the period; The constraint conditions of the optimal scheduling model of the wind power integrated power system based on weak robust optimization add the following constraint conditions on the basis of the constraint conditions of the scheduling model based on the reference scenario: (23) In the formula: is the optimal objective function value under the baseline scenario, is the deterioration degree of the optimal objective function value under the baseline scenario; The solution according to formula (23) is the output actual value of the wind turbine at the actual output value during the time period , and then the joint optimal scheduling scheme of wind-storage-pumped storage considering wind power uncertainty is obtained.

8. Wind-storage-pumped storage combined optimal scheduling system considering the uncertainty of wind power, characterized in that, Including: A scheduling model construction module for establishing a scheduling model based on the selected reference scenario of wind power output; the scheduling model based on the reference scenario includes an objective function and constraint conditions; A first solving module for solving the scheduling model based on the reference scenario to obtain the optimal objective function value under the reference scenario; An optimal scheduling model construction module for establishing an optimal scheduling model of a wind power integrated power system based on weak robust optimization considering the deterioration degree of the optimal objective function value under the reference scenario; A second solving module for solving the optimal scheduling model of the wind power integrated power system based on weak robust optimization to obtain a joint optimal scheduling scheme of wind energy-storage-pumped storage considering the uncertainty of wind power.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the joint optimal scheduling method of wind energy-storage-pumped storage considering the uncertainty of wind power as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the joint optimal scheduling method of wind energy-storage-pumped storage considering the uncertainty of wind power as described in any one of claims 1 to 7.

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