A new energy power generation scheduling method for zero examination quantity constraint

Abstract

The application belongs to the field of power grid dispatching, and specifically discloses a new energy power generation dispatching method facing zero examination quantity constraint. The core of realizing zero examination of the application lies in constructing an economic optimization model allowing active wind curtailment and energy storage cooperation. Traditional methods usually only rely on energy storage to smooth fluctuations, and may fail due to insufficient capacity under extreme fluctuations. The scheme internalizes the examination cost as a cost, and simultaneously takes the wind curtailment instruction and energy storage control as optimization variables, so that the model can autonomously weigh between economy and compliance. The introduced zero examination quantity constraint sets an insurmountable boundary for active power change, and the wind curtailment variable in the model ensures that when the fluctuation may exceed the limit, the system can cooperate with energy storage through the most economical wind curtailment decision to ensure that the grid-connected power strictly meets the standard. Compared with the prior art, through the wind curtailment and energy storage cooperation mechanism, a complete and feasible path for realizing zero examination is provided, and the grid-connected certainty and safety are fundamentally improved.

Description

Technical Field

[0001] This application belongs to the field of power grid dispatching technology, specifically involving a new energy power generation dispatching method oriented towards zero performance constraints. Background Technology

[0002] With the large-scale integration of new energy sources such as wind power into the power grid, the requirements for the quality of new energy power generation are also increasing year by year. In order to continuously promote the consumption of new energy, alleviate the pressure on power grid operation, and implement the requirements for improving the quality of new energy power generation, the "Implementation Rules for the Management of Grid-Connected Power Plant Operation" and the "Implementation Rules for the Management of Ancillary Services of Grid-Connected Power Plants" (hereinafter referred to as the "two rules") have formulated detailed assessment requirements.

[0003] Currently, for wind power storage stations, regarding the operational assessment issues arising from the accuracy of wind power reporting in the "two detailed rules" assessment, there exists a two-stage power optimization reporting strategy that can effectively reduce the risk of assessed electricity volume: day-ahead and intraday. For combined thermal and energy storage systems, regarding the issue of primary frequency regulation performance of conventional units in the "two detailed rules" assessment, there exists a primary frequency regulation scheme and control strategy for configuring rapid-response energy storage in thermal power units. There are also strategies for the joint operation of wind farms with energy storage to address the issues arising from the accuracy of wind power forecasting in the "two detailed rules," especially the increasingly stringent correlation coefficient assessment. All of the above addresses the operational assessment costs resulting from insufficient accuracy in wind power forecasting.

[0004] Specifically, operational assessment costs include not only wind power prediction accuracy but also active power change rate, data qualification rate, and costs related to technical guidance and management. Currently, active power change rate-related costs constitute a significant portion of the assessment costs allocated to new energy power plants. However, there is a general lack of sufficient consideration of the active power change rate at power plants, and a lack of means to incorporate it into the assessment.

[0005] Furthermore, even considering the rate of change of active power, wind power plants with energy storage systems of varying capacities still struggle to achieve zero performance constraints due to the fluctuating nature of new energy sources. This is because any energy storage system has physical limits on its power and capacity, making it unable to cope with potentially extreme and random wind power fluctuations, especially those whose amplitude exceeds its regulation range or whose duration leads to energy depletion.

[0006] In summary, the existing wind farm dispatching system faces the problem of increasing assessment costs year by year, which also means that fluctuations in wind power grid connection will challenge the security of the power system after grid connection. Summary of the Invention

[0007] To address the shortcomings of existing technologies, this application aims to propose a new energy power generation dispatching method oriented towards zero assessment constraints. This method addresses the current new energy power generation dispatching schemes' lack of means to incorporate active power change rate, and the difficulty in meeting the zero assessment requirement due to the fluctuating characteristics of new energy sources, which leads to reduced safety of new energy power plants.

[0008] The first aspect of this application relates to a new energy power generation dispatching method oriented towards zero performance constraints, comprising: constructing a dispatching optimization model with the objective of optimizing the economic operating cost of the wind farm based on the state of the wind-storage combined power generation system and grid connection performance indicators; wherein, the economic operating cost includes the performance cost caused by the excessive fluctuation of grid-connected power; introducing zero performance constraints into the dispatching optimization model to obtain a dispatching optimization model containing zero performance constraints; the zero performance constraints are configured to limit the change value and rate of change of the active power at the grid connection point of the wind farm to within the threshold range specified by the grid connection performance indicators; solving the dispatching optimization model containing zero performance constraints to generate wind curtailment control commands for wind turbines and charging and discharging control commands for the energy storage system; and performing coordinated control of the wind turbines and energy storage system in the wind farm according to the wind curtailment control commands and the charging and discharging control commands to ensure that the grid-connected power of the wind farm meets the grid connection performance indicators.

[0009] In one embodiment, the grid connection assessment indicators include: the excess assessment electricity volume under multiple preset time scales, and the excess assessment electricity volume of the power change rate. Based on the status of the wind-storage combined power generation system and the grid connection assessment indicators, a scheduling optimization model is constructed with the goal of optimizing the economic operating cost of the wind farm. This includes: obtaining a wind-storage combined power generation system model based on power balance constraints, wind turbine output constraints, wind curtailment constraints, and energy storage system constraints; obtaining an assessment electricity volume model based on the excess assessment electricity volume of the power change value and the excess assessment electricity volume of the power change rate under different preset scales; obtaining an assessment cost model based on the assessment electricity volume model; and constructing a scheduling optimization model with the goal of optimizing the economic operating cost of the wind farm based on the assessment cost model and the wind-storage combined power generation system model.

[0010] In one embodiment, a wind-storage combined power generation system model is obtained based on power balance constraints, wind turbine output constraints, and energy storage system constraints. Prior to this, the model further includes: obtaining power balance constraints based on the actual power of the wind turbines in the wind farm, the charging and discharging power of the energy storage units, and the grid-connected power of the wind farm; obtaining wind turbine output constraints and wind curtailment constraints based on the actual power of the wind turbines in the wind farm, the curtailed wind power, and the predicted generating power; and obtaining energy storage system constraints based on the charging and discharging power of the energy storage units and their initial capacity.

[0011] In one embodiment, the preset scale includes a 1-minute scale and a 10-minute scale; the power change value exceeding the limit at the 1-minute scale is used to assess the amount of electricity. for: ; ; Where t is the current time; This represents the total number of consecutive 1-minute intervals within the assessment period. This is a parameter for assessing changes in active power of wind power; the default value is 1. Let t be the grid-connected power. The power change value of the wind farm's grid-connected power on a 1-minute scale: Wind farm installed capacity; Exceeding the limit for power variation in wind farms on a 10-minute scale for: ; ; in, The number of consecutive, non-overlapping fixed windows of length 10 minutes within the assessment period; the power change rate of the wind farm exceeding the assessment limit in minute t. for: ; ; in, The number of sliding windows with a duration of 10 minutes within the assessment period; Let be the power change rate of the wind farm in minute t; This is the limit value for the rate of change of power.

[0012] In one embodiment, an assessment cost model is obtained based on the assessment power volume model, and a scheduling optimization model is constructed based on the assessment cost model and the wind-storage combined power generation system model, with the goal of optimizing the economic operating cost of the wind farm. This includes: obtaining the assessment cost based on the assessment power volume exceeding the limit at the 1-minute and 10-minute scales of the wind farm and the assessment power volume exceeding the limit at the power change rate at minute t; obtaining the grid-connected consumption power of the wind farm after one day of operation based on the wind-storage combined power generation system model, and then generating the grid-connected consumption profit; obtaining the wind curtailment power of the wind farm after one day of operation based on the wind-storage combined power generation system model, and then generating the wind curtailment penalty cost; and constructing a scheduling optimization model with the goal of optimizing the economic operating cost of the wind farm based on the assessment cost, the grid-connected consumption profit, and the wind curtailment penalty cost.

[0013] In one embodiment, a zero-performance constraint is introduced into the scheduling optimization model to obtain a scheduling optimization model containing zero-performance constraints. This includes setting a first zero-performance constraint, a second zero-performance constraint, and a third zero-performance constraint in the scheduling optimization model to generate a scheduling optimization model containing zero-performance constraints. The first zero-performance constraint limits the absolute value of the grid-connected power change at adjacent times to no more than a first predetermined proportion of the installed capacity; the second zero-performance constraint limits the power change value over 10 minutes to no more than a second predetermined proportion of the installed capacity; and the third zero-performance constraint limits the power change rate per minute within a rolling 10-minute period to no more than a power change rate limit.

[0014] In one embodiment, solving a scheduling optimization model with zero performance constraints to generate wind curtailment control commands for wind turbines and charging / discharging control commands for energy storage systems includes: using a mathematical programming solver to solve the scheduling optimization model with zero performance constraints to obtain the optimal wind curtailment power and energy storage charging / discharging power at each time point; generating wind curtailment control commands to control the actual output of each wind turbine based on the optimal wind curtailment power at each time point; and generating charging / discharging control commands to control the charging or discharging state of the energy storage system based on the optimal energy storage charging / discharging power at each time point.

[0015] Overall, the technical solutions conceived in this application have the following beneficial effects compared with the prior art: The core innovation of this application in achieving zero performance evaluation lies in constructing an economic dispatch model that allows for complete coordination between proactive wind curtailment and energy storage control. Specifically, firstly, by internalizing performance evaluation costs as expenses and simultaneously incorporating wind curtailment commands and energy storage charging / discharging commands as optimization variables into the model, a dispatch model is constructed with the goal of optimizing total operating costs. This fundamentally establishes a quantitative trade-off between economic efficiency and grid connection compliance, enabling the system to automatically optimize among multiple objectives such as reducing performance evaluation and reducing wind curtailment / energy storage losses. Subsequently, the zero performance evaluation constraint introduced into the model sets strict boundaries for grid-connected power variations, and the wind curtailment control variable in the model is one of the key guarantees for meeting these hard boundaries. When predicted fluctuations may exceed limits, the model can proactively invoke wind curtailment commands, working in conjunction with energy storage regulation to ensure that the power change value and rate of change never exceed limits. This solves the fundamental problem that relying solely on energy storage may fail to meet stringent constraints due to capacity or power limitations. Ultimately, the cooperative control commands generated by solving the model, because they pre-include optimization decisions that require an economic cost to achieve absolute compliance, physically achieve reliable and precise control of grid-connected power.

[0016] Compared with existing technologies, this solution not only proposes a zero assessment target through constraint setting, but also, through the wind curtailment control option built into the model, forms a necessary and sufficient condition for achieving the target with energy storage. This ensures that the zero assessment target can be achieved in wind power active power variation scenarios where traditional wind-storage combined systems cannot fully absorb the wind power while simultaneously meeting the assessment constraints, significantly improving the certainty and safety of the station operation. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating the new energy power generation dispatching method for zero performance constraints provided in the embodiments of this application; Figure 2 This is a flowchart illustrating step S10 provided in an embodiment of this application; Figure 3 This is a flowchart illustrating step S13 provided in an embodiment of this application; Figure 4 This is a graph of the original wind power data of the wind farm provided in the embodiments of this application; Figure 5 This is a graph showing the 1-minute grid-connected power change of Scheme 1 provided in the embodiments of this application; Figure 6 This is a graph showing the 10-minute scale grid-connected power change value of Scheme 1 provided in the embodiments of this application; Figure 7 This is a graph showing the rate of change of grid-connected power according to Scheme 1 provided in the embodiments of this application; Figure 8 This is a graph showing the grid-connected power versus predicted power of Scheme 2 provided in this application embodiment; Figure 9 This is a graph of the wind curtailment power curve provided in the second embodiment of this application; Figure 10 This is a graph showing the 1-minute scale grid-connected power change value of Scheme 2 provided in the embodiments of this application; Figure 11 This is a graph showing the 10-minute scale grid-connected power change value of Scheme 2 provided in the embodiments of this application; Figure 12 This is a graph showing the rate of change of grid-connected power in Scheme 2 provided in this application embodiment; Figure 13 This is a graph showing the grid-connected power versus predicted power of Scheme 3 provided in the embodiments of this application; Figure 14 This is a power variation curve of the energy storage system according to Scheme 3 provided in the embodiments of this application; Figure 15 This is a graph showing the change in remaining capacity of the energy storage system according to Scheme 3 provided in this application embodiment; Figure 16This is a graph showing the 1-minute scale grid-connected power change value of Scheme 3 provided in the embodiments of this application; Figure 17 This is a graph showing the 10-minute scale grid-connected power change value of Scheme 3 provided in the embodiments of this application; Figure 18 This is a graph showing the rate of change of grid-connected power for Scheme 3 provided in the embodiments of this application; Figure 19 This is a power change curve of the energy storage system when the capacity of Scheme 3 provided in this application is below the critical capacity; Figure 20 This is a graph showing the change in the remaining capacity of the energy storage system when the capacity is below the critical capacity, according to Scheme 3 provided in this application. Figure 21 This is a graph showing the change in grid-connected power at the critical capacity of Scheme 4 provided in this application over a 10-minute timescale. Figure 22 This is a data result diagram of Scheme 4 provided in the embodiments of this application; Figure 23 This is a graph showing the grid-connected power versus predicted power of Scheme 5 provided in the embodiments of this application; Figure 24 This is a power variation curve of the energy storage system according to Scheme 5 provided in the embodiments of this application; Figure 25 This is a graph showing the change in remaining capacity of the energy storage system according to Scheme 5 provided in this application embodiment; Figure 26 This is a graph showing the wind curtailment power curve of Scheme 5 provided in the embodiments of this application; Figure 27 This is a graph showing the 1-minute scale grid-connected power change value of Scheme 5 provided in the embodiments of this application; Figure 28 This is a graph showing the 10-minute scale grid-connected power change value of Scheme 5 provided in the embodiments of this application; Figure 29 This is a graph showing the rate of change of grid-connected power in Scheme 5 provided in the embodiments of this application; Figure 30 This is a data result diagram of Scheme 5 provided in the embodiments of this application. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0019] Currently, the rate of change of active power is a major factor in the assessment costs of new energy power plants, but this indicator is often overlooked or lacks effective means of inclusion in actual assessments. Furthermore, due to the volatility of new energy sources and the physical limitations of energy storage systems, even with energy storage installed, power plants cannot completely avoid assessment costs, posing a safety challenge.

[0020] Based on this, this application proposes a new energy power generation dispatch method oriented towards zero performance constraints. Please refer to... Figure 1 ,exist Figure 1 The new energy power generation dispatch method for zero performance constraints includes steps S10 to S40.

[0021] Step S10: Based on the status of the wind-storage combined power generation system and grid connection assessment indicators, construct a scheduling optimization model with the objective of optimizing the economic operating cost of the wind farm. The economic operating cost includes assessment fees incurred due to exceeding grid-connected power fluctuation limits.

[0022] It should be noted that the zero-assessment constraint refers to an operational boundary condition that requires the power plant's grid-connected power to never violate the grid's assessment rules, thereby ensuring that the economic penalty cost incurred due to power fluctuation exceeding limits is zero.

[0023] It should be noted that the wind-storage combined generation system includes both the wind power system and the energy storage system. Its status includes data that can be monitored in real time, such as the wind turbine output power collected by anemometers and power transmitters, the state of charge (SOC), charge and discharge power, and capacity limits of the energy storage device obtained by the battery management system (BMS) or energy storage converter (PCS), and the grid connection point voltage and frequency measured by the grid-side power quality analyzer.

[0024] It should be noted that grid connection assessment indicators are typically defined as quantitative standards such as the rate of change of active power and the power prediction deviation threshold. These indicators can be obtained from normative documents issued by the power grid dispatching agency. When constructing a dispatch optimization model, the aforementioned state data and assessment indicators must be used as inputs. The objective function is to minimize the economic operating cost. This cost may include the routine operation and maintenance costs of wind turbines and energy storage equipment, the cost of energy storage cycle aging losses, and assessment fees caused by grid-connected power fluctuations exceeding the assessment indicators, etc. The specific costs included in the application should be considered on a case-by-case basis.

[0025] It should be noted that the assessment cost can be modeled as a function of the magnitude and duration of the excess power, according to the grid pricing rules. The constraints of the optimization model cover the physical limits of the energy storage system, such as maximum charging and discharging power, upper and lower limits of available capacity, and wind farm operation constraints, such as turbine start-up and shutdown states and feasible active power output ranges. The hardware required to implement this model may include a Supervisory Control and Data Acquisition (SCADA) system, a programmable logic controller (PLC), or an industrial computer, which runs optimization algorithms, such as model predictive control (MPC) algorithms, linear programming, or nonlinear programming solvers, to continuously calculate the optimal power setpoints for wind turbines and energy storage devices during future scheduling periods.

[0026] Among alternative options, energy storage devices can include lithium-ion batteries, flywheel energy storage, or supercapacitors; the optimization algorithm can also be replaced with a heuristic algorithm, such as a genetic algorithm or a particle swarm optimization algorithm; furthermore, the grid connection assessment indicators can be expanded to include other grid connection technical requirements such as voltage deviation and frequency response. By executing this scheduling optimization model, the system can output control commands to the wind turbine pitch system and energy storage converter in real time, thereby actively smoothing grid-connected power fluctuations, ensuring that the grid-connected power change rate continuously meets the assessment standards, and ultimately achieving the operational goal of zero assessment quantity.

[0027] Specifically, this application provides a specific implementation method for step S10, please refer to... Figure 2 .exist Figure 2 In step S10, steps S11 to S13 are included.

[0028] Step S11: Obtain the wind-storage combined power generation system model based on power balance constraints, wind turbine output constraints, wind curtailment constraints, and energy storage system constraints.

[0029] It should be noted that power balance constraints refer to the real-time energy conservation relationship that a wind-storage combined power generation system must satisfy at the grid connection point, between its total output power, internal losses, and the planned power fed into the grid. Wind turbine output constraints refer to the restrictions on the range and rate of change of the active power output of wind turbine units. These generally include upper and lower limits for output and ramp rate constraints. Curtailment constraints allow wind turbine output to be lower than its current maximum generating power as required by optimized dispatch instructions, i.e., actively limiting wind power output. Energy storage system constraints refer to the set of dynamic equations describing the physical limits of energy storage device operation.

[0030] It is understandable that obtaining a model for a wind-storage combined power generation system refers to integrating the mathematical expressions of all the aforementioned constraints into a unified mathematical model framework that can be solved by a computer. This process is typically completed in the central processing unit of the energy management system (EMS), and the formal definition of the model is achieved by calling built-in mathematical modeling libraries (such as Python's Pyomo library or MATLAB's Optimization Toolbox) or connecting to interfaces of external commercial optimization solvers (such as Gurobi or CPLEX). In alternative implementations, for systems with significant nonlinear characteristics, a physics-based simulation model (such as the Modelica model) or a data-driven surrogate model (such as a neural network model) trained using historical operating data can be established to approximately represent the aforementioned constraint relationships.

[0031] Therefore, various constraints need to be established before proceeding to step S11. First, power balance constraints are obtained based on the actual power of the wind turbines in the wind farm, the charging and discharging power of the energy storage units, and the grid-connected power of the wind farm.

[0032] Understandably, the total actual power of a wind farm's turbines refers to the sum of the real-time active power output of all operating wind turbine units, measured by power transmitters or smart meters installed at the outlet or collection point of each turbine, and aggregated through a data acquisition and monitoring system. The charging and discharging power of the energy storage unit refers to the instantaneous power exchanged between the energy storage system and the grid; it is positive during discharging and negative during charging, measured by bidirectional energy meters at the energy storage converter or connection point. The grid-connected power of the wind farm refers to the total active power injected into the public grid connection point of the entire wind-storage integrated system, measured by high-precision gate energy meters at the grid connection point. Establishing this constraint involves constructing the equations for the above three quantities within any scheduling period (t) based on the law of conservation of energy: (1).

[0033] in, t represents the grid-connected power of the wind farm at time t, which is the variable that will be evaluated in subsequent steps. Let be the actual power output of the i-th wind turbine at time t; The charge / discharge power of the energy storage unit at time t; N is the number of wind farm units.

[0034] Secondly, based on the actual power, curtailment power, and predicted generating power of the wind turbines in the wind farm, the power output constraints and curtailment constraints of the wind turbines are obtained.

[0035] It should be noted that the predicted generating power refers to the maximum theoretical output that a wind turbine may reach within a certain future period, calculated based on numerical weather forecasts and wind turbine power curves, and is generated by a wind power prediction system. In this application, the wind power prediction system is not limited, as long as it can obtain the predicted generating power. Curtailment power refers to the difference between the predicted generating power of a wind turbine and the actual power command value (or planned value) within a specific period to meet dispatch instructions or system constraints; its value is non-negative. Therefore, the equations and inequalities relating wind turbine output constraints can be obtained: (2); (3).

[0036] in, Let be the wind curtailment power of the i-th wind turbine at time t; Let be the predicted generating power of the i-th wind turbine at time t. Meanwhile, the wind curtailment constraint is: (4) That is, it should not exceed the predicted power output.

[0037] Finally, based on the charging and discharging power of the energy storage unit and its initial capacity, the constraints of the energy storage system are obtained. It should be noted that the definition of charging and discharging power is the same as before. The charging and discharging power should be within the allowable range. The initial capacity refers to the electrical energy stored in the energy storage device at the start of the optimization cycle. It is necessary to ensure that the charging and discharging power of the energy storage system is balanced after one day, so that the energy storage system can restore its initial capacity configuration. Therefore, the relationship of the charging and discharging constraints can be obtained as follows: (5); (6); (7).

[0038] in, This represents the maximum allowable charge and discharge power for energy storage. The charging and discharging efficiency of the energy storage unit; These are the minimum and maximum remaining capacity of energy storage allowed during the scheduling process; This refers to the capacity of the energy storage at the initial moment of scheduling.

[0039] Step S12: Obtain the assessment power model based on the power change value exceeding the limit assessment power and the power change rate exceeding the limit assessment power under different preset scales.

[0040] It should be noted that the preset scale refers to the time statistical window explicitly stipulated by the power grid dispatching agency in the grid connection operation management rules or technical standards. Common scales include 1 minute, 5 minutes, 15 minutes, etc. Different scales correspond to different calculation cycles for assessment indicators. The power change value exceeding the limit assessment amount refers to the penalty amount accumulated when the absolute value of the deviation between the actual output power of the power plant's grid connection point and the dispatch plan (or allowable range) continuously exceeds the specified threshold within the preset time scale. The power change rate exceeding the limit assessment amount specifically refers to the penalty amount accumulated when the rate of change of the power plant's grid-connected power exceeds the specified limit.

[0041] Specifically, the two detailed rules require that the maximum change in active power of an offshore wind farm over 10 minutes be 1 / 3 of its installed capacity, and the maximum change over 1 minute be 1 / 10 of its installed capacity. The power smoothing requirements for the two time scales mentioned in the rules, namely the 1-minute and 10-minute scales, are expressed mathematically as follows.

[0042] It should be noted that the power change value of a wind farm exceeding the limit on a 1-minute scale is assessed based on the amount of electricity involved. for: (8); (9); (10).

[0043] Where t is the current time; This represents the total number of consecutive 1-minute intervals within the assessment period. This is a parameter for assessing changes in active power of wind power; the default value is 1. Let t be the grid-connected power. The power change value of the wind farm's grid-connected power on a 1-minute scale: Wind farm installed capacity, This refers to the capacity of a single wind turbine.

[0044] Understandably, when the assessment period is one calendar day, within the 1440 minutes of the day, the total number of adjacent 1-minute intervals is 1439. The value is 1439. The difference between the actual power change value and the limit is calculated minute by minute. Only when the difference is greater than zero is it considered to be exceeding the limit. The excess amounts corresponding to all minutes exceeding the limit are summed and then multiplied by the time unit conversion factor to finally obtain the cumulative assessment electricity in megawatt-hours (MWh). The assessment parameters can also be adjusted to different penalty coefficients according to the specific rules of local power grids.

[0045] It should be noted that the power change value of the wind farm exceeding the limit on a 10-minute scale is subject to assessment for the amount of electricity. for: (11); (12).

[0046] Understandable This refers to the number of consecutive, non-overlapping fixed windows of length 10 minutes within the assessment period. When the assessment period is one calendar day, the effective t-sequences are 1, 11, 21, etc., up to the maximum value that satisfies the condition, resulting in a total of 143. At this point, The value is 143. The rest of the content is largely the same as the above, and will not be repeated here.

[0047] It should be noted that the two revised detailed rules for the Southern Region state that: The active power variation of wind farms will be assessed daily, with the average value of the power variation difference per minute over a 10-minute period used to calculate the assessment amount. This calculation is performed on a rolling basis and assessed daily. This assessment indicator can be mathematically expressed as follows: the amount of electricity assessed for exceeding the limit in the power variation rate of the wind farm in minute t. for: (13); (14).

[0048] in, This refers to the number of sliding windows with a duration of 10 minutes within the assessment period. When the assessment period is one calendar day, starting from t=1, the sliding window moves once per minute until the end of the calendar day at t=1430, resulting in a total of 1430 sliding windows. At this point, It is 1430. Let be the power change rate of the wind farm in minute t; This is the limit value for the rate of change of power.

[0049] It is understandable that equation (13) calculates the average minute-level power change within a continuous 10-minute time window starting from minute t. The numerator calculates the power change from minute t+j. The absolute value of power change within the 1-minute window from 1 minute to the (t+j)-th minute. The summation of j from 1 to 10 represents the accumulation of the absolute power change between each adjacent minute within this 10-minute window. Finally, dividing by 10 yields the average of these 10 one-minute absolute values, which is taken as the power change rate at minute t. This reflects the assessment rule of taking the average of the power change difference per minute within a 10-minute window.

[0050] Step S13: Obtain the assessment cost model based on the assessment power model, and construct a scheduling optimization model with the goal of optimizing the economic operating cost of the wind farm based on the assessment cost model and the wind-storage combined power generation system model.

[0051] Understandably, the purpose of this step is to transform quantified technical assessment indicators into economic costs and combine them with system physical constraints to form a complete mathematical optimization problem. Based on the assessment power volume model, the assessment cost model is obtained by converting the assessment power volume, established in step S12 (in physical quantities), into costs in monetary terms. Specifically, the assessment power volume model is as described above, while the assessment cost model multiplies the aforementioned power volume by the corresponding assessment unit price. Finally, all the equality and inequality constraints contained in the wind-storage combined power generation system model obtained in step S11 are integrated with the aforementioned assessment cost model to form a complete constrained optimization problem. The system model defines the feasible region of the decision variables, while the cost model defines the optimization objective.

[0052] Specifically, this application provides a detailed implementation of step S13. Please refer to... Figure 3 ,exist Figure 3 In step S13, steps S131 to S134 are included.

[0053] Step S131: Obtain the assessment fee based on the power change value exceeding the limit assessment amount at the 1-minute and 10-minute scales of the wind farm and the power change rate exceeding the limit assessment amount at the t-th minute.

[0054] It should be noted that, as mentioned above, the assessment fee model is as follows: (15); (16).

[0055] in, The assessed electricity generated by the wind farm in one day of operation; This is just an example for the purpose of evaluating electricity billing unit prices; The assessed electricity generated by the wind farm in one day of operation.

[0056] Step S132: Obtain the grid-connected electricity consumption of the wind farm after one day of operation based on the wind-storage combined power generation system model, and then generate grid-connected consumption profit.

[0057] Understandably, this step aims to model the core revenue source on the generation side, grid-connected electricity revenue, as a positive revenue term in the economic objective function of the optimization model. Understandably, using the mathematical model of the wind-storage combined power generation system established in step S11, the total electricity expected to be transmitted to and consumed by the grid within a future scheduling cycle (24 hours) is calculated. In the mathematical model, the grid-connected consumption electricity corresponds to the time integral of the planned grid-connected power, a decision variable.

[0058] Therefore, the profit model for grid connection and absorption is as follows: (17); (18).

[0059] in, To generate profit for a wind farm after one day of operation and grid connection; The unit price for electricity consumed by grid connection is for illustrative purposes only. This refers to the amount of electricity consumed by the wind farm after one day of operation and grid connection.

[0060] Step S133: Obtain the amount of wind power curtailed in one day of wind farm operation based on the wind-storage combined power generation system model, and then generate the wind curtailment penalty fee.

[0061] Understandably, the purpose of this step is to quantify the value of wind energy resources actively relinquished due to dispatch needs within the economic optimization model, thereby establishing an economic trade-off between minimizing assessment costs and maximizing power generation revenue. This step is a crucial component of economic operating costs, ensuring the economic viability of the dispatch scheme. The expected total amount of wind curtailment within a dispatch cycle can be defined and calculated. Curtailed wind power refers to the difference between the maximum available power output of wind turbines and the actual planned output during a specific period.

[0062] Therefore, the wind curtailment penalty cost model is as follows: (19); (20).

[0063] in, The penalty fee for one day of wind curtailment at a wind farm; The unit price for wind curtailment penalties; This refers to the amount of wind power curtailed during one day of wind farm operation.

[0064] Step S134: Construct a scheduling optimization model with the goal of optimizing the economic operating cost of wind farms based on assessment costs, grid connection and consumption profits, and wind curtailment penalty costs.

[0065] It is understandable that the objective function for optimizing the economic operating cost of a wind farm can be obtained based on the positive and negative revenue characteristics of these three factors: (twenty one).

[0066] It is understandable that all the equality and inequality constraints included in the wind-storage combined power generation system model, integrated with the aforementioned objective function, constitute a complete constrained optimization problem. The system model defines the feasible region of the decision variables, while the objective function for optimizing the economic operating cost of the wind farm defines the optimization objective.

[0067] Step S20: Introduce zero performance constraint into the scheduling optimization model to obtain a scheduling optimization model containing zero performance constraint; the zero performance constraint is configured to limit the change value and rate of change of active power at the grid connection point of the wind farm to within the threshold range specified by the grid connection assessment index.

[0068] Understandably, the original model treats the assessment as a cost item in the objective function for economic trade-offs, and its optimal solution may be to accept a small amount of assessment in exchange for higher power generation revenue. The introduced zero assessment constraint transforms this condition into a hard constraint of mathematical inequality that must be satisfied, thereby directly eliminating any scheduling plan that would lead to assessment from the solution space and strictly ensuring that technical standards are met.

[0069] Specifically, according to the detailed rules, a first zero-evaluation constraint, a second zero-evaluation constraint, and a third zero-evaluation constraint are set in the scheduling optimization model to generate a scheduling optimization model containing zero-evaluation constraints.

[0070] The first zero assessment constraint restricts the absolute value of the grid-connected power change at adjacent times from not exceeding a first predetermined proportion of the installed capacity, as shown in equation (22); the second zero assessment constraint restricts the power change value over 10 minutes from not exceeding a second predetermined proportion of the installed capacity, as shown in equation (23).

[0071] (twenty two).

[0072] (twenty three).

[0073] Understandably, the above basis is that the maximum change in active power of an offshore wind farm over 10 minutes is 1 / 3 of the wind farm's installed capacity, and the maximum change in active power over 1 minute is 1 / 10 of the wind farm's installed capacity.

[0074] Among them, the third zero-assessment constraint limits the power change rate per minute within 10 minutes of rolling to no more than the power change rate limit: (twenty four).

[0075] Step S30: Solve the scheduling optimization model with zero performance constraints to generate wind curtailment control commands for wind turbines and charging / discharging control commands for energy storage systems.

[0076] Specifically, a mathematical programming solver is used to solve the scheduling optimization model with zero performance constraints to obtain the optimal wind curtailment power and energy storage charging and discharging power at each time. Based on the optimal wind curtailment power at each time, wind curtailment control commands are generated to control the actual output of each wind turbine generator. Based on the optimal energy storage charging and discharging power at each time, charging and discharging control commands are generated to control the charging or discharging state of the energy storage system.

[0077] Step S40: Based on the wind curtailment control command and the charge / discharge control command, coordinate the control of the wind turbine generators and energy storage system in the wind farm so that the grid-connected power of the wind farm meets the grid connection assessment indicators.

[0078] Understandably, the control center uses a site monitoring network, such as an industrial Ethernet network, to send the time-stamped wind curtailment control commands and charging / discharging control commands generated in step S30 to the wind farm central controller, or the control units of individual wind turbine controllers and energy storage converters. The command format follows standard communication protocols, and the content is a specific active power setpoint or limit. Understandably, after receiving a command, the wind turbine's main controller adjusts the pitch system and generator torque to control the actual output below the target value set in the command, thus achieving the specified curtailment power. Upon receiving the command, the energy storage system controls the switching states of the power semiconductor devices to precisely execute charging or discharging operations, tracking the given power setpoint.

[0079] Furthermore, the present invention sets up the following five simulation schemes to verify its effectiveness: Scheme 1 is a wind farm without zero assessment constraints and without energy storage systems, where wind power is fully absorbed; Scheme 2 is a wind farm with zero assessment constraints, where grid-connected wind power is suppressed only through wind curtailment; Scheme 3 is a wind farm with zero assessment constraints, where grid-connected wind power is suppressed only through the charging and discharging of energy storage systems, but it cannot achieve complete zero assessment constraints for arbitrary waveforms under confirmed capacity configuration; Scheme 4 is a wind-storage combined system that achieves complete wind power absorption without mandatory zero assessment constraints, using assessment penalties as the objective function; Scheme 5 is a wind-storage combined system with zero assessment constraints, where the grid-connected power of the wind farm meets the zero assessment constraints through partial wind curtailment and the charging and discharging of the energy storage system. The results are as follows: It should be noted that the results obtained from Option 1 are as follows: Figures 4 to 7 As shown in Table 1: Figure 4 The simulation experiment used raw wind power data from a wind farm selected for one day. In Scheme 1, the wind farm was directly and completely connected to the grid for power consumption. It can be seen that... Figures 5 to 7 In the diagram, the red horizontal lines represent the indicator limits. Exceeding these limits triggers assessment penalties, resulting in assessment fees. Option 1 explains that failing to impose zero assessment constraints will lead to exceeding the limits under the three assessment indicators. Specific figures are detailed in Table 1, with the resulting assessment electricity volume accounting for 16.03% of the wind farm's ideal daily grid-connected electricity volume.

[0080] Table 1:

[0081] It should be noted that the results obtained from Option 2 are as follows: Figures 8 to 12 As shown in Table 2: Figure 8 The red line represents the actual grid-connected power curve. It can be seen that after wind curtailment, the red line is below the black line represented by the predicted power. Figure 9 This refers to the amount of wind curtailment required by a wind farm to achieve grid-connected power without triggering assessments under zero assessment constraints. Figures 10 to 12This indicates that, after the zero assessment constraint, the wind farm was able to avoid triggering the assessment through wind curtailment. For specific values, please refer to Table 2. The amount of wind curtailment required to achieve the zero assessment constraint accounts for 4.16% of the wind farm's ideal daily grid-connected total electricity.

[0082] Table 2:

[0083] It should be noted that the results obtained from Scheme 3 are as follows: Figures 13 to 20 As shown in Table 3: Figure 13 The red line represents the actual grid-connected power curve. It can be seen that part of the red line is above the black line representing the predicted power, which indicates that the energy storage system has achieved valley filling through discharge, further improving the wind power suppression effect. Figure 14 and Figure 15 This indicates that the energy storage system is operating normally and has not exceeded the set size. Figures 16 to 18 This indicates that, after the zero-assessment constraint, the charging and discharging of the energy storage system was used to prevent the wind farm from triggering the assessment. Specific values ​​are detailed in Table 3. It should be noted that, for the predicted wind power output on this day, the energy storage system has a critical capacity; once it falls below this capacity, [the following occurs]. Figure 19 and Figure 20 The energy storage system abnormally exceeded the set value, making it impossible to achieve the zero assessment constraint.

[0084] Table 3:

[0085] It should be noted that the results obtained from Scheme 4 are as follows: Figure 21 and Figure 22 As shown, it should be noted that, under the same critical capacity, if zero performance constraints are not enforced, energy storage systems will still exceed the performance limits even if the capacity meets the requirements.

[0086] It should be noted that the results obtained from Option 5 are as follows: Figures 23 to 30 As shown, all constraints are within limits. Given that most wind farms may not have sufficiently large energy storage systems, or that existing wind farms cannot be optimized for capacity at the planning level, this solution utilizes energy storage systems of any capacity for charging and discharging, as well as partial wind curtailment. This allows for completely zero-evaluation constraints on wind power data of any waveform on any day, ensuring that grid-connected wind power does not trigger operational assessments and reducing the cost of operating wind farms.

[0087] The core innovation of this application in achieving zero performance evaluation lies in constructing an economic dispatch model that allows for complete coordination between proactive wind curtailment and energy storage control. Specifically, firstly, by internalizing performance evaluation costs as expenses and simultaneously incorporating wind curtailment commands and energy storage charging / discharging commands as optimization variables into the model, a dispatch model is constructed with the goal of optimizing total operating costs. This fundamentally establishes a quantitative trade-off between economic efficiency and grid connection compliance, enabling the system to automatically optimize among multiple objectives such as reducing performance evaluation and reducing wind curtailment / energy storage losses. Subsequently, the zero performance evaluation constraint introduced into the model sets strict boundaries for grid-connected power variations, and the wind curtailment control variable in the model is one of the key guarantees for meeting these hard boundaries. When predicted fluctuations may exceed limits, the model can proactively invoke wind curtailment commands, working in conjunction with energy storage regulation to ensure that the power change value and rate of change never exceed limits. This solves the fundamental problem that relying solely on energy storage may fail to meet stringent constraints due to capacity or power limitations. Ultimately, the cooperative control commands generated by solving the model, because they pre-include optimization decisions that require an economic cost to achieve absolute compliance, physically achieve reliable and precise control of grid-connected power.

[0088] Compared with existing technologies, this solution not only proposes a zero assessment target through constraint setting, but also, through the wind curtailment control option built into the model, forms a necessary and sufficient condition for achieving the target with energy storage. This ensures that the zero assessment target can be achieved in wind power active power variation scenarios where traditional wind-storage combined systems cannot fully absorb the wind power while simultaneously meeting the assessment constraints, significantly improving the certainty and safety of the station operation.

[0089] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A new energy power generation dispatch method oriented towards zero performance constraints, characterized in that, include: Based on the status of the wind-storage combined power generation system and grid connection assessment indicators, a scheduling optimization model is constructed with the goal of optimizing the economic operating cost of the wind farm; wherein, the economic operating cost includes assessment costs caused by exceeding the grid-connected power fluctuation limit; Introducing zero-performance constraint into the scheduling optimization model yields a scheduling optimization model with zero-performance constraint; the zero-performance constraint is configured to limit the change value and rate of change of active power at the wind farm grid connection point to the threshold range specified by the grid connection assessment index. Solve the scheduling optimization model containing zero performance constraints to generate wind curtailment control commands for wind turbines and charging / discharging control commands for energy storage systems; Based on the wind curtailment control command and the charge / discharge control command, the wind turbine generators and energy storage system in the wind farm are controlled in a coordinated manner so that the grid-connected power of the wind farm meets the grid connection assessment indicators.

2. The new energy power generation dispatch method for zero performance constraints as described in claim 1, characterized in that, The grid connection assessment indicators include: excess power consumption under multiple preset time scales, and excess power consumption due to power change rate. Based on the status of the wind-storage combined power generation system and the grid connection assessment indicators, a scheduling optimization model is constructed with the goal of optimizing the economic operating cost of the wind farm, including: Based on power balance constraints, wind turbine output constraints, wind curtailment constraints, and energy storage system constraints, a model of a wind-storage combined power generation system is obtained. Based on the power change value exceeding the limit and the power change rate exceeding the limit under different preset scales, obtain the assessment power model; Based on the assessment power model, an assessment cost model is obtained, and based on the assessment cost model and the wind-storage combined power generation system model, a scheduling optimization model is constructed with the goal of optimizing the economic operating cost of the wind farm.

3. The new energy power generation dispatch method for zero performance constraints as described in claim 2, characterized in that, Based on power balance constraints, wind turbine output constraints, and energy storage system constraints, a model for the wind-storage combined power generation system is obtained. This previously included: Based on the actual power of wind turbines in the wind farm, the charging and discharging power of energy storage units, and the grid-connected power of the wind farm, power balance constraints are obtained; Based on the actual power, curtailment power and predicted generating power of wind turbines in wind farms, wind turbine output constraints and curtailment constraints are obtained. Based on the charging and discharging power of the energy storage unit and its initial capacity, the constraints of the energy storage system are obtained.

4. The new energy power generation dispatch method for zero performance constraints as described in claim 2, characterized in that, The preset scales include a 1-minute scale and a 10-minute scale; the power change value exceeding the limit in the wind farm under the 1-minute scale is assessed for the amount of electricity. for: ； ； Where t is the current time; This represents the total number of consecutive 1-minute intervals within the assessment period. This is a parameter for assessing changes in active power of wind power; the default value is 1. Let t be the grid-connected power. The power change value of the wind farm's grid-connected power on a 1-minute scale: Wind farm installed capacity; Exceeding the limit for power variation in wind farms on a 10-minute scale for: ； ； in, The number of consecutive, non-overlapping fixed windows of length 10 minutes within the assessment period; the power change rate of the wind farm exceeding the assessment limit in minute t. for: ； ； in, The number of sliding windows with a duration of 10 minutes within the assessment period; Let be the power change rate of the wind farm in minute t; This is the limit value for the rate of change of power.

5. The new energy power generation dispatch method for zero performance constraints as described in claim 2, characterized in that, Based on the assessed power generation model, an assessed cost model is obtained, and based on the assessed cost model and the wind-storage combined power generation system model, a scheduling optimization model is constructed with the objective of optimizing the economic operating cost of the wind farm, including: The assessment fee is obtained based on the power change value exceeding the limit assessment electricity and the power change rate exceeding the limit assessment electricity at minute t of the wind farm at 1-minute and 10-minute scales. Based on the wind-storage combined power generation system model, the grid-connected and consumed electricity of the wind farm after one day of operation is obtained, and then the grid-connected and consumed profit is generated. Based on the wind-storage combined power generation system model, the amount of wind curtailment generated in one day of wind farm operation is obtained, and then the wind curtailment penalty fee is generated. Based on assessment costs, grid connection and consumption profits, and wind curtailment penalty costs, a scheduling optimization model is constructed with the goal of optimizing the economic operating cost of wind farms.

6. The new energy power generation dispatch method for zero performance constraints as described in claim 1, characterized in that, Introducing zero-evaluation constraints into the scheduling optimization model yields a scheduling optimization model containing zero-evaluation constraints, including: In the scheduling optimization model, a first zero-evaluation constraint, a second zero-evaluation constraint, and a third zero-evaluation constraint are set to generate a scheduling optimization model containing zero-evaluation constraints. Among them, the first zero assessment quantity constraint restricts the absolute value of the grid-connected power change at adjacent times from not exceeding a first predetermined proportion of the installed capacity; the second zero assessment quantity constraint restricts the power change value within 10 minutes from not exceeding a second predetermined proportion of the installed capacity; and the third zero assessment quantity constraint restricts the power change rate per minute within a rolling 10 minutes from not exceeding the power change rate limit.

7. The new energy power generation dispatch method for zero performance constraints as described in claim 1, characterized in that, Solving the scheduling optimization model with zero performance constraints generates wind curtailment control commands for wind turbines and charging / discharging control commands for the energy storage system, including: A mathematical programming solver is used to solve the scheduling optimization model with zero performance constraints to obtain the optimal wind curtailment power and energy storage charging and discharging power at each time point. Based on the optimal wind curtailment power at each time point, wind curtailment control commands are generated to control the actual output of each wind turbine generator set. Based on the optimal energy storage charging and discharging power at each time point, charging and discharging control commands are generated to control the charging or discharging state of the energy storage system.

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