Power system dynamic dispatch method and device considering inter-field wake effect of large-scale wind power cluster

Abstract

The application relates to a power system dynamic scheduling method and equipment considering the inter-field wake effect of a large-scale wind power cluster, and the method comprises the following steps: a wind power cluster operation optimization model is established, the maximum total power generation of the cluster is taken as a target, each wind turbine yaw angle is taken as an optimization variable, the equivalent inflow wind speed of the wind turbine under the superposition of inter-field and intra-field multiple wake flows is calculated based on a Gaussian wake model considering the lateral deviation and yaw influence of the wake flow and a square and superposition method, and the optimization yaw angle of each wind turbine and the optimization output of the cluster are obtained by adopting a grey wolf algorithm; a power system dynamic optimal power flow model considering the access of the offshore wind power cluster is established, the optimization output of the cluster is taken as a grid connection point power constraint, the minimum generation cost of the conventional unit in the whole scheduling period of the system is taken as a target, and the scheduling scheme of the conventional unit is obtained by adopting an interior point method. The application relieves the wake shielding effect of the wind power cluster, excavates the overall output potential of the wind power cluster, and realizes the cooperation of the wind power cluster operation optimization and the power system scheduling.

Description

Technical Field

[0001] This invention relates to the field of power system dispatching technology, and in particular to a dynamic dispatching method and equipment for power systems that takes into account the wake effect between large-scale wind power clusters. Background Technology

[0002] With the accelerated global energy transition, new energy sources, represented by wind power, are gradually becoming the main power source for new power systems. my country's installed wind power capacity continues to climb, and offshore wind power is showing a trend towards clustered and large-scale development. The distance between wind farms within a cluster is constantly decreasing, generally reaching 5 to 10 kilometers. The intermittent and fluctuating nature of wind power output has a profound impact on the power system's power balance, voltage stability, and frequency and peak regulation capabilities. In scenarios with a high proportion of wind power integrated into the grid, how to fully tap the regulation potential of wind power clusters during dispatch has become a key issue in ensuring the safe and economical operation of the system.

[0003] At the power system dispatch level, to address the uncertainties of wind power, existing research has developed methods such as multi-period dynamic optimal power flow, spinning reserve optimization, stochastic unit combination, robust dispatch, and multi-timescale rolling dispatch. However, these methods typically simplify the entire offshore wind farm cluster as a single equivalent power source at the grid connection point, focusing only on its overall output limit when formulating dispatch plans. They do not consider the redistribution of the operating states of individual wind farms within the wind farm cluster after responding to dispatch demands, and in particular, they neglect the dynamic impact of wake effects between wind farms on the actual power output of the cluster.

[0004] At the operational level of wind farms, wake effects are a major factor causing power generation losses. For wakes within a single wind farm, scholars both domestically and internationally have established various wake calculation models, such as the Jensen model, Gaussian model, and yaw wake model, and proposed active wake control strategies such as yaw control and pitch angle control. By optimizing the operating status of the turbines within the wind farm, the overall power generation of the wind farm can be improved. However, with the development of wind power clusters, the wake effect between wind farms is becoming increasingly significant. The wake influence range of an upstream wind farm can reach tens of kilometers, reducing the power generation of downstream wind farms by 10% to 15%. Existing wind farm control strategies are mostly limited to optimization within a single wind farm and fail to consider the constraints of inter-farm wake coupling on the overall output of the cluster.

[0005] In summary, current power system dispatch treats wind farm clusters as equivalent power sources at grid connection points, failing to adequately consider inter-farm wake effects. Furthermore, research on wind farm wake control is limited to single-farm operations, lacking cluster-level coordination. This results in the failure to fully tap the overall output potential of wind farm clusters in dynamic economic dispatch, significantly restricting the optimization space for power system operating costs, thus constituting a pressing technical problem that needs to be addressed. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the existing technology by providing a dynamic power system dispatching method and equipment that considers the wake effect between large-scale wind power clusters. This solves or partially solves the technical problem that existing dynamic power system dispatching methods simplify wind power clusters to equivalent power sources at grid connection points, neglecting the constraint of the wake effect between the clusters on the cluster's output potential, resulting in the overall output of wind power clusters not being fully explored and the space for optimizing system operating costs being limited.

[0007] The objective of this invention can be achieved through the following technical solutions: One aspect of the present invention provides a dynamic dispatching method for power systems that considers the wake effect between large-scale wind power clusters, comprising the following steps: With the goal of maximizing the total power generation of a wind power cluster containing multiple wind farms, and using the yaw angle of each wind turbine in the cluster as the optimization variable, an optimization model for the operation of the wind power cluster is established. Based on the Gaussian wake model that considers the effects of lateral wake offset and yaw, and combined with the sum of squares superposition method, the equivalent inflow wind speed of the wind turbines considering the superposition of multiple wakes between and within the field is calculated, and the power generation of each wind turbine is determined. Under the constraint of wind turbine yaw angle, the operation optimization model is solved based on the Grey Wolf algorithm to obtain the optimized yaw angle of each wind turbine and the optimized output power of the cluster. A dynamic optimal power flow model for the power system considering the access of offshore wind power clusters is established. The optimized output power of the cluster is used as the power constraint of the wind power cluster grid connection point. The objective function is to minimize the sum of the generation costs of conventional units within the entire system scheduling cycle. Under the premise of unit operation constraints and grid security constraints, the interior point method is used to solve the dynamic optimal power flow model to obtain the wind turbine output scheduling scheme.

[0008] As a preferred technical solution, the process of calculating the equivalent inflow velocity of the fan considering the superposition of multiple wakes between and within the field includes the following steps: Using the Gaussian wake model, the wake centerline wind speed, wake width and lateral offset of a single wind turbine at the downstream position are calculated based on the wind turbine yaw angle, thrust coefficient, wind turbine diameter and wake attenuation coefficient. For downstream wind turbines, the equivalent inflow velocity is calculated using the sum of squares method based on the wake obstruction coefficients of multiple upstream wind turbines: in, U ij For the first i Typhoon machine in j The wake velocity at the typhoon generator. U 0 represents the natural wind speed. s ij For wind turbine iFor the fan j The wake obstruction coefficient, U j For wind turbine j The equivalent inflow velocity, N This refers to the number of wind turbines.

[0009] As a preferred technical solution, the process of determining the power generation capacity of each wind turbine includes the following steps: The power generation of each wind turbine is calculated based on the equivalent inflow velocity, air density, impeller swept area, wind energy utilization coefficient, transmission efficiency, and power attenuation caused by yaw angle.

[0010] As a preferred technical solution, the wind turbine yaw angle constraint includes: in, For wind turbine j Yaw angle, For the first s One yaw adjustment cycle , These are the lower and upper limits of the yaw angle, respectively. The yaw angle adjustment rate of the wind turbine. For the first wind turbine s Adjustment amount for different time periods The duration of yaw adjustment.

[0011] As a preferred technical solution, the process of solving the operational optimization model using the Grey Wolf algorithm includes the following steps: In the first stage, the yaw angle of each wind turbine is used as the position of the gray wolf. The gray wolf population is initialized and divided into α wolf, β wolf, δ wolf and ω wolf. The α wolf, β wolf and δ wolf are evaluated and updated through iterative search by encirclement and position update formula, and the ω wolf is guided to update its position until the first termination condition is met. In the second stage, the optimal gray wolf position obtained in the first stage is used as the starting point, and a one-dimensional search is performed along the conjugate direction to gradually approach the optimal solution and obtain the optimized yaw angle of each wind turbine.

[0012] As a preferred technical solution, the objective function of the dynamic optimal power flow model is: in, Let be the objective function. The decision variables include the unit's active power, reactive power, and generator voltage. , These are equality constraints and inequality constraints constructed by introducing slack variables and logarithmic barrier functions, respectively. , These are the lower and upper limits of the inequality constraints, respectively.

[0013] As a preferred technical solution, the process of solving the dynamic optimal power flow model using the interior point method includes the following steps: By introducing slack variables, the inequality constraints in the model are transformed into equality constraints. A Lagrangian function containing logarithmic obstacle terms is constructed, and the optimal power flow solution is obtained through iterative solution.

[0014] As a preferred technical solution, the following steps are also included: The wind turbine layout, turbine parameters, and predicted wind conditions of each wind farm within the wind power cluster are obtained to establish an operation optimization model for the wind power cluster.

[0015] In another aspect, an electronic device is provided, comprising: one or more processors and a memory, wherein the memory stores one or more programs, the one or more programs including instructions for executing the aforementioned dynamic dispatching method for a power system that takes into account the wake effect between large-scale wind farm clusters.

[0016] In another aspect, the present invention provides a computer-readable storage medium including one or more programs executable by one or more processors of an electronic device, the one or more programs including instructions for performing the aforementioned dynamic dispatching method for a power system that takes into account the wake effect between large-scale wind farm clusters.

[0017] Compared with the prior art, the present invention has at least one of the following beneficial effects: (1) Achieved a coordinated improvement in the overall output of the cluster: Existing wind farm control strategies are mostly limited to optimization within a single field, ignoring the increasingly significant wake effect between fields in the context of wind farm cluster development, resulting in severe power generation loss in downstream wind farms and the overall output potential of wind power clusters has not been fully tapped. To address this issue, this invention establishes an operation optimization model with the goal of maximizing the total power generation of the wind power cluster. The yaw angle of each wind turbine in the cluster is used as the optimization variable. The Gaussian wake model, which considers the lateral offset and yaw effect of the wake, is combined with the sum of squares method to calculate the equivalent inflow wind speed of the wind turbines under the superposition of multiple wakes between and within the field. The Grey Wolf algorithm is used to solve the problem and obtain the optimal yaw angle configuration for the entire field. This alleviates the wake shading effect within the wind farm cluster. By sacrificing a small amount of power in the upstream wind farm, the power generation of the downstream wind farm is increased, thus achieving a coordinated improvement in the overall output of the cluster.

[0018] (2) Achieving Synergy Between Wind Power Cluster Internal Operation Optimization and System-Level Scheduling: Existing dynamic economic dispatch methods for power systems typically simplify offshore wind farm clusters as a single equivalent power source at the grid connection point. When formulating dispatch plans, the actual internal output capacity changes due to inter-farm wake effects after the wind power cluster responds to dispatch demands are not considered, leading to a disconnect between the dispatch plan and the actual operating characteristics of wind power. To address this, this invention connects the wind power cluster operation optimization model, which considers the inter-farm wake effect, with the dynamic optimal power flow model of the power system. The optimized wind power cluster power is used as the power constraint at the wind power grid connection point, embedded in the dynamic optimal power flow model with the objective of minimizing the cost of conventional unit power generation throughout the entire system dispatch cycle, and solved using the interior point method. This achieves synergy between wind power cluster internal operation optimization and system-level dispatch, enabling power system dispatch to make decisions based on a more realistic and optimal wind power cluster output boundary.

[0019] (3) High reliability and computational efficiency: The inter-field wake collaborative optimization variables of large-scale wind power clusters are high in dimensionality and the scheduling model contains complex constraints. When using traditional single algorithms to solve the problem, there is a problem that it is difficult to balance global search capability and local convergence accuracy. On the one hand, in solving the wind power cluster operation optimization model, the Grey Wolf algorithm is used to combine global search capability with the fast local convergence capability of one-dimensional conjugate direction search. On the other hand, in solving the dynamic optimal power flow model, the interior point method is used to transform inequality constraints into equality constraints by introducing slack variables and logarithmic barrier functions, and constructing the Lagrangian function for iterative solution, thus ensuring the reliability and computational efficiency of the overall dynamic scheduling method when solving high-dimensional and nonlinear optimization problems. Attached Figure Description

[0020] Figure 1 This is a flowchart of a power system dynamic dispatching method that considers the wake effect between large-scale wind power clusters in the embodiments. Figure 2 This is a schematic diagram of dynamic power system dispatching in the embodiment; Figure 3 This is a schematic diagram of the Gaussian wake model in the embodiment; Figure 4 This is a schematic diagram of the multi-engine wake effect in the embodiment; Figure 5 This is a schematic diagram of wake shielding in the embodiment; Figure 6 This is a schematic diagram of the wind power cluster layout in the embodiment; Figure 7 This is a schematic diagram of the wake under normal operation and wake optimization of wind farm WF4 in the embodiment; Figure 8 The bar chart shows the average power of each exhaust fan under normal operation and in-field wake optimization of wind farm WF4 in the example. Figure 9This is a graph showing the total power curves of wind farm WF4 under normal operation and in-farm wake optimization in the example; Figure 10 This is a schematic diagram showing the total power curves of the wind power cluster under three operating modes and the power gain curves under different strategies in the embodiment; Figure 11 This is a comparison chart of the power curves of WF1 and WF2 under in-field wake optimization and inter-field wake optimization in the embodiment. Figure 12 This is a comparison chart of the power curves of WF5 and WF6 under in-field wake optimization and inter-field wake optimization in the embodiment. Figure 13 This is a graph showing the grid-connected power curve at point PCC in the embodiment; Figure 14 This is a schematic diagram of the power system unit power curves under different wind power operation strategies in the embodiments; Figure 15 This is a bar chart showing the system operating costs under different wind power operation strategies in the embodiments; Figure 16 This is a schematic diagram of the electronic device in the embodiment. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0022] Example 1 To address the problems existing in the prior art, this embodiment provides a dynamic power system dispatching method that considers the wake effect between large-scale wind power clusters. (See [link to relevant documentation]). Figure 2 First, based on the Gaussian yaw wake model, the superposition effect of wakes between wind turbines is quantified. With the goal of maximizing the total power generation of the wind power cluster, an optimization model for wind power cluster operation is established, with the yaw angle of the turbines as the optimization variable, to solve for the optimal yaw angle combination. Second, the optimized wind power cluster output is used as a constraint and embedded into a continuous-time dynamic optimal power flow model with the goal of minimizing system operating costs. The optimal output plan for conventional units is obtained by solving the interior-point method. This method achieves coordinated optimization of yaw control within the wind power cluster and grid dispatching decisions, unifying the increase in cluster output with the reduction of system operating costs.

[0023] See Figure 1 The method includes the following steps: Step S1: With the goal of maximizing the total power generation of the wind power cluster containing multiple wind farms, and with the yaw angle of each wind turbine in the cluster as the optimization variable, establish a wind power cluster operation optimization model.

[0024] See Figure 6 The diagram shows the layout of the offshore wind power cluster in the embodiment. The offshore wind power cluster consists of nine wind farms. Wind farms WF1, WF2, and WF3 are adjacent, and WF5 and WF6 are adjacent, with strong inter-farm wakes between them. Wind farms WF4, WF7, WF8, and WF9 have larger spacing between them, and their farm-level wakes have a smaller impact.

[0025] Step S2: Based on the Gaussian wake model that considers the lateral offset and yaw effects of the wake, and combined with the sum of squares superposition method, calculate the equivalent inflow wind speed of the wind turbine considering the superposition of multiple wakes between and within the field, and determine the power generation capacity of each wind turbine.

[0026] For details, see Figure 3 , Figure 4 and Figure 5 The Gaussian model is used to quantify the wake effect. The relationship between the wind turbine yaw angle, thrust coefficient and wake can be calculated by equations (1) to (4): (1) (2) (3) (4) In the formula: C ( x (downstream) x Wind speed at the centerline of the wake; U 0 represents the natural airflow speed; γ This refers to the yaw angle of the wind turbine; d The diameter of the fan; σ Wake width; δ y This refers to the lateral offset of the wake. k d This is the deflection attenuation coefficient; ε This is the initial wake width factor; k* This is the wake attenuation coefficient; U ( x , y ) is within the wake region ( x , y Wind speed at location )

[0027] In large-scale wind farms, downstream turbines may be affected by the wake effects of multiple upstream turbines. This step, based on the assumption of kinetic energy conservation, uses the sum of squares (SQS) method to handle independent wakes and calculates the equivalent inflow velocity to the downstream turbines: (5) In the formula: U ij For the firsti Typhoon machine in j The wake velocity at the typhoon generator; U 0 represents the natural airflow speed; s ij For wind turbine i For the fan j The wake obstruction coefficient; U j For wind turbine j The equivalent inflow velocity.

[0028] Under yaw conditions, the power generation of the wind turbine is calculated using equation (6). (6) In the formula: ρ air density; A The swept area of ​​the fan impeller; v This refers to the inflow velocity of the fan. C p The wind energy utilization coefficient; η For fan transmission efficiency; γ This refers to the yaw angle of the wind turbine.

[0029] Step S3: Under the constraint of the wind turbine yaw angle, the operation optimization model is solved based on the Grey Wolf algorithm to obtain the optimized yaw angle of each wind turbine and the optimized output power of the cluster.

[0030] With the wind turbine yaw angle γ To optimize the variables, a wind power cluster operation optimization model is established, with yaw angle constraints as shown in equations (7) and (8): (7) (8) Using the total power generation of the wind power cluster as the objective function, an improved gray wolf optimization algorithm is used for a two-stage solution. The gray wolf algorithm divides the wolf pack (solution set) into... α (Optimal solution) β (Suboptimal solution) δ (Third optimal solution) and ω For wolves (other solutions), the optimization is performed by simulating the hunting behavior of wolf packs; the optimization formula for the first stage is (9)~(11); (9) (10) (11) In the formula: t This represents the number of iterations. X p This is the current optimal solution vector; X Position for the gray wolves; A , CFor coefficient vectors; D This represents the distance relationship between solution vectors.

[0031] Assessment α , β , δ Queen Wolf, guide ω The wolf updates its position; after the gray wolf search phase ends, the optimal solution obtained by the gray wolf algorithm is used as the starting point for the next stage of optimization, and a one-dimensional search is performed to gradually approach the optimal solution along the conjugate direction.

[0032] Step S4: Establish a dynamic optimal power flow model of the power system considering the access of offshore wind power clusters. The optimized output power of the cluster is used as the power constraint of the wind power cluster grid connection point. The objective function is to minimize the sum of the generation costs of conventional units within the entire system scheduling cycle. Under the premise of unit operation constraints and grid security constraints, the interior point method is used to solve the dynamic optimal power flow model to obtain the wind turbine output scheduling scheme.

[0033] The goal is to minimize the operating cost of the power system. (12) In the formula: t Indicates the first t Each time period; G Indicates the number of conventional generator sets in the system; f g Indicates the first g The cost function for a unit can be constructed based on factors such as equipment depreciation and carbon emissions. P t For the first g Taiwanese crew t Active power during a given time period.

[0034] Constructing power flow constraints: (13) In the formula: P g,min and P g,max The upper and lower limits are constraints on the active power of the generator unit; Q g,min and Q g,max Upper and lower limits of reactive power constraints for the unit; V n,min and V n,max For nodes n The maximum and minimum values ​​of voltage amplitude; θ n,min and θ n,max For nodes nThe maximum and minimum values ​​of the voltage phase angle; I ij,max For the line ij Maximum carrying capacity; P g,D , P g,U For the unit g The rate of decline of active power and the rate of decline; P g,t For the unit g exist t Active power during a given time period; Δ T This refers to the duration of the scheduling cycle.

[0035] The solution is obtained by using the interior point method, and its standard form is shown in equation (14). (14) Introducing slack variables l , s Transform the inequality constraints in the model into equality constraints: (15) And construct the Lagrangian function containing the logarithmic barrier term: (16) In the formula: x These are the decision variables, namely the unit's active power, reactive power, and generator voltage; f ( x The objective function is the active power cost of the system throughout its entire lifecycle. h ( x )=0 and g ( x The slack variables are the equality and inequality constraints constructed by introducing slack variables and a logarithmic barrier function; slack variables l , s >0.

[0036] Preferred options also include: Step S5: Optimize model validation and performance evaluation.

[0037] Record the power curves of each wind farm and the total power curve of the cluster during normal operation. Compare the wind power output under three operating modes: single-unit maximum power point tracking strategy, single-farm wake collaborative optimization strategy, and inter-farm wake collaborative optimization strategy. Use the optimized power of offshore wind power operation as a constraint condition to solve the dynamic optimal power flow model of the power system and compare the system operation costs under different offshore wind power operation strategies.

[0038] This method achieves a coordinated increase in the overall power output of wind power clusters by constructing an optimization model for cluster operation aimed at maximizing cluster power generation. The proposed wind power cluster yaw optimization method can effectively mitigate the wake shading effect. For example... Figure 7 , Figure 8 and Figure 9 As shown, the optimization within the WF4 wind farm increased the power output of the middle and rear turbines by 11.49% to 34.81%, and the overall power generation increased by 5.7%, verifying the effectiveness of wake-coordinated control. Extending the optimization scope to inter-farm clusters, as shown... Figure 10 , Figure 11 and Figure 12 As shown, upstream wind farms sacrifice a small amount of power (2.8%–3.2%) to achieve a significant gain of 10.7%–12.5% ​​in downstream farms, thus optimizing the overall power output of the cluster. Compared to the single-unit maximum power point tracking strategy, the inter-farm wake collaborative optimization increases the average total power output of the cluster by 8.65%, and further improves it by 1.06% compared to the single-farm wake collaborative optimization strategy.

[0039] This method embeds the optimized wind power cluster power into a dynamic optimal power flow model, achieving collaborative optimization between the wind power cluster and the power system. Using the optimized wind power output as the dispatch boundary of the onshore power system, the system operating cost is significantly reduced. Compared with the single-unit maximum power point tracking strategy, the single-field wake collaborative optimization strategy reduces the cost by 1.506%, and the cluster-field wake collaborative optimization strategy further expands the reduction to 1.603%.

[0040] This method is based on the interior-point method to solve dynamic optimal power flow models, ensuring both the reliability and computational efficiency of the solution. The interior-point method, by introducing slack variables and logarithmic barrier functions to handle inequality constraints, transforms the original problem into one with no constraints or only equality constraints. It has polynomial time complexity and is suitable for large-scale power system optimization problems.

[0041] To verify the effectiveness of this method, simulation experiments were conducted. The simulation example uses the improved IEEE RTS-24 node system as the onshore power system. Nodes 11, 19, 44, and 49 correspond to PCC1, PCC2, PCC3, and PCC4, which are the four grid-connected nodes of the offshore wind power cluster. The wind turbines in the offshore wind power cluster are IEA 10MW turbines. The wind power cluster includes 9 offshore wind farms with a total installed capacity of 3720MW. Specific parameters are shown in Table 1.

[0042] Table 1 Simulation Parameter Table The simulation lasted for 4 hours with a time resolution of 15 minutes. Wind data are shown in Table 2.

[0043] Table 2 Simulated wind data Figure 13 (a) When considering the offshore grid, the offshore wind power is coordinated at the two grid connection points to connect to the grid in a more economical way, which is more conducive to reducing the system operating cost and can reduce the grid connection pressure of high-power grid connection points compared to the default grid connection power. Figure 13 (b) shows that under the proposed cluster optimization strategy, the grid connection power is higher than that under the single-unit maximum power tracking strategy. Higher grid connection power is beneficial to reducing the coal cost of traditional units. Figure 14 This indicates that increasing the power output of offshore wind power clusters helps reduce the output of traditional turbines, thereby lowering system operating costs. Figure 15 This indicates that considering wake optimization of offshore wind power clusters and power coordination and allocation among grid connection points during system optimization can reduce system operating costs to some extent.

[0044] As can be seen from the above figures, the inter-field wake optimization method proposed in this invention can effectively alleviate the wake effect, thereby increasing the overall power generation of the cluster by 8.65%, which is 1.06% higher than that of single-field optimization. After considering the inter-field wake effect, the system operating cost is reduced by 1.603% compared with the single-machine maximum power point tracking strategy, which verifies the effectiveness of the proposed strategy in improving cluster output and reducing system operating cost.

[0045] This invention effectively mitigates the wake effect between wind power clusters, enhances the overall power generation capacity of the cluster, and reduces the power generation cost of thermal power units by coupling the wake optimization model of wind power clusters with the dynamic optimal power flow model of the power system. It provides a feasible technical path for the economic dispatch of the power system in scenarios with a high proportion of offshore wind power.

[0046] Example 2 Based on Embodiment 1, this embodiment provides an electronic device, including: one or more processors and a memory, wherein the memory stores one or more programs, the one or more programs including instructions for executing the power system dynamic dispatching method considering the wake effect between large-scale wind power clusters as described in Embodiment 1.

[0047] like Figure 16 At the hardware level, the electronic device includes a processor, internal bus, network interface, memory, and non-volatile memory, and may also include other hardware required for the business operations. The processor reads the corresponding computer program from the non-volatile memory into memory and then runs it to achieve the above-mentioned functions. Figure 1 The method described herein. Of course, in addition to software implementation, this invention does not exclude other implementation methods, such as logic devices or a combination of hardware and software, etc. That is to say, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0048] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0049] Example 3 Based on Embodiment 1, this embodiment provides a computer-readable storage medium including one or more programs executable by one or more processors of an electronic device, the one or more programs including instructions for executing the power system dynamic dispatching method considering the inter-field wake effect of large-scale wind power clusters as described in Embodiment 1.

[0050] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0051] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A dynamic dispatching method for power systems considering the wake effect between large-scale wind power clusters, characterized in that, Includes the following steps: With the goal of maximizing the total power generation of a wind power cluster containing multiple wind farms, and using the yaw angle of each wind turbine in the cluster as the optimization variable, an optimization model for the operation of the wind power cluster is established. Based on the Gaussian wake model that considers the effects of lateral wake offset and yaw, and combined with the sum of squares superposition method, the equivalent inflow wind speed of the wind turbines considering the superposition of multiple wakes between and within the field is calculated, and the power generation of each wind turbine is determined. Under the constraint of wind turbine yaw angle, the operation optimization model is solved based on the Grey Wolf algorithm to obtain the optimized yaw angle of each wind turbine and the optimized output power of the cluster. A dynamic optimal power flow model for the power system considering the access of offshore wind power clusters is established. The optimized output power of the cluster is used as the power constraint of the wind power cluster grid connection point. The objective function is to minimize the sum of the generation costs of conventional units within the entire system scheduling cycle. Under the premise of unit operation constraints and grid security constraints, the interior point method is used to solve the dynamic optimal power flow model to obtain the wind turbine output scheduling scheme.

2. The power system dynamic dispatching method considering the wake effect between large-scale wind power clusters according to claim 1, characterized in that, The process of calculating the equivalent inflow velocity of the fan considering the superposition of multiple wakes between and within the field includes the following steps: Using the Gaussian wake model, the wake centerline wind speed, wake width and lateral offset of a single wind turbine at the downstream position are calculated based on the wind turbine yaw angle, thrust coefficient, wind turbine diameter and wake attenuation coefficient. For downstream wind turbines, the equivalent inflow velocity is calculated using the sum of squares method based on the wake obstruction coefficients of multiple upstream wind turbines: in, U ij For the first i Typhoon machine in j The wake velocity at the typhoon generator. U 0 represents the natural wind speed. s ij For wind turbine i For the fan j The wake obstruction coefficient, U j For wind turbine j The equivalent inflow velocity, N This refers to the number of wind turbines.

3. The power system dynamic dispatching method considering the wake effect between large-scale wind power clusters according to claim 1, characterized in that, The process of determining the power generation capacity of each wind turbine includes the following steps: The power generation of each wind turbine is calculated based on the equivalent inflow velocity, air density, impeller swept area, wind energy utilization coefficient, transmission efficiency, and power attenuation caused by yaw angle.

4. A dynamic power system dispatching method considering the wake effect between large-scale wind power clusters according to claim 1, characterized in that, The wind turbine yaw angle constraint includes: in, For wind turbine j Yaw angle, For the first s One yaw adjustment cycle , These are the lower and upper limits of the yaw angle, respectively. The yaw angle adjustment rate of the wind turbine. For the first wind turbine s Adjustment amount for different time periods The duration of yaw adjustment.

5. A dynamic power system dispatching method considering the wake effect between large-scale wind power clusters according to claim 1, characterized in that, The process of solving the running optimization model using the Grey Wolf algorithm includes the following steps: In the first stage, the yaw angle of each wind turbine is used as the position of the gray wolf. The gray wolf population is initialized and divided into α wolf, β wolf, δ wolf and ω wolf. The α wolf, β wolf and δ wolf are evaluated and updated through iterative search by encirclement and position update formula, and the ω wolf is guided to update its position until the first termination condition is met. In the second stage, the optimal gray wolf position obtained in the first stage is used as the starting point, and a one-dimensional search is performed along the conjugate direction to gradually approach the optimal solution and obtain the optimized yaw angle of each wind turbine.

6. A dynamic power system dispatching method considering the wake effect between large-scale wind power clusters according to claim 1, characterized in that, The objective function of the dynamic optimal power flow model is: in, Let be the objective function. The decision variables include the unit's active power, reactive power, and generator voltage. , These are equality constraints and inequality constraints constructed by introducing slack variables and logarithmic barrier functions, respectively. , These are the lower and upper limits of the inequality constraints, respectively.

7. A dynamic power system dispatching method considering the wake effect between large-scale wind power clusters according to claim 1, characterized in that, The process of solving the dynamic optimal power flow model using the interior point method includes the following steps: By introducing slack variables, the inequality constraints in the model are transformed into equality constraints. A Lagrangian function containing logarithmic obstacle terms is constructed, and the optimal power flow solution is obtained through iterative solution.

8. A dynamic power system dispatching method considering the wake effect between large-scale wind power clusters according to claim 1, characterized in that, It also includes the following steps: The wind turbine layout, turbine parameters, and predicted wind conditions of each wind farm within the wind power cluster are obtained to establish an operation optimization model for the wind power cluster.

9. An electronic device, characterized in that, include: One or more processors and a memory, wherein the memory stores one or more programs, the one or more programs including instructions for executing the dynamic dispatching method for a power system that takes into account the wake effect between large-scale wind farm clusters as described in any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, It includes one or more programs that are executed by one or more processors of an electronic device, the one or more programs including instructions for executing the dynamic dispatching method for a power system that takes into account the wake effect between large-scale wind farm clusters as described in any one of claims 1-8.

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