Distributed energy device scheduling methods, devices, and storage media

Abstract

This invention discloses a method, apparatus, and storage medium for scheduling distributed energy devices. Relating to the field of safe and stable power grid operation, the method includes: acquiring wind and air humidity data within a target area; collecting power data on power lines during the process of multiple distributed energy devices supplying power to load devices within the target area via power lines; obtaining a target fault probability based on the wind and air humidity data; obtaining a load loss quantification index based on the power data and the target fault probability; and optimizing the scheduling of multiple distributed energy devices based on the load loss quantification index to obtain a scheduling strategy for the multiple distributed energy devices. This invention solves the technical problem in related technologies where the impact of weather disasters on power line operation is not considered during distributed energy device scheduling, resulting in imperfect and poorly applicable distributed energy device control and scheduling strategies, leading to significant impacts on regional power consumption from natural disasters.

Description

Technical Field

[0001] This invention relates to the field of safe and stable operation of power grids, and more specifically, to a method, apparatus, and storage medium for dispatching distributed energy equipment. Background Technology

[0002] In recent years, with global warming and frequent weather disasters, the safe and stable power supply of distributed energy equipment has been greatly threatened. Under the influence of weather disasters, power lines that transmit electricity to distributed energy equipment often experience multi-dimensional faults, causing critical loads within the distributed energy equipment to go offline and lose power supply, posing a significant threat to the normal power supply of a region (such as a city). Furthermore, under the influence of power line faults, the load loss during power transmission increases.

[0003] In related technologies, the control and scheduling of distributed energy equipment does not take into account the situation where weather disasters cause multi-dimensional faults in power lines, nor does it consider the increased load loss of power lines caused by the failure of distributed energy equipment due to weather disasters, thus resulting in imperfect control and scheduling strategies for distributed energy equipment.

[0004] There is currently no effective solution to the above problems. Summary of the Invention

[0005] This invention provides a method, apparatus, and storage medium for scheduling distributed energy devices, at least to address the technical problem in related technologies where the impact of weather disasters on power line operation is not considered when scheduling distributed energy devices, resulting in imperfect and poorly applicable control and scheduling strategies for distributed energy devices, and leading to significant impacts on regional electricity consumption from natural disasters.

[0006] According to one aspect of the present invention, a method for scheduling distributed energy devices is provided, comprising: acquiring wind power data and air humidity data within a target area; collecting power data on power lines during the process of multiple distributed energy devices supplying power to load devices within the target area via power lines; obtaining a target fault probability based on the wind power data and the air humidity data, wherein the target fault probability is used to indicate the probability of the power line failing under the influence of wind power and air humidity; obtaining a load loss quantification index based on the power data and the target fault probability, wherein the load loss quantification index is used to indicate the load loss generated by supplying power to load devices within the target area via power lines under the influence of the target fault probability; and optimizing the scheduling of the multiple distributed energy devices based on the load loss quantification index to obtain a scheduling strategy for the multiple distributed energy devices.

[0007] Optionally, obtaining the target fault probability based on the wind data and the air humidity data includes: determining the icing thickness on the surface of the power line based on the wind data and the air humidity data; determining the first load borne by the power line based on the wind data and the icing thickness; determining the second load borne by the power line based on the air humidity data and the icing thickness; and determining the target fault probability based on the first load and the second load.

[0008] Optionally, determining the target failure probability based on the first load and the second load includes: obtaining a third load based on the first load and the second load; determining the target failure probability as a preset first failure probability when the third load is less than or equal to a first load threshold; or obtaining the target failure probability based on the first load threshold, the second load threshold, and the third load when the third load is greater than the first load threshold and less than a second load threshold; or determining the target failure probability as a preset third failure probability when the third load is greater than or equal to the second load threshold.

[0009] Optionally, obtaining the load loss quantification index based on the power data and the target fault probability includes: partitioning the target area to obtain multiple sub-regions; determining the importance of the power load corresponding to each of the multiple sub-regions based on the power data corresponding to each of the multiple sub-regions; obtaining the load loss value corresponding to each of the multiple sub-regions; and determining the load loss quantification index based on the importance of the power load corresponding to each of the multiple sub-regions, the load loss value corresponding to each of the multiple sub-regions, and the target fault probability.

[0010] Optionally, when there are multiple power lines, determining the load loss quantification index based on the importance of the power load corresponding to each of the multiple sub-regions, the load loss value corresponding to each of the multiple sub-regions, and the target fault probability includes: determining the fault probability corresponding to each of the multiple fault scenarios based on the target fault probability corresponding to each of the multiple power lines, wherein the fault scenario is determined based on the number of power lines with faults among the multiple power lines; and determining the load loss quantification index based on the importance of the power load corresponding to each of the multiple sub-regions, the load loss value corresponding to each of the multiple sub-regions, and the fault probability corresponding to each of the multiple fault scenarios.

[0011] Optionally, the plurality of distributed energy devices include heat source devices in a heating network and power supply devices in a power distribution network. The step of optimizing the scheduling of the plurality of distributed energy devices based on the load loss quantification index to obtain a scheduling strategy for the plurality of distributed energy devices includes: obtaining a first cost incurred by the operation of the heat source devices; obtaining a second cost incurred by the operation of the power supply devices; determining a load loss cost based on the load loss quantification index; obtaining a target cost based on the first cost, the second cost, and the load loss cost; and obtaining a scheduling strategy for the plurality of distributed energy devices with the goal of minimizing the target cost.

[0012] Optionally, when the heat source equipment includes at least a combined heat and power unit, a gas turbine, and a thermal energy storage device, obtaining the first cost incurred during the operation of the heat source equipment includes: obtaining the first cost based on the costs incurred by the combined heat and power unit, the gas turbine, and the thermal energy storage device during operation; when the power supply equipment includes at least a generator, a grid interconnection device, and an electrical energy storage device, obtaining the second cost incurred during the operation of the power supply equipment includes: obtaining the second cost based on the costs incurred by the generator, the grid interconnection device, and the electrical energy storage device during operation.

[0013] According to another aspect of the present invention, a distributed energy device scheduling apparatus is also provided, comprising: an acquisition module for acquiring wind power data and air humidity data within a target area; a collection module for collecting power data on power lines during the process of multiple distributed energy devices supplying power to load devices within the target area via power lines; a probability calculation module for obtaining a target failure probability based on the wind power data and the air humidity data, wherein the target failure probability indicates the probability of the power line failing under the influence of wind power and air humidity; an index calculation module for obtaining a load loss quantification index based on the power data and the target failure probability, wherein the load loss quantification index indicates the load loss generated by supplying power to load devices within the target area via power lines under the influence of the target failure probability; and a scheduling module for optimizing the scheduling of the multiple distributed energy devices based on the load loss quantification index to obtain a scheduling strategy for the multiple distributed energy devices.

[0014] According to another aspect of the present invention, a non-volatile storage medium is also provided, the non-volatile storage medium storing a plurality of instructions adapted for loading by a processor and executing any one of the distributed energy device scheduling methods described herein.

[0015] According to another aspect of the present invention, a computer program product is also provided, including a computer program that, when executed by a processor, implements the steps of any one of the distributed energy device scheduling methods.

[0016] In this embodiment of the invention, wind and air humidity data within a target area are acquired; power data on the power lines is collected during the process of multiple distributed energy devices supplying power to load devices within the target area via power lines; based on the wind and air humidity data, a target fault probability is obtained, wherein the target fault probability indicates the probability of the power line failing under the influence of wind and air humidity; based on the power data and the target fault probability, a load loss quantification index is obtained, wherein the load loss quantification index indicates the load loss caused by supplying power to load devices within the target area via power lines under the influence of the target fault probability. Based on the aforementioned load loss quantification index, the scheduling of the multiple distributed energy devices is optimized to obtain the scheduling strategy for the multiple distributed energy devices. This achieves the goal of accurately determining the scheduling strategy for distributed energy devices while considering power line faults caused by weather disasters. This improves the accuracy and applicability of the scheduling strategy for distributed energy devices, reduces the impact of natural disasters and other factors on regional electricity consumption, and solves the technical problem in related technologies where the impact of weather disasters on power line operation is not considered when scheduling distributed energy devices, resulting in imperfect and poorly applicable distributed energy device control and scheduling strategies, leading to significant impacts on regional electricity consumption from natural disasters. Attached Figure Description

[0017] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0018] Figure 1 This is a flowchart of a distributed energy equipment scheduling method according to an embodiment of the present invention;

[0019] Figure 2 This is a flowchart of an optional distributed energy device scheduling method according to an embodiment of the present invention;

[0020] Figure 3 This is a schematic diagram of a distributed energy equipment scheduling device according to an embodiment of the present invention. Detailed Implementation

[0021] 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 should fall within the scope of protection of the present invention.

[0022] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0023] First, to facilitate understanding of the embodiments of the present invention, some terms or nouns involved in the present invention will be explained below:

[0024] Power lines are lines used to transmit electrical energy. During transmission, power lines experience energy losses, primarily due to resistance, inductive reactance, and capacitance. These losses cause electrical energy to be converted into heat and dissipated, thus affecting the efficiency of energy transmission.

[0025] Load capacity refers to the maximum load or weight that a system, device, or structure can withstand. In engineering, load capacity is commonly used to describe the maximum load-bearing capacity of a material, component, or device to ensure that it does not exceed its design limits during use.

[0026] According to an embodiment of the present invention, a method embodiment for scheduling distributed energy devices is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0027] Figure 1 This is a flowchart of a distributed energy device scheduling method according to an embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps:

[0028] Step S102: Obtain wind force data and air humidity data within the target area;

[0029] Optionally, the target area refers to an urban area. Wind speed and humidity data for an urban area are acquired. This urban area is characterized by multiple functions, including entertainment and residential living. Within this urban area, distributed energy devices are scheduled based on the wind and humidity data to achieve stable power and heating supply. Specifically, wind speed data includes wind speed and force, while humidity data includes air water density, air water content, rainfall rate, and the density of ice formed when water in the air condenses.

[0030] Step S104: Collect power data on the power lines during the process of multiple distributed energy devices supplying power to load devices in the target area through power lines;

[0031] Optionally, multiple distributed energy devices include heat source devices in the heating network and power supply devices in the power distribution network. The power supply devices supply power to load devices within the target area via power lines. The heat source devices can simultaneously produce electrical energy and heat energy. By utilizing the heat energy generated from fuel combustion, they drive a thermal generator to produce electrical energy; that is, at the heat source device end, heat energy is converted into electrical energy. The electrical energy generated by the heat source devices supplies power to load devices within the target area via power lines, and the remaining heat energy after power supply is used to generate steam or hot water to heat the target area, effectively improving energy utilization and reducing energy waste during the power supply process.

[0032] Optionally, the power data on the power lines includes the voltage of electricity used within the urban area, the power of the load equipment, and the load loss value during the transmission of power energy through the power lines. Additionally, parameters such as the line diameter and line length can be obtained to comprehensively calculate the load loss value.

[0033] Step S106: Based on wind data and air humidity data, obtain the target fault probability, whereby the target fault probability is used to indicate the probability of power line failure under the influence of wind and air humidity.

[0034] Optionally, the probability of power line failure can be obtained by combining the effects of wind force and air humidity, in order to determine the risk tolerance of power lines in extreme weather conditions such as wind force greater than level three or air humidity exceeding 75%. That is, the probability of power line failure can be obtained under strong wind, heavy rain or snow weather. Taking the above target failure probability as a factor, countermeasures can be obtained to ensure the stability of urban power supply and heating.

[0035] In one optional embodiment, the target failure probability is obtained based on wind data and air humidity data, including: determining the icing thickness on the surface of the power line based on wind data and air humidity data; determining a first load borne by the power line based on wind data and icing thickness; determining a second load borne by the power line based on air humidity data and icing thickness; and determining the target failure probability based on the first load and the second load.

[0036] Optionally, the icing thickness on the power line surface refers to the thickness of the icing formed on the power line surface when air humidity condenses into ice under the influence of wind. The icing thickness R on the power line surface is... eq The specific calculations are as follows:

[0037]

[0038] Where, ρ I ρ is the density of the ice covering the ice. W Let be the density of water in the air, W be the water content in the air, r be the rainfall rate, υ be the wind speed, and t be the predetermined time.

[0039] Optionally, based on the aforementioned icing thickness and wind data, the first load borne by the power line is obtained, whereby the first load is the weight of the power line subjected to icing under wind influence. The first load L W The specific calculations are as follows:

[0040] L W =BSυ 2 (D+2R eq )

[0041] Where S is the line length, D is the power line diameter, and B is the wind force coefficient. It should be noted that the wind force coefficient is a parameter used to describe the degree of influence of wind on objects. The specific calculation of the wind force coefficient B is as follows:

[0042] B=(0.5*ρ W *A*υ^2) / F1

[0043] Where, ρ W The density of water in the air (kg / m³) 3 A is the windward area (m²) 2 ), where υ is the wind speed (m / s) and F1 is the wind force (N). It should be noted that the wind-receiving area is obtained by the diameter and length of the power line.

[0044] Optionally, based on air humidity data and icing thickness, a second load on the power line is determined, whereby the second load is the weight of the power line subjected to icing under the influence of air humidity. Second load LI The specific calculations are as follows:

[0045] L I =K I (D+R eq )R eq

[0046] Among them, K I The ice force coefficient is a parameter used to describe the degree to which ice force affects an object. Ice force coefficient K I The specific calculations are as follows:

[0047] K I =(0.5*ρ W *A*υ^2) / F2

[0048] Wherein, F2 is the gravity of the ice covering the power line, which is obtained based on the ice density and gravitational acceleration.

[0049] In one optional embodiment, determining the target failure probability based on the first load and the second load includes: obtaining a third load based on the first load and the second load; determining the target failure probability as a preset first failure probability when the third load is less than or equal to the first load threshold; or obtaining the target failure probability based on the first load threshold, the second load threshold, and the third load when the third load is greater than the first load threshold and less than the second load threshold; or determining the target failure probability as a preset third failure probability when the third load is greater than or equal to the second load threshold.

[0050] Optionally, a third load is determined based on the first and second loads, whereby the third load is the load exerted on the power line by icing under the combined influence of wind and air humidity. The third load is L. WI The specific calculations are as follows:

[0051]

[0052] Optionally, based on the first load threshold α WI Second load threshold β WI and the third load L WI Obtain the target failure probability F line Target failure probability F line The specific calculations are as follows:

[0053]

[0054] It should be noted that the target failure probability is the probability that a single power line will fail under the influence of wind and air humidity when supplying power to the target area through a single line.

[0055] Step S108: Based on power data and target failure probability, obtain load loss quantification index, wherein the load loss quantification index is used to indicate the load loss caused by power supply to load equipment in the target area through power lines under the influence of target failure probability.

[0056] Optionally, during the transmission of electrical energy through power lines, the resistance of the conductors causes electrical energy to be converted into heat, resulting in energy loss. Additionally, power line faults, damage, short circuits, or aging also lead to energy loss. The greater the energy loss, the lower the efficiency of power transmission, leading to voltage instability in the target area and ensuring normal power supply. Energy loss directly manifests as load loss during power line transmission. However, among the factors contributing to load loss, load loss due to power line faults accounts for 80% of the total load loss during power line transmission. Therefore, it is necessary to consider the degree of load loss under the influence of the target fault probability, and then maintain the power lines or regulate distributed energy devices accordingly to ensure stable power and heating in the target area.

[0057] In one optional embodiment, a load loss quantification index is obtained based on power data and a target fault probability, including: partitioning the target area to obtain multiple sub-regions; determining the importance of the power load corresponding to each of the multiple sub-regions based on the power data corresponding to each of the multiple sub-regions; obtaining the load loss value corresponding to each of the multiple sub-regions; and determining the load loss quantification index based on the importance of the power load corresponding to each of the multiple sub-regions, the load loss value corresponding to each of the multiple sub-regions, and the target fault probability.

[0058] Optionally, the target area is an urban area with multiple functions such as entertainment and living. The urban area is divided into multiple sub-areas based on the different electricity consumption of each functional area and the varying degrees of impact of electricity on these areas. For example, entertainment areas, such as shopping malls and amusement parks, cannot operate normally if power lines are cut off. Living areas, such as residential buildings, can still be used for activities like sleeping even without power. Therefore, the importance of electricity load for entertainment areas is greater than that for living areas. Based on the electricity consumption and time periods corresponding to each sub-area, the electricity consumption of each sub-area during the same time period is compared to determine the importance of the electricity load for each sub-area. Specifically, the importance ω of the electricity load for each sub-area is determined. n func The specific calculations are as follows:

[0059] ω a func :ω b func =entropy a func :entropy b func

[0060] Where, ω a func ω represents the importance of the power load corresponding to sub-region 'a' among multiple sub-regions. b func Entropy represents the importance of the power load corresponding to sub-region b among multiple sub-regions, where sub-regions a and b are any two sub-regions among the multiple sub-regions. a func For sub-region a, the corresponding electricity consumption is entropy. b func Let be the electricity consumption corresponding to sub-region b. Based on the set of importance of the electricity load corresponding to the above multiple sub-regions, the importance ω of the electricity load corresponding to each sub-region is obtained. n func .

[0061] Optionally, electricity is generated by multiple distributed energy devices, and power lines transmit the electricity generated by these devices to multiple sub-regions. Each sub-region experiences varying degrees of load loss during the reception of electricity, and the load loss value l corresponding to each sub-region is obtained. n sheddingThe importance of the power load corresponding to each of the multiple sub-regions is used as a weight value to calculate the importance of the load loss value of each sub-region in the loss process. In the case of excessive load loss value, the power line of the sub-region with high power load importance is prioritized for maintenance, or the distributed energy equipment is prioritized for regulation, so as to ensure that the power supply and heating status of the sub-region are maintained.

[0062] In one optional embodiment, when there are multiple power lines, a load loss quantification index is determined based on the importance of the power load corresponding to each of the multiple sub-regions, the load loss value corresponding to each of the multiple sub-regions, and the target fault probability. This includes: determining the fault probability corresponding to each of the multiple fault scenarios based on the target fault probability corresponding to each of the multiple power lines, wherein the fault scenario is determined based on the number of power lines with faults among the multiple power lines; and determining the load loss quantification index based on the importance of the power load corresponding to each of the multiple sub-regions, the load loss value corresponding to each of the multiple sub-regions, and the fault probability corresponding to each of the multiple fault scenarios.

[0063] Optionally, to ensure normal power supply across multiple sub-regions, multiple power lines are typically used to transmit power simultaneously, effectively carrying the entire power supply capacity of each sub-region. However, among these multiple power lines, some may transmit power normally while others fail, preventing power transmission. Therefore, multiple fault scenarios are defined, where each scenario is determined based on the number of faulty power lines. For example, one scenario might be five power lines transmitting power simultaneously, with three faulty and two transmitting normally. Each fault scenario corresponds to a probability of occurrence. Specifically, the probabilities of each scenario are determined based on the target fault probabilities of each power line, calculated as follows:

[0064] P i =C(k,i)×F line i ×(1-F line ) (k-i)

[0065] Among them, P i Let F be the probability of a fault scenario involving i power lines, k be the total number of power lines transmitting power simultaneously, C(k,i) be the number of combinations of faults in the i power lines out of the k power lines, and F be the probability of a fault scenario involving i power lines. line Let i be the target failure probability of a single power line, and i be the number of faulty wire bundles in the k power lines.

[0066] Considering a fault scenario where 5 power lines are transmitting power simultaneously, 3 power lines are faulty, and 2 power lines are transmitting power normally, calculate the probability of a fourth fault under this scenario as follows:

[0067] Where k = 5, i = 3, C(5,3) = 5! / (3! × (5-3)!) = 10, and the fault probability P corresponding to the scenario of 3 power line faults is... i =10×F line ^3×(1-F line )^2.

[0068] Optionally, based on the importance of the power load corresponding to each of the multiple sub-regions. Load loss values ​​corresponding to multiple sub-regions and the failure probabilities P corresponding to multiple failure scenarios. i The load loss quantification index Ind is determined, and the specific calculation is as follows:

[0069]

[0070] Among them, P i Let be the failure probability corresponding to the i-th failure scenario. To determine the importance of the power load corresponding to the nth sub-region, This represents the load loss value corresponding to the nth sub-region.

[0071] Optionally, the fault scenario can also be used to indicate the distribution location of the faulty power lines, that is, the fault occurring in the power line transmitting sub-region a is one scenario, and the fault occurring in the power line transmitting sub-region b is another scenario.

[0072] Step S110: Based on the load loss quantification index, the scheduling of multiple distributed energy devices is optimized to obtain the scheduling strategy of multiple distributed energy devices.

[0073] Optionally, the aforementioned load loss quantification index is obtained by comprehensively considering the failure probability of multiple fault scenarios, the importance of the power load corresponding to multiple sub-regions, and the load loss value corresponding to multiple sub-regions. Using this load loss quantification index as a reference, scheduling optimization is performed on multiple distributed energy devices. This yields the load loss quantification index calculated based on the importance of multiple sub-regions within the target area under conditions of strong winds, heavy rainfall, or snow. With the goal of minimizing the load loss quantification index, i.e., minimizing the load loss within the target area, a scheduling strategy for distributed energy devices is derived based on the importance of multiple sub-regions within the target area, thus minimizing load loss.

[0074] In one optional embodiment, the multiple distributed energy devices include heat source devices in a heating network and power supply devices in a power distribution network. Based on a load loss quantification index, the multiple distributed energy devices are scheduled and optimized to obtain a scheduling strategy for the multiple distributed energy devices, including: obtaining a first cost incurred by the operation of the heat source devices; obtaining a second cost incurred by the operation of the power supply devices; determining the load loss cost based on the load loss quantification index; obtaining a target cost based on the first cost, the second cost, and the load loss cost; and obtaining a scheduling strategy for the multiple distributed energy devices with the goal of minimizing the target cost.

[0075] Optionally, based on load loss quantification indicators, determine the load loss cost, C. lc The specific calculations are as follows:

[0076] C lc =Ind′*c load

[0077] Among them, c load Cost per unit load loss.

[0078] Optionally, during the process of electricity energy from production to transmission and then to use, not only will load losses occur during transmission, leading to increased electricity energy transportation costs, but energy losses will also occur at the electricity energy generation end, leading to increased electricity energy production costs. Electricity energy generation includes at least a portion of the heat energy generated by heat source equipment being converted into electrical energy, and the power supply equipment in the distribution network. The first cost C incurred by the operation of the heat source equipment is then obtained. heat And the second cost C incurred by the operation of power supply equipment elec The target cost is the total cost of all electrical energy losses during the process from production to transmission and use. The target cost is the sum of the first cost, the second cost, and the load loss cost.

[0079] Optionally, while ensuring that the quantifiable load loss index is reduced to a minimum, since the unit load loss cost is a constant, the load loss cost is also reduced to a minimum. Combining the first cost, the second cost, and the load loss cost, and with the goal of minimizing the target cost, a scheduling strategy for multiple distributed energy devices is obtained.

[0080] In one optional embodiment, when the heat source equipment includes at least a combined heat and power unit, a gas turbine, and a thermal energy storage device, obtaining the first cost incurred in operating the heat source equipment includes: obtaining the first cost based on the costs incurred by the combined heat and power unit, the gas turbine, and the thermal energy storage device during operation; when the power supply equipment includes at least a generator, a grid interconnection device, and an electric energy storage device, obtaining the second cost incurred in operating the power supply equipment includes: obtaining the second cost based on the costs incurred by the generator, the grid interconnection device, and the electric energy storage device during operation.

[0081] Optionally, when the heat source equipment includes at least a combined heat and power (CHP) unit, a gas turbine, and thermal energy storage equipment, the first cost is the sum of the costs incurred by the CHP unit, gas turbine, and thermal energy storage equipment during operation. When the power supply equipment includes at least a generator, a grid interconnection device, and electrical energy storage equipment, the second cost is the sum of the costs incurred by the generator, grid interconnection device, and electrical energy storage equipment during operation.

[0082] Optionally, models can be established based on multiple distributed energy devices to obtain power supply equipment models and heat pipe models. The scheduling strategy for the multiple distributed energy devices obtained above is to schedule the power supply equipment models and heat pipe models, thereby realizing the scheduling of multiple distributed energy devices.

[0083] The power supply equipment model is as follows:

[0084]

[0085] in, This indicates the electrical power of the generator. Indicates the thermal power of the generator. This indicates the energy conversion efficiency of the generator. This indicates the energy conversion efficiency of the generator; This indicates the preset lower limit of the electrical power output of the generator. R represents the preset upper limit of the electrical power output of the generator. H R represents the preset upper limit of the generator's electrothermal power ratio. L This indicates the preset lower limit of the generator's electrothermal power ratio.

[0086] The specific model of the thermal pipeline is as follows:

[0087]

[0088] in, This represents the set of pipes starting with water supply pipe node e. This represents the set of pipes ending at water supply pipe node e. This represents the temperature of the water flowing at the x-end of the water supply pipe, in milliseconds (ms). x This represents the mass of water in water supply pipe x. This indicates the temperature of the water flowing out of node e of the water supply pipeline; The temperature of the water flowing at the outlet of the return water pipe x is represented by mr. x This represents the mass of water in the return water pipe x. This indicates the temperature of the water flowing out of node e of the return water pipe. This indicates the temperature of the water flowing at the inlet of the water supply pipe x. This indicates the temperature of the water flowing at the inlet of the return water pipe x. This represents the temperature of the water flowing at the outlet of pipe x. λ represents the temperature of the water flowing at the inlet of pipe x. x T represents the thermal conductivity coefficient of the pipe. y Indicates ambient temperature, L represents pipe length, C p This indicates specific heat capacity.

[0089] Through the above steps S102 to S110, the goal of accurately determining the dispatch strategy for distributed energy equipment can be achieved based on the consideration of power line faults caused by weather disasters. This improves the accuracy and applicability of the dispatch strategy for distributed energy equipment, reduces the impact of natural disasters and other factors on regional electricity consumption, and solves the technical problem in related technologies where the impact of weather disasters on power line operation is not considered when dispatching distributed energy equipment, resulting in imperfect and poorly applicable control and dispatch strategies for distributed energy equipment, leading to significant impacts on regional electricity consumption from natural disasters.

[0090] Based on the above embodiments and optional embodiments, the present invention proposes an optional implementation method. Figure 2 This is a flowchart of an optional distributed energy device scheduling method according to an embodiment of the present invention, such as... Figure 2 As shown, the method includes:

[0091] S1. Obtain meteorological data of blizzard disaster, including wind data and air humidity data. Based on the wind data and air humidity data, establish the first load and second load of blizzard on power lines. Based on the first load and second load, obtain the target failure probability of urban power lines, and thus obtain the mapping relationship between the target failure probability of power lines and meteorological data under the influence of blizzard.

[0092] S2. Based on the social functions of each urban block, the city is functionally divided into multiple sub-regions. Based on the failure probability of power lines in the sub-regions under the influence of disasters, the load importance (importance to electricity demand) in different sub-regions, and the load loss value in different sub-regions, a quantitative index of urban load loss considering the load importance is proposed.

[0093] S3. First, considering the energy conversion efficiency and operational constraints of the coupled equipment, a model of the power supply equipment within the distributed energy equipment is established to obtain the power supply equipment model; second, based on the characteristics of the water supply and return pipelines of the thermal system and combined with the operational characteristics of the water supply network, a thermal pipeline model is constructed.

[0094] S4, with the optimization objectives of minimizing the load loss quantification index and minimizing the overall operating cost, obtains the strategy for scheduling distributed energy equipment under the influence of severe weather. The above distributed energy equipment scheduling strategy is used to schedule the power supply equipment model and the heat pipeline model, thereby scheduling the distributed energy equipment.

[0095] Through the above steps S1 to S4, the goal of accurately determining the dispatch strategy for distributed energy equipment can be achieved based on the consideration of power line faults caused by weather disasters. This improves the accuracy and applicability of the dispatch strategy for distributed energy equipment, reduces the impact of natural disasters and other factors on regional electricity consumption, and solves the technical problem in related technologies where the impact of weather disasters on power line operation is not considered when dispatching distributed energy equipment, resulting in imperfect and poorly applicable control and dispatch strategies for distributed energy equipment, leading to significant impacts on regional electricity consumption from natural disasters.

[0096] This embodiment also provides a distributed energy equipment scheduling device, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the terms "module" and "device" can refer to a combination of software and / or hardware that performs a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, hardware implementations, or a combination of software and hardware, are also possible and contemplated.

[0097] According to an embodiment of the present invention, an apparatus embodiment for implementing the above-described distributed energy equipment scheduling method is also provided. Figure 3 This is a schematic diagram of the structure of a distributed energy equipment scheduling device according to an embodiment of the present invention, such as... Figure 3 As shown, the aforementioned distributed energy equipment scheduling device includes: an acquisition module 200, a data collection module 202, a probability calculation module 204, an index calculation module 206, and a scheduling module 208, wherein:

[0098] The acquisition module 200 is used to acquire wind force data and air humidity data within the target area;

[0099] The acquisition module 202, connected to the acquisition module 200, is used to acquire power data on the power lines during the process of multiple distributed energy devices supplying power to load devices in the target area through power lines.

[0100] The probability calculation module 204 is connected to the acquisition module 202 and is used to obtain the target fault probability based on wind force data and air humidity data. The target fault probability is used to indicate the probability of power line failure under the influence of wind force and air humidity.

[0101] The index calculation module 206 is connected to the probability calculation module 204 and is used to obtain a load loss quantification index based on power data and target fault probability. The load loss quantification index is used to indicate the load loss caused by power supply to load equipment in the target area through power lines under the influence of the target fault probability.

[0102] The scheduling module 208, connected to the index calculation module 206, is used to optimize the scheduling of multiple distributed energy devices based on the load loss quantification index, and obtain the scheduling strategy of multiple distributed energy devices.

[0103] In this embodiment of the invention, an acquisition module 200 is configured to acquire wind power data and air humidity data within a target area; a collection module 202, connected to the acquisition module 200, is configured to collect power data on the power lines during the process of multiple distributed energy devices supplying power to load devices within the target area via power lines; a probability calculation module 204, connected to the collection module 202, is configured to obtain a target fault probability based on the wind power data and air humidity data, wherein the target fault probability indicates the probability of a power line fault occurring under the influence of wind power and air humidity; and an index calculation module 206, connected to the probability calculation module 204, is configured to obtain a load loss quantification index based on the power data and the target fault probability, wherein the load loss quantification index indicates the probability of a power line fault occurring under the influence of the target fault probability. The load loss generated by the power line supplying power to the load equipment in the target area; the scheduling module 208, connected to the index calculation module 206, is used to optimize the scheduling of multiple distributed energy devices based on the load loss quantification index, and obtain the scheduling strategy of multiple distributed energy devices. This achieves the goal of accurately determining the scheduling strategy of distributed energy devices based on considering power line faults caused by weather disasters, thereby improving the accuracy and applicability of the scheduling strategy of distributed energy devices, reducing the impact of natural disasters and other factors on regional electricity consumption, and solving the technical problem in related technologies where the impact of weather disasters on power line operation was not considered when scheduling distributed energy devices, resulting in imperfect and poorly applicable distributed energy device control and scheduling strategies, leading to significant impact of natural disasters on regional electricity consumption. It should be noted that the above modules can be implemented by software or hardware. For example, for the latter, it can be implemented in the following ways: the above modules can be located in the same processor; or, the above modules can be located in different processors in any combination.

[0104] It should be noted that the acquisition module 200, collection module 202, probability calculation module 204, index calculation module 206, and scheduling module 208 mentioned above correspond to steps S102 to S110 in the embodiments. The instances and application scenarios implemented by the above modules and corresponding steps are the same, but are not limited to the content disclosed in the above embodiments. It should be noted that the above modules, as part of the device, can run on a computer terminal.

[0105] It should be noted that the optional or preferred implementation methods of this embodiment can be found in the relevant descriptions in the embodiments, and will not be repeated here.

[0106] The aforementioned distributed energy equipment scheduling device may also include a processor and a memory. The aforementioned acquisition module 200, collection module 202, probability calculation module 204, index calculation module 206, scheduling module 208, etc., are all stored in the memory as program modules, and the processor executes the aforementioned program modules stored in the memory to realize the corresponding functions.

[0107] The processor contains a core that retrieves the corresponding program modules from memory. One or more cores may be configured. Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory includes at least one memory chip.

[0108] According to an embodiment of this application, an embodiment of a non-volatile storage medium is also provided. Optionally, in this embodiment, the non-volatile storage medium includes a stored program, wherein, when the program runs, it controls the device where the non-volatile storage medium is located to execute any of the distributed energy device scheduling methods described above.

[0109] Optionally, in this embodiment, the non-volatile storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any mobile terminal in a group of mobile terminals, and the non-volatile storage medium includes stored programs.

[0110] Optionally, during program execution, the device containing the non-volatile storage medium may perform the following functions: acquire wind and air humidity data within the target area; collect power data on the power lines during the process of multiple distributed energy devices supplying power to load devices within the target area via power lines; obtain the target failure probability based on the wind and air humidity data, where the target failure probability indicates the probability of power line failure under the influence of wind and air humidity; obtain a load loss quantification index based on the power data and the target failure probability, where the load loss quantification index indicates the load loss generated when supplying power to load devices within the target area via power lines under the influence of the target failure probability; and optimize the scheduling of multiple distributed energy devices based on the load loss quantification index to obtain a scheduling strategy for multiple distributed energy devices.

[0111] According to an embodiment of this application, an embodiment of a processor is also provided. Optionally, in this embodiment, the processor is used to run a program, wherein the program executes any of the distributed energy device scheduling methods described above.

[0112] According to an embodiment of this application, an embodiment of a computer program product is also provided. Optionally, in this embodiment, the computer program product includes a computer program that, when executed by a processor, implements the steps of any of the above-described distributed energy device scheduling methods.

[0113] Optionally, when the aforementioned computer program product is executed on a data processing device, it is suitable to execute an initialization program with the following method steps: acquiring wind power data and air humidity data within a target area; collecting power data on power lines during the process of multiple distributed energy devices supplying power to load devices within the target area via power lines; obtaining a target fault probability based on the wind power data and air humidity data, wherein the target fault probability is used to indicate the probability of a power line fault occurring under the influence of wind power and air humidity; obtaining a load loss quantification index based on the power data and the target fault probability, wherein the load loss quantification index is used to indicate the load loss generated by supplying power to load devices within the target area via power lines under the influence of the target fault probability; and optimizing the scheduling of multiple distributed energy devices based on the load loss quantification index to obtain a scheduling strategy for multiple distributed energy devices.

[0114] This invention provides an electronic device, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it performs the following steps: acquiring wind power data and air humidity data within a target area; collecting power data on the power lines during the process of multiple distributed energy devices supplying power to load devices within the target area via power lines; obtaining a target fault probability based on the wind power data and air humidity data, wherein the target fault probability indicates the probability of a power line fault under the influence of wind power and air humidity; obtaining a load loss quantification index based on the power data and the target fault probability, wherein the load loss quantification index indicates the load loss generated when supplying power to load devices within the target area via power lines under the influence of the target fault probability; and optimizing the scheduling of multiple distributed energy devices based on the load loss quantification index to obtain a scheduling strategy for the multiple distributed energy devices.

[0115] The order of the above embodiments of the present invention is merely for description and does not represent the superiority or inferiority of the embodiments.

[0116] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0117] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of modules described above can be a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules 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 modules, and may be electrical or other forms.

[0118] The modules described above as separate components may or may not be physically separate. Similarly, the components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple modules. Some or all of the modules can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0119] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0120] If the aforementioned integrated modules are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable non-volatile 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 non-volatile storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned non-volatile storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0121] The above are merely preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for scheduling distributed energy devices, characterized in that, include: Acquire wind speed and air humidity data within the target area; The power data on the power lines is collected during the process of multiple distributed energy devices supplying power to load devices in the target area through power lines. Based on the wind force data and the air humidity data, the target fault probability is obtained, including: determining the icing thickness on the surface of the power line based on the wind force data and the air humidity data; and determining the first load borne by the power line based on the wind force data and the icing thickness using the following method. ,in, The length of the power line, Let B be the diameter of the power line, and B be the wind force coefficient, which is a parameter used to describe the degree of influence of wind on objects. The wind force coefficient B is obtained as follows: B = (0.5 * * A * ^2) / F1, where, Let A be the density of water in the air and A be the windward area. F1 represents wind speed, and F1 represents wind force. The wind-receiving area is obtained by considering the diameter and length of the power line. Based on the air humidity data and the icing thickness, the second load borne by the power line is determined as follows: ,in, This refers to the thickness of the ice layer. The ice force coefficient is a parameter used to describe the degree of influence of ice force on an object. It is obtained in the following way: = (0.5 * * A * ^2) / F2, where F2 is the gravity of the ice covering the power line, obtained based on the ice density and gravitational acceleration; the target fault probability is determined based on the first load and the second load; wherein the target fault probability is used to indicate the probability of the power line failing under the influence of wind and air humidity; Based on the power data and the target fault probability, a load loss quantification index is obtained, including: partitioning the target area to obtain multiple sub-areas; determining the importance of the power load corresponding to each of the multiple sub-areas based on the power data corresponding to each of the multiple sub-areas; obtaining the load loss value corresponding to each of the multiple sub-areas; and determining the fault probability corresponding to multiple fault scenarios based on the target fault probability corresponding to each of the multiple power lines, wherein the fault scenario is determined based on the number of power lines with faults among the multiple power lines, and the fault probability of the fault scenario is obtained in the following way: ,in, Let be the probability of failure for a scenario where i power lines are faulty, and k be the total number of power lines transmitting power simultaneously. Let i be the number of combinations of faults in k power lines. Let i be the target failure probability of a power line being damaged, and i be the number of faulty wire bundles in k power lines. Based on the importance of the power load corresponding to the multiple sub-regions, the load loss value corresponding to the multiple sub-regions, and the failure probability corresponding to the multiple failure scenarios, the load loss quantification index is determined. The load loss quantification index is used to indicate the load loss caused by supplying power to the load equipment in the target region through the power line under the influence of the target failure probability. Based on the load loss quantification index, the scheduling of the multiple distributed energy devices is optimized to obtain the scheduling strategy for the multiple distributed energy devices.

2. The method according to claim 1, characterized in that, Determining the target failure probability based on the first load and the second load includes: Based on the first load and the second load, a third load is obtained; If the third load is less than or equal to the first load threshold, the target failure probability is determined to be a preset first failure probability; or When the third load is greater than the first load threshold and less than the second load threshold, the target failure probability is obtained based on the first load threshold, the second load threshold, and the third load; or If the third load is greater than or equal to the second load threshold, the target failure probability is determined to be the preset third failure probability.

3. The method according to claim 1 or 2, characterized in that, The plurality of distributed energy devices includes heat source devices in a heating network and power supply devices in a power distribution network. The scheduling optimization of the plurality of distributed energy devices based on the load loss quantification index, to obtain a scheduling strategy for the plurality of distributed energy devices, includes: Obtain the first cost incurred by the operation of the heat source equipment; Obtain the second cost incurred by the operation of the power supply equipment; Based on the aforementioned load loss quantification indicators, the load loss cost is determined; Based on the first cost, the second cost, and the load loss cost, the target cost is obtained; With the goal of minimizing the target cost, a scheduling strategy for the multiple distributed energy devices is obtained.

4. The method according to claim 3, characterized in that, When the heat source equipment includes at least a combined heat and power unit, a gas turbine, and a thermal energy storage device, obtaining the first cost generated by the operation of the heat source equipment includes: obtaining the first cost based on the costs generated by the combined heat and power unit, the gas turbine, and the thermal energy storage device during operation; When the power supply equipment includes at least a generator, a grid interconnection device, and an energy storage device, obtaining the second cost generated by the operation of the power supply equipment includes: obtaining the second cost based on the costs generated by the generator, the grid interconnection device, and the energy storage device during operation.

5. A distributed energy equipment dispatching device, characterized in that, include: The acquisition module is used to acquire wind speed data and air humidity data within the target area; The acquisition module is used to collect power data on the power lines during the process of multiple distributed energy devices supplying power to load devices in the target area through power lines; The probability calculation module is used to obtain the target fault probability based on the wind data and the air humidity data, including: determining the icing thickness on the surface of the power line based on the wind data and the air humidity data; and determining the first load borne by the power line based on the wind data and the icing thickness in the following manner. ,in, The length of the power line, Let B be the diameter of the power line, and B be the wind force coefficient, which is a parameter used to describe the degree of influence of wind on objects. The wind force coefficient B is obtained as follows: B = (0.5 * * A * ^2) / F1, where, Let A be the density of water in the air and A be the windward area. F1 represents wind speed, and F1 represents wind force. The wind-receiving area is obtained by considering the diameter and length of the power line. Based on the air humidity data and the icing thickness, the second load borne by the power line is determined as follows: ,in, This refers to the thickness of the ice layer. The ice force coefficient is a parameter used to describe the degree of influence of ice force on an object. It is obtained in the following way: =(0.5 * * A * ^2) / F2, where F2 is the gravity of the ice covering the power line, obtained based on the ice density and gravitational acceleration; the target fault probability is determined based on the first load and the second load; wherein the target fault probability is used to indicate the probability of the power line failing under the influence of wind and air humidity; The indicator calculation module is used to obtain a load loss quantification indicator based on the power data and the target fault probability, including: partitioning the target area to obtain multiple sub-areas; determining the importance of the power load corresponding to each of the multiple sub-areas based on the power data corresponding to each of the multiple sub-areas; obtaining the load loss value corresponding to each of the multiple sub-areas; and determining the fault probability corresponding to multiple fault scenarios based on the target fault probability corresponding to each of the multiple power lines, wherein the fault scenario is determined based on the number of power lines with faults among the multiple power lines, and the fault probability of the fault scenario is obtained in the following way: ,in, Let be the probability of failure for a scenario where i power lines are faulty, and k be the total number of power lines transmitting power simultaneously. Let i be the number of combinations of faults in k power lines. Let i be the target failure probability of a power line being damaged, and i be the number of faulty wire bundles in k power lines. Based on the importance of the power load corresponding to the multiple sub-regions, the load loss value corresponding to the multiple sub-regions, and the failure probability corresponding to the multiple failure scenarios, the load loss quantification index is determined. The load loss quantification index is used to indicate the load loss caused by supplying power to the load equipment in the target region through the power line under the influence of the target failure probability. The scheduling module is used to optimize the scheduling of the multiple distributed energy devices based on the load loss quantification index, and obtain the scheduling strategy of the multiple distributed energy devices.

6. A non-volatile storage medium, characterized in that, The non-volatile storage medium stores multiple instructions, which are adapted to be loaded by a processor and executed by the distributed energy device scheduling method according to any one of claims 1 to 4.

7. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the distributed energy equipment scheduling method according to any one of claims 1 to 4.

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