A power grid unit output uncertainty scheduling method, system, device and medium

Abstract

The present application relates to the technical field of power system, disclose a kind of power grid unit output uncertainty scheduling method, system, equipment and medium, the present application considers multiple uncertain factors of power grid system, and multiple uncertain factors are modeled, generate multiple uncertainty scenarios using the operating function of uncertain factor, and the uncertainty scenarios are reduced, also minimize the optimization target of power grid output reduction cost, and with power grid system steady state as constraint condition, construct power grid system scheduling optimization model, the reduced typical scenario set is used to optimize and solve power grid system scheduling optimization model, determine the optimal scheduling scheme of power grid system according to optimal solution, so that the scheduling scheme of power grid system adapts to the change of uncertain factor, improve the transmission capacity and flexibility of power grid system, and can be adapted to the fluctuation of renewable energy of power grid system.

Description

Technical Field

[0001] This invention relates to the field of power system technology, and in particular to a method, system, equipment and medium for scheduling uncertain power output of power grid units. Background Technology

[0002] With the increasing penetration rate of renewable energy sources such as wind power and photovoltaics, the proportion of new energy in the power system is rising rapidly. However, their inherent volatility and intermittency bring new technical challenges to the stable operation of the power system. Traditional power systems relying on coal and gas have good flexibility and can cope with load changes. However, with the increase in the installed capacity of new energy, these regulating thermal power units are gradually being phased out, and the demand for grid flexibility is becoming increasingly urgent.

[0003] With a high proportion of renewable energy integration, the power system needs to incorporate smart grid technology to improve equipment utilization and the flexibility of transmission and distribution networks. By introducing dynamic line and transformer rating technologies, intelligent dispatching of the power system can be achieved under varying weather conditions, thereby enhancing the adaptability of the power system without increasing infrastructure, and further ensuring the economic efficiency and security of the power system.

[0004] Existing technical solutions mainly include static rated capacity, redundancy construction and equipment expansion, energy storage systems, demand response, distributed power sources, and microgrids. However, due to the many uncertainties associated with renewable energy in power grid systems, the output of the power grid is prone to uncertainty. Furthermore, current technical solutions are difficult to adjust to changes in these uncertainties, resulting in poor transmission capacity and flexibility of the power grid, making it difficult for the power grid to adapt to the volatility of renewable energy. Summary of the Invention

[0005] In view of this, in order to solve the above-mentioned technical problems, the present invention provides a method for scheduling the output uncertainty of power grid units.

[0006] The first aspect of this invention provides a method for scheduling power grid unit output under uncertainties, comprising:

[0007] Model multiple uncertainties in the power grid system and determine the operating function of each uncertainty.

[0008] Multiple uncertainty scenarios are generated based on the operating functions of each uncertainty factor;

[0009] The uncertain scenarios are reduced to obtain multiple typical uncertain scenarios, and a set of typical scenarios is constructed.

[0010] With the goal of minimizing the cost of power grid output reduction and with the steady state of the power grid system as a constraint, a power grid system dispatch optimization model is constructed.

[0011] The power grid system scheduling optimization model is optimized based on the typical scenario set, and the optimal scheduling scheme of the power grid system is determined based on the optimal solution.

[0012] Preferably, the uncertainties include changes in heat transfer of transmission lines, changes in transformer load temperature, changes in wind speed of wind turbines, changes in load demand, and changes in solar radiation.

[0013] Preferably, the modeling of multiple uncertainties in the power grid system and the determination of the operating function for each uncertainty include:

[0014] Based on the heat balance equation function of the transmission line, determine the operating function of the heat transfer change of the transmission line;

[0015] Based on the dynamic equation function of the top oil temperature using a discrete-time model, the operating function for the load temperature change of the transformer is determined.

[0016] Based on the probability density function of wind speed of the wind turbine, determine the operating function of wind speed change of the wind turbine.

[0017] Based on the probability density function of load demand, determine the operating function for the changes in load demand;

[0018] The operating function of the solar radiation variation is determined based on the Beta probability distribution function of solar radiation.

[0019] Preferably, the method further includes:

[0020] Multiple uncertainty scenarios are generated through Monte Carlo simulation based on the operating functions of each uncertainty factor.

[0021] Preferably, the method further includes:

[0022] The k-means clustering algorithm is used to reduce the uncertainty of multiple scenarios.

[0023] Preferably, the power grid system dispatch optimization model includes an objective function; wherein the objective function is:

[0024]

[0025] In the formula, OF represents the cost of reducing grid output. , , All are weighting coefficients. Let be the probability of scenario s occurring. Let g be the power generation capacity of unit g under time t and scenario s. For the assembly of generator units, A set of typical scenarios, Total time Let i be the unit load shedding value. Let i be the load shedding power of load node i at time t and scenario s. For the unit's unutilized costs, Let w be the unutilized wind power of the wind farm at time t and scenario s.

[0026] Preferably, the power grid system dispatch optimization model includes constraints; wherein the constraints include upper and lower limits of thermal power unit output, power flow constraints, transmission line thermal constraints, transformer thermal constraints, upper and lower limits of bus voltage, load constraints, and wind power reduction constraints.

[0027] Secondly, the present invention also provides a power grid unit output uncertainty dispatching system, comprising:

[0028] The factor modeling module is used to model multiple uncertainties in the power grid system and determine the operating function of each uncertainty.

[0029] The scenario generation module is used to generate multiple uncertain scenarios based on the running functions of various uncertain factors;

[0030] The scenario reduction module is used to reduce multiple uncertain scenarios to obtain multiple typical uncertain scenarios and construct a typical scenario set.

[0031] The model building module is used to construct a power grid system scheduling optimization model with the goal of minimizing the cost of power grid output reduction and with the steady state of the power grid system as the constraint.

[0032] The scheduling optimization module is used to optimize the power grid system scheduling optimization model based on the typical scenario set, and determine the optimal scheduling scheme of the power grid system based on the optimal solution.

[0033] Thirdly, the present invention also provides an electronic device, the electronic device including a memory and a processor, the memory storing a computer program, the computer program being executed by the processor causing the processor to perform the steps of the grid unit output uncertainty scheduling method as described in the first aspect.

[0034] Fourthly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed, implements the steps of the power grid unit output uncertainty scheduling method as described in the first aspect.

[0035] As can be seen from the above technical solutions, this invention considers multiple uncertainties in the power grid system, models these uncertainties, generates multiple uncertain scenarios using the operating functions of these uncertainties, reduces these uncertain scenarios, minimizes the cost of power grid output reduction as the optimization objective, and constructs a power grid system scheduling optimization model with the power grid system steady state as a constraint. The optimized model is then solved using a set of reduced typical scenarios, and the optimal scheduling scheme for the power grid system is determined based on the optimal solution. This allows the power grid system scheduling scheme to adapt to changes in uncertainties, improves the transmission capacity and flexibility of the power grid system, and can adapt to the volatility of renewable energy sources. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 This invention provides an application environment for a power grid unit output uncertainty scheduling method according to an embodiment of the invention.

[0038] Figure 2 A flowchart of a power grid unit output uncertainty scheduling method provided in an embodiment of the present invention;

[0039] Figure 3 This is a schematic diagram of a power grid unit output uncertainty dispatching system provided in an embodiment of the present invention;

[0040] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0041] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0042] Existing technical solutions mainly include static rated capacity, redundancy construction and equipment expansion, energy storage systems, demand response, distributed power sources, and microgrids. However, due to the many uncertainties associated with renewable energy in power grid systems, the output of the power grid is prone to uncertainty. Furthermore, current technical solutions are difficult to adjust to changes in these uncertainties, resulting in poor transmission capacity and flexibility of the power grid, making it difficult for the power grid to adapt to the volatility of renewable energy.

[0043] The static rated capacity ignores dynamic changes in meteorological conditions, and in most cases, the rated value is conservative, resulting in low actual capacity utilization of transmission equipment. This conservative strategy limits the system's scheduling flexibility, especially during periods of large fluctuations in renewable energy output and peak loads, which can easily lead to transmission bottlenecks. However, the static rated capacity is underutilized.

[0044] Redundancy and equipment expansion require significant infrastructure investment and are constrained by environmental protection, land resources, and construction timelines. Furthermore, redundancy can lead to resource waste and prolonged equipment downtime, making it less than ideal for rapidly improving system flexibility in the short term. However, relying on redundancy is costly.

[0045] 3. Energy storage systems are expensive, especially large-scale battery energy storage systems. In addition, energy storage systems have limited capacity and lifespan, frequent charging and discharging accelerate battery aging, and their management and optimization scheduling are highly complex.

[0046] The effectiveness of demand response depends on user participation and response speed, making it highly uncertain. Furthermore, demand response is highly dependent on user behavior, and its actual effect may be insufficient during peak hours, making it difficult to completely replace regulating power sources or energy storage. However, the effectiveness of demand response is relatively poor and highly uncertain.

[0047] Coordination and control of distributed power sources and microgrids are complex and costly. Furthermore, microgrids are small in scale and have limited capacity to support large-scale load demands; their flexibility and stability are difficult to match those of traditional large power grids.

[0048] In summary, current technical solutions are unable to adapt to changes in uncertainties within the power grid system, resulting in poor transmission capacity and flexibility, and making it difficult for the power grid system to adapt to the volatility of renewable energy.

[0049] Therefore, the grid unit output uncertainty scheduling method provided in this application embodiment can be applied to, for example, Figure 1In the application environment shown, each node of the power grid system communicates with server 102 via a network. A data storage system can store the data that server 102 needs to process. The data storage system can be integrated onto server 102 or placed in the cloud or on other network servers. Server 102 models multiple uncertainties in the power grid system, determines the operating functions of each uncertainty, generates multiple uncertainty scenarios based on the operating functions of each uncertainty, reduces these uncertain scenarios to obtain multiple typical uncertainty scenarios, and constructs a set of typical scenarios. With minimizing the cost of power grid output reduction as the optimization objective and the steady state of the power grid system as the constraint, a power grid system scheduling optimization model is constructed. Based on the set of typical scenarios, the power grid system scheduling optimization model is optimized and solved, and the optimal scheduling scheme for the power grid system is determined based on the optimal solution. Server 102 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0050] like Figure 2 As shown in the embodiments of this application, a method for scheduling power grid unit output uncertainty is provided, which is applied to... Figure 1 Taking server 102 as an example, the explanation includes the following steps S1 to S5. Wherein:

[0051] Step S1: Model multiple uncertainties in the power grid system and determine the operating function of each uncertainty.

[0052] Uncertain factors include changes in heat transfer of transmission lines, changes in transformer load temperature, changes in wind speed of wind turbines, changes in load demand, and changes in solar radiation.

[0053] Specifically, step S1 involves modeling multiple uncertainties in the power grid system and determining the operating functions for each uncertainty, including:

[0054] Step S101: Determine the operating function of heat transfer change of the transmission line based on the heat balance equation function of the transmission line.

[0055] For transmission lines, the IEEE-738 standard defines their ratings / capacities (in amperes or MVA) as a function of conductor structure (e.g., cross-section and diameter) and field meteorological parameters (e.g., ambient temperature and wind speed).

[0056] This function is based on the heat balance equation (HBE), which represents the heat transfer from the conductor to the surrounding air so that the temperature of the conductor does not exceed a specified limit (e.g., 75°C), because exceeding the temperature limit may cause electrical and mechanical stress.

[0057] Because the heat power generated by the current in the conductor and the heating caused by solar radiation are transferred to the surrounding air through convection and radiation processes, the convective heat transfer rate under low and high wind speed conditions was calculated based on the effect of solar heating, showing the heat transfer through radiation. Therefore, the heat balance equation is expressed as follows:

[0058]

[0059] In the formula, The resistance heating power of the circuit; This represents the solar radiation power, indicating the heating effect of the sun on the circuit. Convection heat dissipation power represents the heat lost by the line through airflow; Radiation heat dissipation power represents the heat lost by the line through radiation. This is the absorption coefficient of the line to solar radiation; This represents the total solar radiation power. The surface area of ​​the line; V is the characteristic diameter of the line; V is the wind speed. Aerodynamic viscosity; The air heat transfer coefficient; This is the correction factor for the angle between the line and the wind speed; For line temperature; Ambient temperature; This represents the difference in emissivity on the surface of the line.

[0060] According to the heat balance equation (HBE), two rated values ​​(ampere capacity) can be defined.

[0061] The first type is called Static Line Rating (SLR), which is a fixed value that can be preset.

[0062] The second type is dynamic line rating, which is calculated using thermal ratings measured with real-time weather parameters. Dynamic line ratings require real-time measuring equipment, such as sensors installed on-site to measure ambient temperature and wind speed. When a power system is equipped with dynamic line rating measuring equipment, system operators can implement flexible and intelligent resource scheduling to efficiently utilize the free capacity of transmission lines and manage wind turbines and related uncertainties.

[0063] Step S102: Based on the dynamic equation function of the top oil temperature in the discrete-time model, determine the operating function of the transformer's load temperature change.

[0064] The load temperature variation of a transformer is limited by two temperatures, including top oil temperature (TOT) and hot spot temperature (HST).

[0065] In this embodiment, the top oil temperature dynamic equation function based on a discrete-time model is used as the operating function for the transformer's load temperature change. Specifically, the top oil temperature dynamic equation function based on the discrete-time model is:

[0066]

[0067] In the formula, This refers to the transformer load current. The ambient temperature at the current moment; This represents the top oil temperature at the previous moment; , , These are coefficients related to the transformer's thermal characteristics and time step, respectively; R is the ratio of load loss to no-load loss. This refers to the rise in oil temperature under transformer rated conditions. This is the oil temperature time constant; For time step; Temperature rise of hot spots caused by load current; This refers to the top oil temperature.

[0068] Step S103: Determine the operating function of the wind turbine's wind speed change based on the probability density function of the wind turbine's wind speed.

[0069] A wind turbine generator set includes a wind turbine, whose output power depends on wind speed and the turbine's structural characteristics. The power-wind speed curve given in the following formula represents the output power of the wind turbine, where... This indicates the rated power of the wind turbine. Indicates wind speed. , and These represent the wind speed at startup, shutdown, and rated speed of the wind turbine, respectively. Since wind speed varies throughout the day, different amounts of wind power can be supplied to the grid.

[0070]

[0071] If the input parameters of the Optimal Power Flow (OPF) problem are without uncertainty, then the OPF problem is a deterministic optimal power flow. However, as mentioned before, uncertain parameters can affect the results of the proposed scheduling problem, and therefore should be considered and appropriately modeled to ensure that the obtained results are practical and close to reality. In this way, probabilistic optimal power flows can be handled.

[0072] Wind speed is an uncertain parameter that affects the output power of wind turbines. Wind speed uncertainty is typically modeled using the Weibull distribution. The probability density function of wind speed is shown below, where k and c are constants representing shape and scale parameters:

[0073]

[0074] The parameters of the probability density function for wind speed depend on the geographical location of the wind turbine and typically vary hourly, daily, or seasonally. In this technical solution, the probability density function will be used to generate wind speed scenarios through Monte Carlo simulation.

[0075] Step S104: Determine the operating function for load demand changes based on the probability density function of load demand.

[0076] Load demand is one of the main sources of uncertainty in power systems. The normal distribution is typically used to model load uncertainty. The probability density function of load demand is shown below:

[0077] In the formula, and These represent the mean and standard deviation, respectively. These parameters are taken from historical data for each day and each hour in the current problem.

[0078] Step S105: Determine the operating function of solar radiation variation based on the Beta probability distribution function of solar radiation.

[0079] Ambient temperature and initial top oil temperature can be modeled using a normal distribution, similar to the distribution of load demand. Finally, the probability distribution function of solar radiation is modeled using a Beta probability distribution function, as shown in the following equation:

[0080]

[0081] In the formula, S is a random variable of solar radiation (unit: kW / m²). and These are the parameters of the Beta distribution function, which vary at different times of the day. They are calculated from the mean and standard deviation of S, as shown below:

[0082] .

[0083] This application's embodiment innovatively incorporates dynamic line and transformer rating technology into the power grid dispatching system and combines it with an uncertainty model, thereby enhancing the system's dynamic adaptability. This integrated solution includes not only dynamic line and transformer rating technology mechanisms but also addresses how to optimize the dynamic dispatching process under varying weather conditions.

[0084] Dynamic line and transformer rating technology can dynamically adjust the capacity of transmission lines and transformers based on real-time weather conditions (such as wind speed and temperature), rather than setting a conservative upper limit based on the most unfavorable weather conditions, as is the case with static ratings. This allows for full utilization of the potential capacity of transmission equipment in actual operation, especially when weather conditions are favorable.

[0085] Dynamic line and transformer rating technology optimizes the use of existing resources without requiring large-scale investment in new transmission lines, transformers, or energy storage systems, which significantly reduces the cost of equipment expansion and redundancy construction.

[0086] Dynamic line and transformer rating technology enhances the dynamic capacity of transmission lines and transformers, enabling the system to respond rapidly during peak load periods and fluctuations in renewable energy output, thus avoiding large-scale reliance on energy storage systems. This approach not only reduces the high deployment costs of energy storage systems but also mitigates the aging issues caused by frequent charging and discharging.

[0087] Step S2: Generate multiple uncertainty scenarios based on the running functions of each uncertainty factor.

[0088] In one embodiment, multiple uncertainty scenarios are generated through Monte Carlo simulation based on the operating functions of each uncertainty factor.

[0089] Monte Carlo simulations can use numerous iterations to generate a complete set of uncertainty scenarios, with the output represented as the mean, standard deviation, and histogram of the desired variables. While Monte Carlo simulations can obtain complete information from the output variables, they are very time-consuming. For operational problems (such as the model in this technical solution, where the probability distribution function of the uncertain parameters is known), a scenario-based approach is typically used. In this way, different scenarios are generated to simulate the uncertain behavior of parameters such as wind speed and load demand, allowing for the calculation of the expected value of operating costs with appropriate estimation.

[0090] Step S3: Reduce multiple uncertain scenarios to obtain multiple typical uncertain scenarios, and construct a set of typical scenarios.

[0091] To avoid increasing computational burden, a scenario reduction method was employed, such as using k-means clustering to reduce multiple uncertain scenarios. However, to avoid losing accuracy and covering up uncertainty, the characteristics of the main scenario were preserved as much as possible when merging similar scenarios.

[0092] For example, a large number of uncertain scenarios (e.g., 1000) were generated. To reduce the computational burden and the complexity of the solution, a K-means scenario reduction technique was used to reduce the number of uncertain scenarios.

[0093] Step S4: With minimizing the cost of power grid output reduction as the optimization objective and the steady state of the power grid system as the constraint, construct a power grid system scheduling optimization model.

[0094] In this embodiment, the objective is to obtain the optimal day-ahead scheduling for thermal power and wind turbine units, considering the uncertainties of wind power, dynamic line ratings, and dynamic transformer ratings. The study period is the intraday scheduling in the following days, where uncertain scenarios and their probabilities of occurrence are integrated into the objective function. Uncertain scenarios need to be analyzed from historical power grid operation data. For example, taking the power grid operation conditions of the past year as an example, several typical scenarios are summarized, and then the proportion of each scenario throughout the year is used as a coefficient. .

[0095] Specifically, with the optimization objective of minimizing the cost of power grid output reduction, the power grid system dispatch optimization model includes an objective function; where the objective function is:

[0096]

[0097] In the formula, OF represents the cost of reducing grid output. , , These are all weighting coefficients, which reflect the correlation between the fuel characteristics and operating conditions of thermal power units. Let be the probability of scenario s occurring. Let g be the power generation capacity of unit g under time t and scenario s. For the assembly of generator units, A set of typical scenarios, Total time Let be the unit load shedding value for node i, which reflects the economic loss when the load cannot be met. Let i be the load shedding power of load node i at time t and scenario s. The unutilized cost per unit of wind power reflects the economic losses caused by the failure to utilize wind power. Let w be the unutilized wind power of the wind farm at time t and scenario s.

[0098] In the objective function, the first term represents the operating (fuel) cost of the thermal power unit, which is a quadratic function of the power generation capacity; the second and third terms calculate the load shedding cost and the wind power reduction cost, respectively. The last term maximizes the utilization of wind power by minimizing the wind power reduction cost, which will promote the integration of more renewable energy into the grid.

[0099] The decision variables of the objective function include the power generation level of each thermal power unit per hour in each scenario, the load shedding value at each node in each scenario and per hour, and the power generation of each wind turbine in each scenario and time.

[0100] The power grid system dispatch optimization model includes constraints; among which, the constraints include upper and lower limits of output of thermal power units, power flow constraints, transmission line thermal constraints, transformer thermal constraints, upper and lower limits of bus voltage constraints, load constraints, and wind power reduction constraints.

[0101] Specifically, the upper and lower limits of cogeneration unit output constraints describe the active and reactive power generation limitations of each unit. Furthermore, variations in unit output power are subject to speed increase and decrease limits. The upper and lower limits of cogeneration unit output constraints are as follows:

[0102]

[0103] In the formula, This represents the minimum / maximum generating capacity of unit g; The maximum generating capacity of unit g; The upper limit of the reactive power of unit g; This is the unit's maximum rate of ascent and ramp-up. This represents the unit's maximum descent and ramp rate.

[0104] Power flow constraints adopt the linear form of AC power flow, representing the active and reactive power balance equations of each node in the network under different hours and scenarios. Power flow constraints include:

[0105]

[0106] In the formula, These represent the active and reactive power outputs of the generator, respectively. These represent the active and reactive power outputs of the wind power system, respectively. These are the active and reactive power demands of the load, respectively. These represent the active / reactive power cut-off from the load, respectively. These represent the sum of active and reactive power transmitted from node i to other nodes via line ij, respectively. These are the admittance parameters of the line; These represent the voltage change; These are the voltage phase angles.

[0107] The thermal constraints of transmission lines and transformers are as follows:

[0108]

[0109]

[0110]

[0111] In the formula, The apparent power of the line; This represents the upper limit of the line's apparent power. Line current; This refers to the line voltage.

[0112] Since an AC model is used in the power flow calculation, the voltage amplitude of the bus must be kept within an acceptable range. Therefore, the upper and lower limits of the bus voltage are constrained as follows:

[0113]

[0114] In the formula, The minimum / maximum allowable amplitude of the node voltage; This represents the voltage offset.

[0115] In some cases, to meet power system operational constraints, it is necessary to reduce some load or limit some available wind power generation. Therefore, the load constraints and wind power reduction constraints are:

[0116]

[0117] In the formula, This refers to the available power of wind power. This refers to the unused wind power.

[0118] Step S5: Based on the typical scenario set, find the optimal solution for the power grid system scheduling optimization model, and determine the optimal scheduling scheme for the power grid system based on the optimal solution.

[0119] Commercial mathematical solvers can be used to find the optimal solution for the power grid system dispatch optimization model. The optimal dispatch scheme of the power grid system includes the power generation level of each thermal power unit, the load shedding value at each node per hour, and the power generation of each wind turbine.

[0120] It should be noted that the embodiments of this application consider multiple uncertainties in the power grid system and model these uncertainties. Multiple uncertainties are generated using the operating functions of the uncertainties, and the uncertainties are reduced. The optimization objective is to minimize the cost of power grid output reduction, and the power grid system steady state is used as a constraint. A power grid system scheduling optimization model is constructed, and the power grid system scheduling optimization model is optimized using the reduced set of typical scenarios. The optimal scheduling scheme of the power grid system is determined based on the optimal solution, thereby enabling the power grid system scheduling scheme to adapt to changes in uncertainties, improving the transmission capacity and flexibility of the power grid system, and adapting to the volatility of renewable energy.

[0121] Based on the same inventive concept, this application also provides a power grid unit output uncertainty scheduling system for implementing the above-mentioned power grid unit output uncertainty scheduling method.

[0122] The solution provided by this system is similar to the solution described in the above method. Therefore, the specific limitations of one or more power grid unit output uncertainty dispatch system embodiments provided below can be found in the limitations of the power grid unit output uncertainty dispatch method described above, and will not be repeated here.

[0123] like Figure 3 As shown in the figure, this application embodiment also provides a power grid unit output uncertainty dispatching system, including:

[0124] The factor modeling module 100 is used to model multiple uncertainties in the power grid system and determine the operating function of each uncertainty.

[0125] The scenario generation module 200 is used to generate multiple uncertain scenarios based on the running functions of each uncertain factor;

[0126] The scenario reduction module 300 is used to reduce multiple uncertain scenarios to obtain multiple typical uncertain scenarios and construct a typical scenario set.

[0127] The model building module 400 is used to build a power grid system scheduling optimization model with the goal of minimizing the cost of power grid output reduction and with the power grid system steady state as the constraint.

[0128] The scheduling optimization module 500 is used to optimize the power grid system scheduling optimization model based on a set of typical scenarios, and determine the optimal scheduling scheme of the power grid system based on the optimal solution.

[0129] In some embodiments, uncertainties include changes in heat transfer of transmission lines, changes in load temperature of transformers, changes in wind speed of wind turbines, changes in load demand, and changes in solar radiation.

[0130] In some embodiments, multiple uncertainties in the power grid system are modeled, and the operating function of each uncertainty is determined, including:

[0131] Based on the heat balance equation function of the transmission line, determine the operating function of the heat transfer change of the transmission line;

[0132] Based on the dynamic equation function of top oil temperature using a discrete-time model, the operating function for determining the load temperature change of the transformer is determined.

[0133] Based on the probability density function of wind speed of the wind turbine, determine the operating function of wind speed change of the wind turbine.

[0134] Based on the probability density function of load demand, determine the operating function for load demand changes;

[0135] Based on the Beta probability distribution function of solar radiation, determine the operating function of solar radiation variation.

[0136] In some embodiments, the method further includes:

[0137] Multiple uncertainty scenarios are generated through Monte Carlo simulation based on the operating functions of each uncertainty factor.

[0138] In some embodiments, the method further includes:

[0139] Multiple uncertainties are reduced by using the k-means clustering algorithm.

[0140] In some embodiments, the power grid system dispatch optimization model includes an objective function; wherein the objective function is:

[0141]

[0142] In the formula, OF represents the cost of reducing grid output. , , All are weighting coefficients. Let be the probability of scenario s occurring. Let g be the power generation capacity of unit g under time t and scenario s. For the assembly of generator units, A set of typical scenarios, Total time Let i be the unit load shedding value. Let i be the load shedding power of load node i at time t and scenario s. For the unit's unutilized costs, Let w be the unutilized wind power of the wind farm at time t and scenario s.

[0143] In some embodiments, the power grid system dispatch optimization model includes constraints; wherein the constraints include upper and lower limits of thermal power unit output, power flow constraints, transmission line thermal constraints, transformer thermal constraints, upper and lower limits of bus voltage, load constraints, and wind power reduction constraints.

[0144] like Figure 4 As shown, this application embodiment also provides an electronic device. The electronic device 10 includes a memory 20 and a processor 30. The memory 20 stores a computer program. When the computer program is executed by the processor 30, the processor 30 performs the steps of the grid unit output uncertainty scheduling method as described in any of the above embodiments.

[0145] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed, implements the steps of the power grid unit output uncertainty dispatching method as described in any of the above embodiments.

[0146] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, electronic devices, and computer storage media described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0147] It should be noted that the terms "comprising" and "having" and any variations thereof in the specification, claims and accompanying drawings of this invention are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such processes, methods, products or devices.

[0148] In the several embodiments provided by this invention, it will be understood that each block in the flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the figures. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved.

[0149] In the embodiments provided by this invention, it should be understood that the disclosed systems, electronic devices, computer storage media, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between devices or units, and may be electrical, mechanical, or other forms.

[0150] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0151] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0152] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for executing all or part of the steps of the methods of the various embodiments of this invention through a computer device (which may be a personal computer, server, or network device, etc.). The aforementioned storage medium includes: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0153] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for scheduling power grid unit output uncertainty, characterized in that, The method comprises the following steps: modeling a plurality of uncertain factors of a power grid system, determining an operation function of each uncertain factor, including: determining an operation function of heat transfer change of a power transmission line according to a heat balance equation function of the power transmission line; the heat balance equation function is expressed as: wherein is the electrical resistance heating power of the line; is the solar radiation power; is the convective heat dissipation power; is the radiative heat dissipation power; is the absorption coefficient of the line to the solar radiation; is the total solar radiation power; is the surface area of the line; is the characteristic diameter of the line; V is the wind speed; is the air dynamic viscosity; is the air heat transfer coefficient; is the correction coefficient of the angle between the line and the wind speed; is the line temperature; is the ambient temperature; is the emissivity difference of the line surface; determining an operation function of load temperature change of a transformer based on a top oil temperature dynamic equation function of a discrete time model; the top oil temperature dynamic equation function of the discrete time model is expressed as: wherein, is the transformer load current; is the ambient temperature at the current time instant; is the top oil temperature at the previous time instant; , , are coefficients related to the transformer thermal characteristics and the time step, respectively; R is the ratio of the load loss to the no-load loss; is the oil temperature rise under the rated conditions of the transformer; is the oil temperature time constant; is the time step; is the hot spot temperature rise due to the load current; is the top oil temperature; determining an operation function of wind speed change of a wind turbine according to a probability density function of wind speed of the wind turbine; the operation function of wind speed change of the wind turbine is expressed as: wherein represents the rated power of the wind turbine, represents the wind speed, , and respectively represent the cut-in wind speed, the cut-out wind speed and the rated wind speed of the wind turbine; determining an operation function of load demand change according to a probability density function of load demand; the probability density function of load demand is expressed as: wherein and denote the mean and standard deviation, respectively; determining an operation function of solar radiation change according to a Beta probability distribution function of solar radiation; the Beta probability distribution function of solar radiation is expressed as: where S is a random variable of solar radiation; and are parameters of the Beta distribution function; generating a plurality of uncertainty scenarios according to the operation functions of the uncertain factors, including: generating the plurality of uncertainty scenarios by Monte Carlo simulation according to the operation functions of the uncertain factors; reducing the plurality of uncertainty scenarios to obtain a plurality of typical uncertainty scenarios, and constructing a typical scenario set; constructing a power grid system dispatching optimization model with a minimum power grid output reduction cost as an optimization objective and a power grid system steady state as a constraint condition; optimizing and solving the power grid system dispatching optimization model based on the typical scenario set, and determining an optimal dispatching scheme of the power grid system according to an optimal solution.

2. The method of claim 1, wherein, The uncertain factors include heat transfer change of a power transmission line, load temperature change of a transformer, wind speed change of a wind turbine, load demand change and solar radiation change.

3. The method of claim 1, wherein, The method further comprises the following steps: reducing the plurality of uncertainty scenarios by a k-means clustering algorithm.

4. The method of claim 1, wherein, The power grid system dispatching optimization model comprises an objective function; wherein the objective function is expressed as: wherein OF is the grid output reduction cost, , , are weight coefficients, is the occurrence probability of scenario s, is the generation power of unit g at time t, scenario s, is the unit set, is the typical scenario set, is the total time, is the unit load shedding value of node i, is the load shedding power of load node i at time t, scenario s, is the unit unit non-utilization cost, is the non-utilization wind power of wind farm w at time t, scenario s.

5. The method of claim 1, wherein, The power grid system dispatching optimization model comprises a constraint condition; wherein the constraint condition comprises upper and lower limits of a thermal power unit output, power flow constraints, transmission line heat constraints, transformer heat constraints, upper and lower limits of a bus voltage, load constraints and wind power reduction constraints.

6. A power grid unit output uncertainty scheduling system based on the power grid unit output uncertainty scheduling method of any one of claims 1-5, characterized in that, The method comprises the following steps: a factor modeling module, configured to model a plurality of uncertain factors of a power grid system, and determine an operation function of each uncertain factor; a scenario generation module, configured to generate a plurality of uncertainty scenarios according to the operation functions of the uncertain factors; a scenario reduction module, configured to reduce the plurality of uncertainty scenarios to obtain a plurality of typical uncertainty scenarios, and construct a typical scenario set; a model construction module, configured to construct a power grid system dispatching optimization model with a minimum power grid output reduction cost as an optimization objective and a power grid system steady state as a constraint condition; a dispatching optimization module, configured to optimize and solve the power grid system dispatching optimization model based on the typical scenario set, and determine an optimal dispatching scheme of the power grid system according to an optimal solution.

7. An electronic device, comprising: The electronic device comprises a memory and a processor, the memory stores a computer program, and the computer program is executed by the processor to make the processor execute the steps of the power grid unit output uncertainty scheduling method according to any one of claims 1-5.

8. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed to implement the steps of the power grid unit output uncertainty scheduling method according to any one of claims 1-5.

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