Power grid unit output uncertainty scheduling method, system, equipment and medium

Abstract

The invention relates to the technical field of power systems, and discloses a power grid unit output uncertainty scheduling method, system, device and medium, the method considers a plurality of uncertain factors of a power grid system, models the plurality of uncertain factors, generates a plurality of uncertain scenes by using operation functions of the uncertain factors, and provides a power grid unit output uncertainty scheduling method, system and device. The method comprises the following steps: establishing a power grid system scheduling optimization model by taking a typical scene set after reduction, reducing uncertainty scenes, taking minimization of power grid output reduction cost as an optimization target and taking a power grid system steady state as a constraint condition, optimizing and solving the power grid system scheduling optimization model by utilizing the reduced typical scene set, and determining an optimal scheduling scheme of a power grid system according to an optimal solution. Therefore, the scheduling scheme of the power grid system adapts to the change of uncertain factors, the transmission capability and flexibility of the power grid system are improved, and the power grid system adapts to the fluctuation of renewable energy sources.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a method, system, equipment and medium for dispatching power grid unit output uncertainty. Background Art

[0002] As the penetration rate of renewable energy such as wind power and photovoltaic power continues to increase, the proportion of new energy in the power system has increased rapidly, but its inherent volatility and intermittency have brought new technical challenges to the stable operation of the power system. The traditional power system that relies on coal and gas has good flexibility and can cope with load changes, but with the increase in installed capacity of new energy, these regulating thermal power units are gradually withdrawn, and the demand for grid flexibility is becoming increasingly urgent.

[0003] In the context of a high proportion of new energy access, the power system needs to introduce smart grid technology to improve equipment utilization and the flexibility of the transmission and distribution network. By introducing dynamic line and transformer rating technology, the intelligent dispatch of the power system can be realized under variable climate conditions, thereby improving the adaptability of the power system without increasing infrastructure, and further ensuring the economy and safety of the power system.

[0004] Existing technical solutions mainly include static rated capacity, redundant construction and equipment expansion, energy storage system, demand response, distributed power supply and microgrid, etc. However, due to the many uncertainties of renewable energy in the power grid system, the output of the power grid system is prone to uncertainty. At the same time, the current technical solutions are difficult to dispatch as the uncertainties of the power grid system change, which leads to poor transmission capacity and flexibility of the power grid system, and it is difficult for the power grid system to adapt to the volatility of renewable energy. Summary of the invention

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

[0006] A first aspect of the present invention provides a method for dispatching power grid unit output uncertainty, comprising:

[0007] Model multiple uncertain factors of the power grid system and determine the operating functions of each uncertain factor;

[0008] Generate multiple uncertainty scenarios based on the operating functions of various uncertain factors;

[0009] Reducing the plurality of uncertainty scenarios to obtain a plurality of typical uncertainty scenarios, and constructing a typical scenario set;

[0010] Taking minimizing the power grid output and reducing the cost as the optimization goal and taking the steady state of the power grid system as the constraint condition, a power grid system dispatch optimization model is constructed;

[0011] The power grid system dispatch optimization model is optimized and solved based on the typical scenario set, and the optimal dispatch plan of the power grid system is determined according to the optimal solution.

[0012] Preferably, the uncertain factors include heat transfer changes of the transmission line, load temperature changes of the transformer, wind speed changes of the wind turbine, load demand changes and solar radiation changes.

[0013] Preferably, the modeling of multiple uncertain factors of the power grid system and determining the operating function of each uncertain factor includes:

[0014] Determining an operating function of heat transfer variation of the transmission line based on a heat balance equation function of the transmission line;

[0015] Determine the operating function of the load temperature change of the transformer based on the top oil temperature dynamic equation function of the discrete time model;

[0016] Determine the operation function of the wind speed change of the wind turbine generator set according to the probability density function of the wind speed of the wind turbine generator set;

[0017] Determining an operating function of the load demand change according to a probability density function of the load demand;

[0018] According to the Beta probability distribution function of solar radiation, the operating function of the solar radiation change is determined.

[0019] Preferably, the method further comprises:

[0020] Multiple uncertainty scenarios are generated through Monte Carlo simulation according to the operating functions of various uncertain factors.

[0021] Preferably, the method further comprises:

[0022] The plurality of uncertainty scenarios are reduced by using a k-means clustering algorithm.

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

[0024]

[0025] Where, OF is the cost reduction of power grid output, , , are weight coefficients, is the occurrence probability of scenario s, is the power generation capacity of unit g at time t and scenario s, For the unit collection, A typical scene set. is the total time, is the unit load removal value of node i, is the load shedding power of load node i at time t and scenario s, is the unutilized cost of the unit, is the unused wind power of wind farm w at time t and scenario s.

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

[0027] In a second aspect, the present invention further provides a power grid unit output uncertainty dispatching system, comprising:

[0028] The factor modeling module is used to model multiple uncertain factors of the power grid system and determine the operating function of each uncertain factor;

[0029] A scenario generation module is used to generate multiple uncertainty scenarios according to the operation functions of various uncertain factors;

[0030] A scenario reduction module, used to reduce the plurality of uncertainty scenarios, obtain a plurality of typical uncertainty scenarios, and construct a typical scenario set;

[0031] Model building module, used to build a grid system dispatch optimization model with minimizing grid output reduction costs as the optimization goal and grid system steady state as the constraint condition;

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

[0033] In a third aspect, the present invention further provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the grid unit output uncertainty scheduling method as described in the first aspect.

[0034] In a fourth aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed, the steps of the method for scheduling power grid unit output uncertainty as described in the first aspect are implemented.

[0035] It can be seen from the above technical solutions that the present invention takes into account multiple uncertainties of the power grid system, models multiple uncertainties, generates multiple uncertainty scenarios using the operating functions of the uncertainties, and reduces the uncertain scenarios. It also takes minimizing the power grid output reduction cost as the optimization goal, and takes the steady state of the power grid system as the constraint condition to construct a power grid system scheduling optimization model, and uses the reduced typical scenario set to find the optimal solution for the power grid system scheduling optimization model, and determines the optimal scheduling plan of the power grid system based on the optimal solution, so that the scheduling plan of the power grid system adapts to the changes in uncertainties, improves the transmission capacity and flexibility of the power grid system, and can adapt to the power grid system to the volatility of renewable energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0037] Figure 1 An application environment of a power grid unit output uncertainty scheduling method provided by an embodiment of the present invention;

[0038] Figure 2 A flow chart of a method for dispatching power grid unit output uncertainty provided by an embodiment of the present invention;

[0039] Figure 3 A schematic diagram of the structure of a power grid unit output uncertainty dispatching system provided by an embodiment of the present invention;

[0040] Figure 4 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0041] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] Existing technical solutions mainly include static rated capacity, redundant construction and equipment expansion, energy storage system, demand response, distributed power supply and microgrid, etc. However, due to the many uncertainties of renewable energy in the power grid system, the output of the power grid system is prone to uncertainty. At the same time, the current technical solutions are difficult to dispatch as the uncertainties of the power grid system change, which leads to poor transmission capacity and flexibility of the power grid system, and it is difficult for the power grid system to adapt to the volatility of renewable energy.

[0043] Among them, the static rated capacity ignores the dynamic changes of meteorological conditions. In most cases, the rated value is conservative, resulting in low actual capacity utilization of transmission equipment. This conservative strategy limits the dispatch flexibility of the system, especially when the output of renewable energy fluctuates greatly and the load peaks, which makes it easier to produce transmission bottlenecks. However, the static rated capacity is underutilized.

[0044] Redundant construction and equipment expansion require high infrastructure investment and are subject to restrictions on environmental protection, land resources and construction cycles. In addition, redundant construction is prone to waste of resources and may cause equipment to be idle for a long time, so it is not the best solution to quickly improve system flexibility in the short term. However, it is easy to rely on redundant construction and the cost is high.

[0045] 3. The cost of energy storage systems is high, especially large-scale battery energy storage systems. In addition, the capacity and life of energy storage systems are limited, frequent charging and discharging will accelerate the aging of batteries, and their management and optimization scheduling are more complex.

[0046] The effect of demand response depends on the user's enthusiasm for participation and response speed, and has a large uncertainty. At the same time, demand response is highly dependent on user behavior, and the actual effect may be insufficient during peak hours, making it difficult to completely replace regulating power supplies or energy storage. However, the effect of demand response is poor and has a large uncertainty.

[0047] The coordinated control of distributed power sources and microgrids is complex and costly. In addition, microgrids are small in scale and have limited support for large-scale load demands. Their flexibility and stability are difficult to reach the level of traditional large power grids.

[0048] In summary, the current technical solutions are difficult to dispatch as the uncertainty factors of the power grid system change, which leads to poor transmission capacity and flexibility of the power grid system, making it difficult for the power grid system to adapt to the volatility of renewable energy.

[0049] To this end, the power grid unit output uncertainty scheduling method provided in the embodiment of the present application can be applied to Figure 1In the application environment shown. Among them, each node of the power grid system communicates with the server 102 through the network. The data storage system can store the data that the server 102 needs to process. The data storage system can be integrated on the server 102, or it can be placed on the cloud or other network servers. The server 102 models multiple uncertain factors of the power grid system and determines the operating function of each uncertain factor; generates multiple uncertainty scenarios according to the operating function of each uncertain factor; reduces multiple uncertainty scenarios, and reduces to obtain multiple typical uncertainty scenarios, and constructs a typical scenario set; takes minimizing the power grid output reduction cost as the optimization goal, and takes the steady state of the power grid system as the constraint condition, constructs a power grid system scheduling optimization model; based on the typical scenario set, the power grid system scheduling optimization model is optimized and solved, and the optimal scheduling plan of the power grid system is determined according to the optimal solution. Server 102 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services.

[0050] like Figure 2 As shown, the embodiment of the present application provides a method for dispatching power grid unit output uncertainty, and the method is applied to Figure 1 The server 102 in the example is used as an example to illustrate the method, which includes the following steps S1 to S5. Among them:

[0051] Step S1: Model multiple uncertain factors of the power grid system and determine the operating function of each uncertain factor.

[0052] Among them, 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.

[0053] Specifically, the multiple uncertain factors of the power grid system are modeled in step S1 to determine the operating function of each uncertain factor, including:

[0054] Step S101: determining an operating function of heat transfer variation of a power transmission line according to a heat balance equation function of the power transmission line.

[0055] For transmission lines, the IEEE-738 standard defines their rating / capacity (expressed in amperes or MVA) as a function of the conductor structure (such as cross-section and diameter) and site meteorological parameters (such as ambient temperature and wind speed).

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

[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 convection heat transfer rate under low and high wind speed conditions is calculated through the influence of solar heating, showing the heat transfer of the radiation process. Therefore, the heat balance equation function is expressed as:

[0058]

[0059] In the formula, is the resistance heating power of the circuit; is the solar radiation power, representing the heating effect of the sun on the line; It is the convection heat dissipation power, which means the heat dissipated by the circuit through air flow; The radiation heat dissipation power refers to the heat dissipated by the circuit through radiation. is the absorption coefficient of the line to solar radiation; is the total solar radiation power; is the line surface area; 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 on the line surface.

[0060] Here, two ratings (ampere capacities) can be defined based on the heat balance equation (HBE).

[0061] The first is called the Static Line Rating (SLR), which is a pre-set, fixed value.

[0062] The second is for dynamic line rating, which is related to thermal rating calculation using real-time weather parameter measurements. Dynamic line rating requires some real-time measurement equipment, such as sensors installed on site to measure ambient temperature and wind speed. When the power system is equipped with dynamic line rating measurement 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: Determine an operating function of the load temperature change of the transformer based on the top oil temperature dynamic equation function of the discrete time model.

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

[0065] Among them, the embodiment of the present application adopts the top oil temperature dynamic equation function based on the discrete time model as the operating function of the load temperature change of the transformer. Specifically, the top oil temperature dynamic equation function based on the discrete time model is:

[0066]

[0067] In the formula, is the transformer load current; is the current ambient temperature; is the top oil temperature at the previous moment; , , are coefficients related to transformer thermal characteristics and time step, respectively; R is the ratio of load loss to no-load loss; is the oil temperature rise under rated conditions of the transformer; is the oil temperature time constant; is the time step; It is the hot spot temperature rise caused by the load current; is the top oil temperature.

[0068] Step S103: determining an operating function of the wind speed change of the wind turbine generator set according to the probability density function of the wind speed of the wind turbine generator set.

[0069] The wind turbine generator system includes a wind turbine, and the output power of the wind turbine depends on the wind speed and the structural characteristics of the wind turbine. The power-wind speed curve given in the following formula represents the output power of the wind turbine, where represents the rated power of the wind turbine, Indicates wind speed, , and They represent the start-up wind speed, shutdown wind speed and rated wind speed of the wind turbine respectively. Since the wind speed varies at different times of the day, different amounts of wind power can be injected into the grid.

[0070]

[0071] If there is no uncertainty in the input parameters of the optimal power flow (OPF) problem, then the optimal power flow problem is a deterministic optimal power flow. However, as before, the uncertain parameters affect the results of the proposed scheduling problem and should be considered and appropriately modeled to ensure that the results obtained are practical and close to reality. In this way, probabilistic optimal power flow can be handled.

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

[0073]

[0074] The parameters of the probability density function of wind speed depend on the geographical location of the wind turbines and usually have hourly / daily / seasonal variations. In this technical solution, the probability density function will be used to generate wind speed scenarios through Monte Carlo simulation.

[0075] Step S104: Determine an operating function of load demand changes according to the probability density function of load demand.

[0076] Load demand is one of the main sources of uncertainty in power systems. Normal distribution is usually used to model load uncertainty. The probability density function of load demand is shown as follows:

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

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

[0079] The 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 the Beta probability distribution function, as shown below:

[0080]

[0081] Where S is the random variable of solar radiation (unit: kW / m²); and 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 follows:

[0082] .

[0083] Among them, the embodiment of the present application innovatively incorporates dynamic line and transformer rating technology into the power grid dispatching system and combines it with the uncertainty model to improve the dynamic adaptability of the system. This integrated solution not only includes the dynamic line and transformer rating technology mechanism, but also involves how to achieve dynamic dispatch process optimization under changing meteorological conditions.

[0084] Dynamic line and transformer rating technology can dynamically adjust the capacity of transmission lines and transformers according to real-time meteorological conditions (such as wind speed, temperature, etc.), rather than setting conservative upper limits based on the most unfavorable weather conditions like static rated capacity. This can fully utilize 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 the need for large-scale investments in new transmission lines, transformers or energy storage systems, which greatly reduces the cost of equipment expansion and redundancy construction.

[0086] Dynamic line and transformer rating technology improves the dynamic capacity of transmission lines and transformers, allowing the system to respond quickly to load peaks and fluctuations in renewable energy output, avoiding large-scale reliance on energy storage systems. This approach not only reduces the high deployment cost of energy storage systems, but also alleviates the problem of energy storage aging caused by frequent charging and discharging.

[0087] Step S2: Generate multiple uncertainty scenarios according to the operating functions of various uncertain factors.

[0088] In one embodiment, a plurality of uncertainty scenarios are generated through Monte Carlo simulation according to the operating functions of various uncertain factors.

[0089] Among them, Monte Carlo simulation can use a large number of iterations to generate a complete set of uncertainty scenarios, and the problem output is expressed in the form of mean, standard deviation and histogram of the required variables. Although Monte Carlo simulation can obtain complete information from the output variables, it is very time-consuming. For operational problems (such as the model of this technical solution, where the probability distribution function of the uncertain parameters is known), a scenario-based approach is usually adopted. In this way, different scenarios are generated to simulate the uncertain behavior of parameters such as wind speed and load demand, so that the expected value of the operating cost can be calculated with appropriate estimates.

[0090] Step S3: reduce the multiple uncertainty scenarios to obtain multiple typical uncertainty scenarios, and construct a typical scenario set.

[0091] In order to avoid the increase of computational burden, scenario reduction methods are used, for example, multiple uncertain scenarios are reduced by k-means clustering algorithm. However, in order to avoid losing precision and covering uncertainty, the characteristics of the main scenario are kept as much as possible when merging similar scenarios.

[0092] Exemplarily, a large number of uncertainty scenarios (eg, 1000) are generated. In order to reduce the computational burden and the complexity of the solution, the K-means scenario reduction technique is used to reduce the number of uncertainty scenarios.

[0093] Step S4: Taking minimizing the power grid output reduction cost as the optimization goal and taking the power grid system steady state as the constraint condition, a power grid system dispatch optimization model is constructed.

[0094] Among them, in the embodiment of the present application, the goal is to obtain the best day-ahead scheduling of thermal power and wind turbines, taking into account the uncertainty of wind power, dynamic line ratings and dynamic transformer ratings. The time period studied is the next intraday scheduling, in which the uncertainty scenarios are integrated into the objective function together with their probability of occurrence. Uncertainty scenarios need to be analyzed from historical grid operation data. For example, taking the grid operation conditions in the past year as an example, several typical scenarios are summarized, and then the proportion of the scenarios in the whole year is used as the coefficient .

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

[0096]

[0097] Where, OF is the cost reduction of power grid output, , , are weight coefficients, which reflect the coefficients related to the fuel characteristics and operating status of the thermal power unit. is the occurrence probability of scenario s, is the power generation capacity of unit g at time t and scenario s, For the unit collection, A typical scene set. is the total time, is the unit load shedding value of node i, which reflects the economic loss when the load cannot be met. is the load shedding power of load node i at time t and scenario s, is the unused cost of the unit, which reflects the economic loss caused by the unabsorbed wind power. is the unused wind power of wind farm w at time t and scenario s.

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

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

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

[0101] Specifically, the upper and lower output constraints of thermal power units describe the active and reactive power generation limits of each unit. In addition, the output power change of the unit is subject to speed increase and speed decrease limits. The upper and lower output constraints of thermal power units are:

[0102]

[0103] In the formula, is the minimum / maximum power generation of unit g; is the maximum power generation power of unit g; is the lower upper limit of reactive power of unit g; is the maximum climbing rate of the unit; It is the maximum descending climbing rate of the unit.

[0104] The power flow constraint uses the linear form of AC power flow to express the active and reactive power balance equations of each node in the network at different hours and in different scenarios. The power flow constraints include:

[0105]

[0106] In the formula, are respectively the active / reactive power output by the generator; are the active / reactive power output of wind power respectively; They are load active / reactive demand respectively; They are load active / reactive removal power respectively; are the total active / reactive power transmitted from node i to other nodes through line ij; are the admittance parameters of the line respectively; are the voltage changes respectively; are the voltage phase angles respectively.

[0107] The transmission line thermal constraints and transformer thermal constraints are:

[0108]

[0109]

[0110]

[0111] In the formula, is the apparent power of the line; is the apparent power upper limit of the line; is the line current; is the line voltage.

[0112] Since the AC model is used in the power flow calculation, the bus voltage amplitude 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, is the minimum / maximum allowed amplitude of the node voltage; is the voltage offset.

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

[0116]

[0117] In the formula, is the available power of wind power; is the unabsorbed wind power.

[0118] Step S5: finding the optimal solution for the power grid system dispatch optimization model based on the typical scenario set, and determining the optimal dispatch plan for the power grid system according to the optimal solution.

[0119] Among them, commercial mathematical solvers can be used to find the optimal solution to the power grid system scheduling optimization model. The optimal scheduling plan of the power grid system includes the power generation level of each thermal power unit, the load removal value at each node every hour, and the power generation of each wind turbine unit.

[0120] It should be noted that the embodiment of the present application takes into account multiple uncertainties of the power grid system, models multiple uncertainties, generates multiple uncertainty scenarios using the operating functions of the uncertainties, and reduces the uncertain scenarios. It also takes minimizing the power grid output reduction cost as the optimization goal, and takes the steady state of the power grid system as the constraint condition to construct a power grid system scheduling optimization model. The power grid system scheduling optimization model is solved by using the typical scenario set after reduction, and the optimal scheduling plan of the power grid system is determined according to the optimal solution, so that the scheduling plan of the power grid system adapts to the changes in uncertainties, improves the transmission capacity and flexibility of the power grid system, and can adapt to the power grid system to the volatility of renewable energy.

[0121] Based on the same inventive concept, an embodiment of the present application also provides a power grid unit output uncertainty dispatching system for implementing the above-mentioned power grid unit output uncertainty dispatching method.

[0122] The implementation solution for solving the problem provided by the system is similar to the implementation solution recorded in the above method. Therefore, the specific limitations in one or more power grid unit output uncertainty scheduling system embodiments provided below can be referred to the limitations on the power grid unit output uncertainty scheduling method above, and will not be repeated here.

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

[0124] The factor modeling module 100 is used to model multiple uncertain factors of the power grid system and determine the operating function of each uncertain factor;

[0125] A scenario generation module 200, for generating a plurality of uncertainty scenarios according to the operation functions of various uncertain factors;

[0126] A scenario reduction module 300 is used to reduce multiple uncertainty scenarios, obtain multiple uncertainty typical scenarios, and construct a typical scenario set;

[0127] A model building module 400 is used to build a power grid system dispatch optimization model with minimizing power grid output reduction costs as an optimization goal and with the power grid system steady state as a constraint condition;

[0128] The dispatch optimization module 500 is used to find the optimal solution for the power grid system dispatch optimization model based on a typical scenario set, and determine the optimal dispatch plan for the power grid system according to the optimal solution.

[0129] In some embodiments, the uncertainties include heat transfer variations of the transmission line, load temperature variations of the transformer, wind speed variations of the wind turbine, load demand variations, and solar radiation variations.

[0130] In some embodiments, multiple uncertain factors of the power grid system are modeled to determine the operating function of each uncertain factor, including:

[0131] Determine the operation function of the heat transfer variation of the transmission line according to the heat balance equation function of the transmission line;

[0132] Based on the top oil temperature dynamic equation function of the discrete time model, determine the operation function of the transformer load temperature change;

[0133] Determine the operation function of the wind speed change of the wind turbine generator set according to the probability density function of the wind speed of the wind turbine generator set;

[0134] According to the probability density function of the load demand, determine the operation function of the load demand change;

[0135] According to the Beta probability distribution function of solar radiation, the operating function of solar radiation change is determined.

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

[0137] Multiple uncertainty scenarios are generated through Monte Carlo simulation according to the operating functions of various uncertain factors.

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

[0139] Multiple uncertainty scenarios are reduced 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] Where, OF is the cost reduction of power grid output, , , are weight coefficients, is the occurrence probability of scenario s, is the power generation capacity of unit g at time t and scenario s, For the unit collection, A typical scene set. is the total time, is the unit load removal value of node i, is the load shedding power of load node i at time t and scenario s, is the unutilized cost of the unit, is the unused wind power of wind farm w 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 limit constraints on thermal power unit output, power flow constraints, transmission line thermal constraints, transformer thermal constraints, upper and lower limit constraints on bus voltage, load constraints and wind power reduction constraints.

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

[0145] An embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed, implements the steps of the method for scheduling power grid unit output uncertainty in any of the above embodiments.

[0146] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described system, electronic device and computer storage medium can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0147] It should be noted that the terms "including" and "having" and any variations thereof in the specification and claims of the present invention and the above-mentioned drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or apparatus comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or apparatus.

[0148] In several embodiments provided by the present invention, it is understood that each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and a part of a module, a program segment or a code includes one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved.

[0149] In the several embodiments provided by the present 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 only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

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

[0151] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0152] If the integrated unit is implemented in the form of 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 the present 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, and the computer software product is stored in a storage medium, including a number of instructions for executing all or part of the steps of the various embodiments of the method of the present invention through a computer device (which can be a personal computer, a server, or a network device, etc.). The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (full name in English: Read-Only Memory, English abbreviation: ROM), random access memory (full name in English: Random Access Memory, English abbreviation: RAM), disk or optical disk, etc. Various media that can store program codes.

[0153] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for dispatching power grid unit output uncertainty, characterized in that: include: Model multiple uncertain factors of the power grid system and determine the operating functions of each uncertain factor; Generate multiple uncertainty scenarios based on the operating functions of various uncertain factors; Reducing the plurality of uncertainty scenarios to obtain a plurality of typical uncertainty scenarios, and constructing a typical scenario set; Taking minimizing the power grid output and reducing the cost as the optimization goal and taking the steady state of the power grid system as the constraint condition, a power grid system dispatch optimization model is constructed; The power grid system dispatch optimization model is optimized and solved based on the typical scenario set, and the optimal dispatch plan of the power grid system is determined according to the optimal solution.

2. The method for dispatching power grid unit output uncertainty according to claim 1, characterized in that: The uncertainties include variations in heat transfer of transmission lines, variations in load temperature of transformers, variations in wind speed of wind turbines, variations in load demand and variations in solar radiation.

3. The method for dispatching power grid unit output uncertainty according to claim 2, characterized in that: The method of modeling multiple uncertain factors of the power grid system and determining the operating function of each uncertain factor includes: Determining an operating function of heat transfer variation of the transmission line based on a heat balance equation function of the transmission line; Determine the operating function of the load temperature change of the transformer based on the top oil temperature dynamic equation function of the discrete time model; Determine the operation function of the wind speed change of the wind turbine generator set according to the probability density function of the wind speed of the wind turbine generator set; Determining an operating function of the load demand change according to a probability density function of the load demand; According to the Beta probability distribution function of solar radiation, the operating function of the solar radiation change is determined.

4. The method for dispatching power grid unit output uncertainty according to claim 1, characterized in that: Also includes: Multiple uncertainty scenarios are generated through Monte Carlo simulation according to the operating functions of various uncertain factors.

5. The method for dispatching power grid unit output uncertainty according to claim 1, characterized in that: Also includes: The plurality of uncertainty scenarios are reduced by using a k-means clustering algorithm.

6. The method for dispatching power grid unit output uncertainty according to claim 1, characterized in that: The power grid system dispatch optimization model includes an objective function; wherein the objective function is: Where, OF is the cost reduction of power grid output, , , are weight coefficients, is the occurrence probability of scenario s, is the power generation capacity of unit g at time t and scenario s, For the unit collection, A typical scene 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 and scenario s, is the unutilized cost of the unit, is the unused wind power of wind farm w at time t and scenario s.

7. The method for dispatching power grid unit output uncertainty according to claim 1, characterized in that: The grid system dispatch optimization model includes constraints; wherein the constraints include upper and lower limit constraints on thermal power unit output, power flow constraints, transmission line thermal constraints, transformer thermal constraints, upper and lower limit constraints on bus voltage, load constraints and wind power reduction constraints.

8. A power grid unit output uncertainty dispatching system, characterized in that: include: The factor modeling module is used to model multiple uncertain factors of the power grid system and determine the operating function of each uncertain factor; A scenario generation module is used to generate multiple uncertainty scenarios according to the operation functions of various uncertain factors; A scenario reduction module, used to reduce the plurality of uncertainty scenarios, obtain a plurality of typical uncertainty scenarios, and construct a typical scenario set; Model building module, used to build a grid system dispatch optimization model with minimizing grid output reduction costs as the optimization goal and grid system steady state as the constraint condition; The scheduling optimization module is used to optimize and solve the power grid system scheduling optimization model based on the typical scenario set, and determine the optimal scheduling plan of the power grid system according to the optimal solution.

9. An electronic device, characterized in that: The electronic device includes a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes the steps of the method for uncertain output scheduling of power grid units as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed, the steps of the method for dispatching power grid unit output uncertainty as described in any one of claims 1 to 7 are implemented.

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