Load classification scheduling method and system in extreme high temperature scene

Abstract

The invention discloses a load classification scheduling method and system in an extreme high temperature scene. The method comprises the following steps: dividing node loads in a power distribution network into important loads, centralized control loads and general loads; uniformly regulating and controlling the temperature control load in the centralized control load to obtain a load reduction amount; establishing a demand side response mechanism for a general load, interrupting or transferring a part of load to an off-peak period, and obtaining transferred and interrupted load power; the electric vehicle is regarded as a common load when being charged, the electric vehicle is discharged in a vehicle grid-connected mode in a load peak period, power support is provided for the power distribution network, and the power injected to the nodes of the power distribution network by each V2G station is obtained; and establishing an optimization model. And solving to obtain the minimum load loss amount and the economic cost, and realizing load classification scheduling in the extreme high temperature scene. Through load grading control, temperature control and centralized control adjustment, demand response and V2G collaborative peak clipping, and in combination with multi-objective optimization, the economic cost is minimized while the power supply reliability is guaranteed, and the power grid toughness at an extreme high temperature is improved.

Description

Background Art

[0002] In recent years, the frequent occurrence of extreme high-temperature weather has led to a record high in urban load demand, posing a serious threat to the operation safety of urban distribution networks. On the one hand, extreme high temperature will significantly reduce the power generation of clean energy such as hydropower, wind power, and photovoltaic power, resulting in a shortage of source supply. On the other hand, temperature-controlled loads such as air conditioners are greatly affected by weather and temperature, and their peak usage times often coincide with peak demand periods, further exacerbating the challenges of peak load management. The problem of power supply guarantee for distribution networks in extreme high-temperature scenarios has become an urgent problem to be solved.

[0003] In recent years, the frequent occurrence of extreme high-temperature weather has led to a record high in urban load demand, posing a serious threat to the operation safety of urban distribution networks. This phenomenon exposes multiple challenges faced by the current urban power system in extreme climate scenarios: The spatio-temporal mismatch between clean energy output and load demand has intensified In a high-temperature environment, the output of hydropower decreases by 20-30% due to reduced runoff and increased evaporation; wind power is affected by the atmospheric thermal stability stratification, and the wind speed decreases, resulting in a power generation efficiency drop of more than 15%; for every 1°C increase in the temperature of photovoltaic modules, the efficiency decays by 0.4% - 0.5%, and the comprehensive efficiency loss can reach 10-15% in extreme high temperature. At the same time, the surge in temperature-controlled load demand expands the daily peak-valley difference to 2-3 times that of normal periods, forming a "supply reduction and demand increase" scissors difference phenomenon.

[0004] The adaptability of traditional load scheduling models is insufficient Existing scheduling systems mainly rely on historical load curves for prediction, but the non-linear load mutations caused by extreme high temperature result in a prediction error rate as high as 30% - 40%. Especially in load-intensive areas where the air-conditioning load accounts for more than 50%, the clustering characteristics of temperature-sensitive loads make traditional rigid control means ineffective. The 2019 California blackout incident exposed the lag in the dynamic response of traditional scheduling systems to temperature-controlled loads.

[0005] The transmission margin of the distribution network continues to shrink The problem of equipment aging has caused the average load rate of urban distribution transformers to exceed the critical value of 85%, and the allowable current-carrying capacity of some heavily loaded equipment decreases by 15% - 20% at an ambient temperature of 40°C. During the extreme high temperature in Chongqing in 2022, the failure rate of distribution lines increased by 3.8 times compared with normal years, exposing the problem of mismatch between the thermal stability checking standards of equipment and extreme climate.

[0006] It caused regional voltage collapse in the existing dispatching system during the European heatwave in 2022. The maximum load duration of the distribution networks in multiple cities exceeded the 8-hour limit of the design standard, highlighting the urgency of developing a new method for load classification and dispatching. Subsequent research needs to focus on breaking through key technologies such as uncertainty modeling on both the source and load sides, multi-time scale coordinated control, and power cyber-physical integration, and constructing a resilient distribution network system with climate adaptability. Summary of the Invention

[0007] The technical problem to be solved by the present invention is to provide a method and system for load classification and dispatching in extreme high-temperature scenarios in view of the deficiencies in the above-mentioned existing technologies. Aiming at the spatio-temporal imbalance between supply and demand, lagged regulation response, and overload risk of distribution equipment caused by the attenuation of clean energy output, the surge of temperature-controlled loads, and the interaction of equipment thermal stability constraints in the urban distribution network under extreme high-temperature scenarios, a load classification and dispatching model integrating multi-source heterogeneous data is constructed to solve the technical problems of mismatched multi-time scale coordination, insufficient release of flexible regulation potential, and difficulty in synergistically optimizing multi-dimensional security constraints in the traditional dispatching system, and to achieve the safe and economic operation of the distribution network under extreme climate conditions.

[0008] The present invention adopts the following technical solutions: A method for load classification and dispatching in extreme high-temperature scenarios, comprising the following steps: Classify the loads of each node in the distribution network into important loads, centralized control loads, and general loads; Unify the regulation of the temperature-controlled loads in the centralized control loads to obtain the load reduction amount ; Establish a demand response mechanism for general loads, interrupt or transfer part of the loads to off-peak hours to obtain the transferred and interrupted load powers; Regard electric vehicles as general loads when charging. During peak load hours, electric vehicles discharge in the form of vehicle-to-grid connection to provide power support for the distribution network, and obtain the power injected by each V2G station into the distribution network nodes; Establish an optimization model based on the obtained load reduction amount, step-by-step transferred and interrupted load powers, and the power injected by each V2G station into the distribution network nodes, solve to obtain the minimum load shedding amount and economic cost, and achieve load classification and dispatching in extreme high-temperature scenarios.

[0009] Preferably, the load reduction amount generated by centralized control of node i at time t is:

[0010] Wherein, is the aggregated power change amount of the temperature-controlled loads, is the change amount of the air-conditioning set temperature, is the time set for centralized control of the temperature-controlled loads, is the ambient temperature, is the centralized control load node.

[0011] Preferably, the rated operating power of each air conditioner is set to be the same, and the aggregated power of the temperature-controlled load is a function related only to the external temperature and the set temperature of the air conditioner:

[0012] Among them, is the proportion of the temperature-controlled load in node i, is the load power of node i at time t.

[0013] Preferably, the transferred and interrupted load power is:

[0014] Among them, is the load power transferred out from node i at time t, is the load power transferred into node i at time t, is the general load node.

[0015] Preferably, for the general load node, during the peak load period, demand response incentive measures are taken for interruptible loads and shiftable loads. According to the load power interrupted by users and the load power shifted to the off-peak period, a certain price compensation is given. The user interruptible load willingness model is defined as:

[0016] Among them, is the proportional coefficient of the load that the general load user can interrupt at time t, is the maximum interruptible load proportional coefficient, is the user interruptible load willingness coefficient, is the compensation electricity price per unit power for interrupting the load at time t, is the expected compensation electricity price for the user to interrupt the load; The user shiftable load willingness model is defined as:

[0017] Among them, is the proportional coefficient of the load that the general load user can shift at time t, is the maximum shiftable load proportional coefficient, is the user shiftable load willingness coefficient, is the compensation electricity price per unit power for shifting the load at time t, is the expected compensation electricity price for the user to shift the load.

[0018] Preferably, the power injected by each V2G station into the distribution network node at time t is obtained. It is:

[0019] Wherein, is the number of EVs connected to the V2G station at node i at time t, is the output power of the kth electric vehicle at time t, is the set of distribution network nodes connected to the V2G station, is the set of times when electric vehicles participate in the response, is the output power of the kth electric vehicle.

[0020] Preferably, the travel chains of electric vehicle users are divided into simple chains and complex chains. A simple chain involves one or two types of nodes, and a complex chain involves R, W, and C three types of nodes. The set of states of all electric vehicles in the travel chain L is:

[0021] Wherein, B 0 is the starting point of the travel chain path; B f is the end point of the travel chain path; W 0f is the overall route in the travel chain; L 0f is the driving distance required for the travel chain route; T 0 is the departure time of the travel chain; T f is the arrival time of the travel chain; T 0f is the travel time; T p is the parking time.

[0022] Preferably, the objective function of the optimization model is as follows:

[0023]

[0024] Wherein: is the weight coefficient for balancing the load shedding amount and the economic cost, is the weight coefficient of the load importance of node is the load shedding amount of node at time, is the total cost of electric vehicles participating in power supply, is the total cost of demand response, is node The load power interrupted at time, T is the set of time periods within a day.

[0025] Preferably, the constraint conditions of the optimization model include: The load reduction amount generated by centralized control at node i at time t ; the power of the shiftable load at node i at time t ; the load power transferred out from node i at time t ; the load power transferred into node i at time t ; The willingness of electric vehicle users to participate in the response:

[0026] Among them, is the proportionality coefficient of electric vehicle users' participation in the response at time t, is the willingness coefficient of electric vehicle users, is at the unit power compensation electricity price of electric vehicle users at time, is the expected unit power compensation electricity price of electric vehicle users, is the actual number of electric vehicles participating in the dispatch at time t; N all is the total number of electric vehicles in the distribution network area; Obtain the power injected by each V2G station into the distribution network node at time t:

[0027]

[0028] Among them, is the power injected by the V2G station at node i at time t, is the number of electric vehicles connected to the V2G station at node i at time t, is the output power of the kth electric vehicle at time t, is the set of distribution network nodes connected to the V2G station, is the set of times when electric vehicles participate in the response, is the output power of the kth electric vehicle; Temperature constraint of thermostatic load:

[0029] Among them, is the lowest target temperature set by the air conditioner, is the highest target temperature set by the air conditioner; Upper and lower limit constraints of interruptible load power:

[0030] Transferable load power upper and lower limit constraints:

[0031] Among them, is the maximum transferable load ratio coefficient; Electric vehicle reverse power transmission constraints:

[0032] Among them, is the maximum discharge power of the electric vehicle; V2G node access quantity constraints:

[0033] Among them, is the maximum access quantity of V2G nodes; Power constraints:

[0034]

[0035]

[0036] Among them, and respectively represent t the actual active power injection and reactive power injection at node i during the and respectively represent t the active power output and reactive power output of the generator at node i during the and respectively represent t the active power demand and reactive power demand at node i during the is the set of nodes connected to node i by branches; and respectively represent t the active power and reactive power flowing from node i to node j during the and respectively represent t the active power and reactive power flowing from node j to node i during the and respectively represent the resistance and reactance of branch ; For t the lower branch of the time period the square of the modulus of the transmitted current; f and b is a 0-1 variable that specifies the forward and reverse directions of the branch power flow, f = 1, b = 0 represents that the forward direction of the power flow is selected from node i to node j ; Voltage constraint:

[0037]

[0038] Among them, and respectively represent the upper and lower limits of the voltage modulus of node i ; is t the voltage modulus of node i at the time period; is a 0-1 variable that reflects the operating state of branch ; K is a positive real number; Current constraint:

[0039] Among them, and respectively represent the upper and lower limits of the modulus of the transmitted current of branch ; E is the set of branches in the distribution network; Radial constraint:

[0040]

[0041]

[0042] Among them, and are both 0-1 variables used to represent the actual power flow direction of branch at the time period t , is the set of branches directly connected to the balanced node of the distribution network; Second-order cone constraint:

[0043] Among them, is t the power flow from node i to node at the time periodj The active power is t the reactive power flowing from node i to node j during a certain period.

[0044] In a second aspect, an embodiment of the present invention provides a load classification and dispatching system under an extreme high temperature scenario, including: A load module that classifies the loads of each node in the distribution network into important loads, centralized control loads, and general loads; A regulation module that uniformly regulates the temperature-controlled loads in the centralized control loads to obtain the load reduction amount; A response module that establishes a demand-side response mechanism for general loads, interrupts or transfers some loads to off-peak periods to obtain the transferred and interrupted load powers; A power module that treats electric vehicle charging as a general load. During peak load periods, electric vehicles discharge in the form of vehicle grid connection to provide power support to the distribution network and obtain the power injected by each V2G site into the distribution network nodes; A dispatching module that establishes an optimization model based on the obtained load reduction amount, transferred and interrupted load powers, and the power injected by each V2G site into the distribution network nodes, solves to obtain the minimum load shedding amount and economic cost, and realizes load classification and dispatching under an extreme high temperature scenario.

[0045] In a third aspect, a computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned load classification and dispatching method under an extreme high temperature scenario are implemented.

[0046] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium including a computer program. When the computer program is executed by a processor, the steps of the above-mentioned load classification and dispatching method under an extreme high temperature scenario are implemented.

[0047] In a fifth aspect, a chip includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned load classification and dispatching method under an extreme high temperature scenario are implemented.

[0048] In a sixth aspect, an embodiment of the present invention provides an electronic device including a computer program. When the computer program is executed by the electronic device, the steps of the above-mentioned load classification and dispatching method under an extreme high temperature scenario are implemented.

[0049] Compared with the prior art, the present invention has at least the following beneficial effects: A load classification and dispatching method for extreme high-temperature scenarios adopts a three-level load classification mechanism to prioritize the power supply for important loads, achieve precise dispatching through differential control, and effectively improve the emergency response ability of the power grid. Second, it innovatively integrates three means: temperature-controlled load centralized control and regulation, demand response, and V2G reverse power supply to form a multi-dimensional load regulation system. Among them, the unified regulation of temperature-controlled loads can quickly reduce high-energy-consuming loads such as air conditioners, demand response realizes the temporal and spatial transfer of loads, and the V2G technology of electric vehicles taps the potential of distributed energy storage. Third, a multi-objective optimization model is constructed, taking into account the synergistic effects of load reduction, transfer power, and V2G support power, achieving the best economy under the premise of ensuring power supply reliability. Through hierarchical control and multi-source coordination, it significantly improves the power grid's ability to cope with extreme high temperatures, reduces the risk of equipment overload, and reduces the economic losses caused by power outages. At the same time, it takes into account the energy consumption needs of users and the economy of power grid operation, and has high practical value.

[0050] Furthermore, through the three-level load classification mechanism (important / centralized control / general), differential and precise dispatching is achieved. The power supply reliability of important loads is prioritized to avoid the risk of power outages for key facilities; the centralized control of centralized control loads reduces the coordination cost and lays a foundation for the unified management of temperature-controlled loads; the flexible regulation of general loads releases the regulation potential. This hierarchical strategy not only meets the supply guarantee priority in extreme scenarios but also improves the overall dispatching efficiency, avoiding resource waste or safety hazards caused by "one-size-fits-all".

[0051] Furthermore, centralized control and regulation are implemented for temperature-controlled loads such as air conditioners, and "flexible peak shaving" is achieved by using their thermal inertia. By uniformly regulating the temperature setting value or start-stop cycle, the equivalent load power can be quickly reduced without significantly affecting the user's physical sensation, alleviating the overload pressure on the distribution network line. Compared with direct power-off, this method takes into account both electricity demand and grid safety, reduces the risk of social resistance, and has a fast regulation response speed, making it suitable for short-term emergency regulation in high-temperature scenarios.

[0052] Furthermore, a demand-side response incentive mechanism is established to guide general loads to actively participate in peak shaving. Through electricity price compensation or protocol interruption, transferable loads (such as non-continuous production equipment) are adjusted to off-peak hours, and rigid loads (such as low-priority lighting) are reduced to achieve the redistribution of load time and space. This measure taps the flexible resources on the user side, reduces the total load demand during peak hours, delays the investment in grid upgrading, and at the same time balances the interests of users and grid safety through economic means, improving the fairness of dispatching.

[0053] Furthermore, the V2G technology of electric vehicles is innovatively utilized to transform it from a single charging load into a two-way regulation resource. During peak hours, the on-vehicle battery is called to discharge to support the power grid, which not only alleviates the pressure of charging loads but also plays the role of distributed energy storage. This strategy improves the power balance ability of distribution network nodes, reduces the standby demand of traditional power generation, and at the same time creates benefits for users through the price difference between charging and discharging, promoting the consumption of new energy and the coordinated optimization of the transportation-energy system.

[0054] Furthermore, a multi-objective optimization model is constructed to integrate the load regulation amount and V2G support power in the first three steps to achieve global optimal scheduling. The model simultaneously minimizes the load shedding amount (ensuring power supply) and economic cost (compensation cost + V2G loss), and uses linear programming or intelligent algorithms for rapid solution to ensure the scientificity and real-time nature of decisions in extreme scenarios. By quantitatively evaluating the contribution degrees of various measures and optimizing the resource allocation ratio, the overall efficiency loss caused by local optimization is avoided, and the feasibility of the solution is improved.

[0055] It can be understood that the beneficial effects of the second to sixth aspects above can be referred to the relevant descriptions in the first aspect above, and will not be elaborated here.

[0056] In summary, the present invention combines load classification control, temperature control centralized control regulation, demand response and V2G collaborative peak shaving, and combines multi-objective optimization to minimize economic costs while ensuring power supply reliability, and improve the resilience of the power grid under extreme high temperatures.

[0057] The technical solutions of the present invention will be further described in detail below with reference to the drawings and embodiments. Description of the Drawings

[0058] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application, and those of ordinary skill in the art can also obtain other drawings based on these drawings without creative efforts.

[0059] Figure 1 is the flowchart of the present invention; Figure 2 is the partition and node information of the IEEE 33-node distribution network; Figure 3 is the topological graph of the 30-node transportation network; Figure 4 is the schematic diagram of the computer device provided by an embodiment of the present invention; Figure 5 is the block diagram of an electronic device provided by an embodiment of the present invention.

[0060] Among them, 60. computer device; 61. processor; 62. memory; 63. computer program; 600. electronic device; 610. processing unit; 620. storage unit; 6201. random access storage unit; 6202. cache storage unit; 6203. read-only storage unit; 6204. program / utilities; 6205. program module; 630. bus; 640. display unit; 650. input / output interface; 660. network adapter; 700. external device. Detailed implementation mode

[0061] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without making creative efforts fall within the protection scope of the present invention.

[0062] In the description of the present invention, it should be understood that the terms "include" and "comprise" indicate the presence of the described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or their combinations.

[0063] It should also be understood that the terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include the plural forms.

[0064] It should be further understood that the term "and / or" used in the specification of the present invention and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in the present invention generally represents an "or" relationship between the contextually related objects.

[0065] It should be understood that although the terms first, second, third, etc. may be used in the embodiments of the present invention to describe preset ranges, etc., these preset ranges should not be limited to these terms. These terms are only used to distinguish the preset ranges from each other. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0066] Depending on the context, the word "if" as used herein can be interpreted as "when" or "while" or "in response to determining" or "in response to detecting". Similarly, depending on the context, the phrase "if determined" or "if detected (stated condition or event)" can be interpreted as "when determined" or "in response to determining" or "when detected (stated condition or event)" or "in response to detecting (stated condition or event)".

[0067] Various structural schematic diagrams according to the disclosed embodiments of the present invention are shown in the accompanying drawings. These figures are not drawn to scale, where for the purpose of clear expression, some details are enlarged and some details may be omitted. The shapes of various regions and layers shown in the figures and their relative sizes and positional relationships are merely exemplary. In practice, there may be deviations due to manufacturing tolerances or technical limitations, and those skilled in the art can design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0068] The present invention provides a load classification and scheduling method for extreme high-temperature scenarios. By classifying the loads in the distribution network into important loads, centralized control loads, and general loads, and taking corresponding control measures for different load types, the power supply pressure of the distribution network during peak hours is alleviated. Specifically, this method ensures that important loads are not powered off under extreme high temperatures, centrally adjusts the temperature of temperature-controlled loads such as air conditioners in the centralized control loads to reduce the load, and at the same time establishes a demand-side response mechanism for general loads to reduce the grid burden by interrupting or transferring some loads. The present invention also regards electric vehicles as a special two-way load, providing power support to the grid during peak hours through V2G technology; simulating the travel behavior of electric vehicles through a travel chain model to quickly obtain the spatio-temporal distribution and battery state of EVs at different times, so as to count the number and state of charge of EVs under V2G nodes, and thus obtain the power support information that V2G nodes can provide. Finally, the present invention establishes an optimization model that comprehensively considers the load shedding volume, V2G cost, and demand-side response cost. By assigning weights to the load shedding amount and scheduling cost of different loads, the load shedding amount is minimized while ensuring economic scheduling, and the reliability and economy of power supply are ensured.

[0069] Embodiment 1 Please refer to Figure 1 , a load classification and scheduling method for extreme high-temperature scenarios of the present invention includes the following steps: S1. Classify the loads of each node in the distribution network into important loads, centralized control loads, and general loads; In extreme high-temperature situations, important loads should not be powered off during peak load hours; the temperature-controlled loads in the centralized control loads are uniformly controlled to achieve the effect of load reduction; a demand-side response mechanism is established for general loads to interrupt or transfer some loads to non-peak hours.

[0070] (1) Among them, is the set of distribution network nodes, is the set of important load nodes, is the set of centralized control load nodes, is the set of general load nodes.

[0071] The present invention regards an electric vehicle (EV) as a special two-way load. When the EV is charging, it is regarded as a general load and has the property of being transferable. During peak load periods, the EV discharges in the form of vehicle to grid (V2G) and provides power support to the distribution network through a V2G station, so as to make full use of the mobile resources in the transportation network to ensure power supply.

[0072] S2. Uniformly regulate the temperature control loads in the centralized control loads to achieve the effect of load reduction and obtain the load reduction amount. ; For the centralized control load nodes, according to the thermal comfort standard of ISO Standard 7730 and the predicted mean vote thermal comfort model, centralized temperature regulation measures are taken for the temperature control loads represented by air conditioners. During peak load periods, the target temperature set by the air conditioner is increased to achieve the effect of load reduction at the cost of sacrificing thermal comfort.

[0073] The load model of a single air conditioner can be represented by an equivalent thermodynamic parameter model as follows: (2) (3) Where, and are the internal and external environmental temperatures at time t; C and R are the equivalent heat capacity and thermal resistance of the internal environment; is a Boolean variable, taking 0 to indicate that the air conditioner is out of operation and 1 to indicate that it is running; is the rated operating power of the r-th air conditioner; is the energy efficiency ratio of the air conditioner; and are the minimum and maximum values of the internal temperature when the air conditioner is running; is an extremely small time delay.

[0074] The target temperature set by the air conditioner and , The relationship is (4) Where, is the allowable temperature deviation when the air conditioner is running.

[0075] From equation (1), the running time and the out-of-operation time of the air conditioner are: (5) (6) The probability that a single air conditioner is in the running state is: (7) Then the aggregated power of r air conditioners under node i at time t is expressed as: (8) where, is the total number of air conditioners turned on at node i at time t.

[0076] In the present invention, it is set that the rated operating power of each air conditioner is the same. Therefore, the aggregated power of the temperature-controlled load can be simplified to the following function that is only related to the external temperature and the set temperature of the air conditioner: (9) where, is the proportion of the temperature-controlled load in node i, is the load power of node i at time t.

[0077] By increasing the target temperature set by the air conditioner and reducing the aggregated power of the temperature-controlled load, the load reduction amount generated by node i through centralized control at time t is: (10) where, is the change amount of the aggregated power of the temperature-controlled load, is the change amount of the set temperature of the air conditioner, is the time set for centralized control of the temperature-controlled load.

[0078] S3. Establish a demand response mechanism for general loads, interrupt or transfer part of the load to off-peak hours to obtain the load power of user transfer and interruption; For general load nodes, demand response incentive measures are taken for interruptible loads and transferable loads during peak load hours. According to the load power interrupted by users and the load power transferred to off-peak hours, a certain price compensation is given.

[0079] Define the user interrupt load willingness model as: (11) where, is the proportional coefficient of the load that general load users can interrupt at time t, is the maximum interruptible load proportional coefficient, is the user interrupt load willingness coefficient, is the compensation electricity price per unit power for interrupting the load at time t, is the expected compensation electricity price for users to interrupt the load.

[0080] Similarly, define the user transfer load willingness model as: (12) Among them, is the proportionality coefficient of the transferable load of general load users at time t, is the maximum transferable load proportionality coefficient, is the willingness coefficient of general load users to transfer load, is the unit power compensation electricity price for transferring load at time t, is the expected compensation electricity price for users to transfer load.

[0081] The power of the transferable load consists of the transferred-out power and the transferred-in power, and it is necessary to clarify the time of load transfer-out and transfer-in at the time level: (13) (14) (15) (16) Among them, is the power of the transferable load at node i at time t, is the load power transferred out from node i at time t, is the load power transferred into node i at time t, and are the time periods when transfer-out and transfer-in are not allowed, respectively.

[0082] S4. Regard electric vehicles as a special two-way load. When charging, they can be regarded as general loads. During peak load periods, EVs discharge in the form of vehicle-to-grid (V2G) to provide power support to the distribution network, and obtain the power injected into the distribution network nodes by each V2G site ; Since the scheduling and status of EVs need to consider the information of the power grid and the road network, an activity chain model is used to simulate the travel behavior of EVs, quickly obtain the spatio-temporal distribution of EVs at different times and the state of the vehicle batteries within the scheduling period, obtain the willingness of EV users to accept the guidance of nearby V2G sites during peak load periods, count the number of EVs and the state of charge at each V2G node, and obtain the power support information that the V2G nodes can provide.

[0083] The topological structure of the road network is composed of traffic nodes and a set of streets, which is expressed as: (17) Among them, is an undirected graph depicting the topological structure of the road network; is the set of road network nodes; is the set of connecting lines between road network nodes.

[0084] While serving as a node in the road network, the V2G station also has electrical characteristics, that is, the EV will transmit electric energy to the power grid at the V2G station on the road network node. Therefore, it is necessary to construct the coupling relationship between the power grid and the road network node.

[0085] (18) Among them, is the set of all associated edges connecting the power grid and the transportation network; represents an element in the set of connecting edges ; is the set of power grid nodes i ; is the set of nodes r in the road network; is a 0-1 variable used to represent whether the road network node and the power grid node are coupled. If then it means that this is a V2G node and can transmit electric energy to the power grid through the road network node. Otherwise, it means that there is no coupling relationship.

[0086] The present invention divides the actual corresponding road network area in the urban distribution network into residential areas, working areas and commercial areas. The corresponding sets of road network nodes are respectively denoted as R 、 W and C . The EV user travel chains are divided into two types: simple chains and complex chains. Among them, the simple chain involves one or two types of nodes, while the complex chain involves R, W, and C three types of nodes.

[0087] (19) Among them, L represents the set of states of all EVs in the travel chain, including the following variables: The starting point of the travel chain path B 0; the end point of the travel chain path B f ; the overall route in the travel chain W 0f ; the driving distance required for the travel chain route L 0f ; the departure time of the travel chain T 0; the arrival time of the travel chain T f ; the travel time T 0f ; the parking time T p .

[0088] In extremely high temperature weather, the travel willingness of EV users will change due to the influence of temperature. Define the travel willingness of EV users as: (20) Among them, is the travel chain correction coefficient, is the predicted dissatisfaction ratio index in the human comfort index standard, and the value of this index changes with temperature.

[0089] The travel chain considering the travel willingness of users is shown in Table 1 Table 1 Travel Chain Table Considering the Travel Willingness of Users

[0090] Introduce a driving state set to record the real-time state of each EV at each moment t, which is used as the input parameter for the interaction between the V2G station and the power grid during the post-disaster stage.

[0091] (21) Among them, E t represents t the real-time state information of the EV at moment C t is the battery power of the EV at t moment; W t is the driving state, that is, it represents t the location of the EV in the road network at moment O t is the number of the V2G node closest to the EV at t moment.

[0092] The willingness of EV users to participate in the response will change with the change of the compensation electricity price. Define the willingness of EV users to participate in the response: (22) Among them, is the proportion coefficient of EV users participating in the response at moment t, is the willingness coefficient of EV users, is the unit power compensation electricity price of EV users at moment, is the expected unit power compensation electricity price of EV users. is the actual number of EVs participating in the dispatch at moment t; N all is the total number of EVs in the distribution network area.

[0093] According to the real-time state information of each EV and the willingness of EV users to participate in the response, the power injected by each V2G station into the distribution network node at moment t can be obtained: (23) (24) Among them, is the power injected by the V2G station at node i at time t, is the number of EVs connected to the V2G station at node i at time t, is the output power of the k-th EV at time t, is the set of distribution network nodes connected to the V2G station, is the set of times when EVs participate in response, is the output power of the k-th EV.

[0094] S5. Based on what is obtained from S2 - S4 , , and , an optimization model is established, and the minimum load shedding amount and economic cost are obtained by solving.

[0095] In an extreme high-temperature scenario, the overall objective of the optimization model established by the present invention includes the load shedding amount, V2G cost, and demand-side response cost. Weights are assigned to the load loss power and the scheduling cost respectively to minimize the load loss power while ensuring economic scheduling. Since the classified scheduling of various types of loads can involve diversified cost factors, the present invention assigns weights to the load loss power and the scheduling cost respectively, and the weights can be adjusted according to the actual situation to minimize the load loss power while ensuring economic scheduling Objective function: (25) (26) Among them: is the weight coefficient for balancing the load shedding amount and the economic cost, is the weight coefficient of the load importance of node , is node at time's load shedding amount, is the total cost of electric vehicles participating in power supply, is the total cost of demand-side response. is the load power interrupted at node at time, T is the set of time periods within a day. In the present invention, one hour is taken as a time period, with a total of 24 time periods.

[0096] Constraint conditions: Based on the constraints established by formulas (10), (13) - (16), (22) - (24), the optimization model also needs to satisfy the following constraints: (1) Temperature constraint of temperature-controlled load: (27) Wherein: is the lowest target temperature set for the air conditioner, is the highest target temperature set for the air conditioner.

[0097] (2) Constraints on the upper and lower limits of the interruptible load power: (28) (3) Constraints on the upper and lower limits of the transferable load power: (29) Wherein, is the maximum transferable load ratio coefficient.

[0098] (4) Constraint on the power of the reverse power supply of electric vehicles: (30) Wherein, is the maximum discharge power of the EV.

[0099] (6) Constraint on the number of V2G node accesses: (31) Wherein, is the maximum number of V2G node accesses.

[0100] (7) Power constraint (32) (33) (34) Wherein: and respectively represent t the actual active power injection and reactive power injection at node i during the time period; and respectively represent t the active power output and reactive power output of the generator at node i during the time period; and respectively represent t the active power demand and reactive power demand at node i during the time period; is the set of nodes that are connected to node i by branches; and respectively represent t the active power and reactive power flowing from node i to node j during the time period; and respectively representt Active power and reactive power flowing from the subordinate node j to the node i during the and respectively represent the resistance and reactance of branch ; is t the square of the modulus of the transmission current of branch during the f and b are 0-1 variables that define the positive and negative directions of the branch power flow. f = 1, b = 0 indicates that the positive direction of the power flow is selected from node i to node j .

[0101] (8) Voltage constraint (35) (36) Where: and respectively represent the upper and lower limits of the voltage modulus of node i ; is t the voltage modulus of node i during the is a 0-1 variable that reflects the operating state of branch . Taking 1 indicates that the line is operating normally; K is a very large positive real number.

[0102] (9) Current constraint (37) Where: and respectively represent the upper and lower limits of the modulus of the transmission current of branch ; E is the set of branches in the distribution network.

[0103] (10) Radial constraint (38) (39) (40) Where: and are both 0-1 variables used to represent the actual power flow direction of branch during the t period. If it means that the branch power flow direction is the positive direction. If It indicates that the branch power flow direction is opposite; is the set of branches directly connected to the balancing node of the distribution network.

[0104] (11) Second-order cone constraint (41) This multi-period scheduling model is a mixed-integer programming problem, and commercial solvers (such as CPLEX and Gurobi) can be directly called for solution.

[0105] Those skilled in the art of the present technology can understand that various aspects of the present invention can be implemented as a system, a method, or a program product. Therefore, various aspects of the present invention can be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or an implementation combining hardware and software aspects, which can be collectively referred to as "circuit", "module", or "platform" here.

[0106] Embodiment 2 The present invention provides a load classification scheduling system under an extreme high temperature scenario, which can be used to implement the above-mentioned load classification scheduling method under an extreme high temperature scenario. Specifically, the load classification scheduling system under an extreme high temperature scenario includes a load module, a regulation and control module, a response module, a power module, and a scheduling module.

[0107] Among them, the load module classifies the loads of each node in the distribution network into important loads, centralized control loads, and general loads; The regulation and control module uniformly regulates the temperature control loads in the centralized control loads to obtain the load reduction amount; The response module establishes a demand-side response mechanism for general loads, interrupts or transfers part of the loads to off-peak periods to obtain the transferred and interrupted load powers; The power module regards electric vehicles as general loads when charging. During peak load periods, electric vehicles discharge in the form of vehicle grid connection to provide power support for the distribution network to obtain the power injected by each V2G site into the distribution network nodes; The scheduling module establishes an optimization model based on the obtained load reduction amount, transferred and interrupted load powers, and the power injected by each V2G site into the distribution network nodes, solves to obtain the minimum load shedding amount and economic cost, and realizes load classification scheduling under an extreme high temperature scenario.

[0108] Embodiment 3 The present invention provides a terminal device, which includes a processor and a memory. The memory is used to store a computer program, and the computer program includes program instructions. The processor is used to execute the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Graphics Processing Unit (GPU), Tensor Processing Unit (TPU), Digital Signal Processor (DSP), Application Specific Integrated Circuit (ASIC), Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, and is suitable for implementing one or more instructions. Specifically, it is suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding function. The processor described in the embodiments of the present invention can be used for the operation of the load classification and scheduling method in an extremely high-temperature scenario, including: Classify the loads of each node in the distribution network into important loads, centralized control loads, and general loads; uniformly regulate the temperature control loads in the centralized control loads to obtain the load reduction amount; establish a demand response mechanism for general loads, interrupt or transfer part of the loads to off-peak hours to obtain the transferred and interrupted load power; regard electric vehicles as general loads when charging. During peak load hours, electric vehicles discharge in the form of vehicle grid connection to provide power support to the distribution network to obtain the power injected by each V2G site into the distribution network nodes; establish an optimization model based on the obtained load reduction amount, transferred and interrupted load power, and the power injected by each V2G site into the distribution network nodes, and solve to obtain the minimum load shedding amount and economic cost, so as to achieve load classification and scheduling in an extremely high-temperature scenario.

[0109] Please refer to Figure 4 , the terminal device is a computer device. The computer device 60 in this embodiment includes: a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When the computer program 63 is executed by the processor 61, it implements the load classification and scheduling method in an extremely high-temperature scenario in the embodiment. To avoid repetition, it will not be elaborated here one by one. Alternatively, when the computer program 63 is executed by the processor 61, it implements the functions of each model / unit in the load classification and scheduling system in an extremely high-temperature scenario in the embodiment. To avoid repetition, it will not be elaborated here one by one.

[0110] The computer device 60 can be a computing device such as a desktop computer, a notebook, a handheld computer, and a cloud server. The computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art can understand that Figure 4 merely examples of the computer device 60, which do not constitute a limitation on the computer device 60, may include more or fewer components than shown in the figure, or combine certain components, or different components. For example, the computer device may also include input / output devices, network access devices, buses, etc.

[0111] The so-called processor 61 may be a central processing unit (CPU), or may also be other general-purpose processors, a graphics processing unit (GPU), a tensor processing unit (TPU), a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0112] The memory 62 may be an internal storage unit of the computer device 60, such as the hard disk or memory of the computer device 60. The memory 62 may also be an external storage device of the computer device 60, such as a plug-in hard disk equipped on the computer device 60, a smart media card (SMC), a secure digital (SD) card, a flash card, etc.

[0113] Furthermore, the memory 62 may also include both the internal storage unit of the computer device 60 and the external storage device. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 may also be used to temporarily store data that has been output or will be output.

[0114] Please refer to Figure 5 , the terminal device is an electronic device 600, and the electronic device 600 is presented in the form of a general computing device. The components of the electronic device may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including the storage unit 620 and the processing unit 610), a display unit 640, etc.

[0115] Among them, the storage unit stores program code, which can be executed by the processing unit 610, so that the processing unit 610 executes the steps according to various exemplary embodiments of the present invention described in the method section of this specification. For example, the processing unit 610 can execute the steps as shown in Figure 1 .

[0116] The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 6201 and / or a cache storage unit 6202, and may further include a read-only storage unit (ROM) 6203.

[0117] The storage unit 620 may also include a program / utilities 6204 having a set (at least one) of program modules 6205. Such program modules 6205 include, but are not limited to: an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include the implementation of a network environment.

[0118] The bus 630 may represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus structures.

[0119] The electronic device 600 may also communicate with one or more external devices 700 (such as a keyboard, a pointing device, a Bluetooth device, etc.), and may also communicate with one or more devices that enable a user to interact with the electronic device 600, and / or communicate with any device that enables the electronic device 600 to communicate with one or more other computing devices (such as a router, a modem). Such communication may be performed through the input / output interface 650. In addition, the electronic device 600 may also communicate with one or more networks (such as a local area network, a wide area network, and / or a public network, such as the Internet) through the network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 through the bus 630. It should be understood that although not shown in the figure, other hardware and / or software modules may be used in combination with the electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms, etc.

[0120] Example 4 The present invention also provides a storage medium, specifically a computer-readable storage medium, which is a memory device in a terminal device for storing programs and data. It can be understood that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and, of course, the extended storage medium supported by the terminal device. It can be any tangible medium that contains or stores a program, and this program can be used by or in conjunction with an instruction execution system, apparatus, or device. The computer-readable storage medium provides a storage space that stores the operating system of the terminal. And, in this storage space, there are also stored one or more instructions suitable for being loaded and executed by a processor, and these instructions can be one or more computer programs (including program codes). It should be noted that more specific examples of the computer-readable storage medium here include: an electrical connection having one or more wires, a portable disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, an optical fiber, a portable compact disk read-only memory, an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0121] The computer-readable storage medium also includes data signals propagated in a baseband or as part of a carrier wave, in which the readable program code is carried. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The readable storage medium can also be any readable medium other than the readable storage medium, and this readable medium can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium can be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, radio frequency, etc., or any suitable combination of the above.

[0122] The program code for performing the operations of the present invention can be written in any combination of one or more programming languages. The programming languages include object-oriented programming languages - such as Java, C++, etc., and also include conventional procedural programming languages - such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computing device, partially on the user's device, executed as an independent software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user's computing device through any type of network, including a local area network or a wide area network, or can be connected to an external computing device (for example, by using an Internet service provider to connect through the Internet).

[0123] One or more instructions stored in a computer-readable storage medium can be loaded and executed by a processor to implement the corresponding steps of the load classification and scheduling method in the above embodiments in an extreme high temperature scenario; one or more instructions in the computer-readable storage medium are loaded and executed by the processor to perform the following steps: Classify the loads at each node in the distribution network into important loads, centralized control loads, and general loads; uniformly control the temperature-controlled loads in the centralized control loads to obtain the load reduction amount; establish a demand response mechanism for the general loads, interrupt or transfer part of the loads to off-peak hours to obtain the transferred and interrupted load power; regard the electric vehicle charging as a general load, and during the load peak hours, the electric vehicle discharges in the form of vehicle grid connection to provide power support for the distribution network to obtain the power injected by each V2G site into the distribution network node; establish an optimization model based on the obtained load reduction amount, transferred and interrupted load power, and the power injected by each V2G site into the distribution network node, solve to obtain the minimum load shedding amount and economic cost, and realize load classification and scheduling in an extreme high temperature scenario.

[0124] The databases involved in the embodiments provided in the present application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., and is not limited thereto. The processors involved in the embodiments provided in the present application may be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., and is not limited thereto.

[0125] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Usually, the components described and shown in the accompanying drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed present invention, but merely represents selected embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0126] The present invention uses a coupled system composed of an improved IEEE 33-node distribution network and a 30-node transportation network for example analysis. The distribution network partition and the classification information of each node are as Figure 2 shown, and the transportation network information is as Figure 3 shown.

[0127] To deeply study the actual application effectiveness and economic benefit potential of the load classification scheduling strategy, a comparative analysis of the optimal scheduling effects of centralized control, demand-side response, and V2G is carried out. The following four strategy plans are set to explore the lost power and economic costs of each plan. Plan 1: Do not consider centralized control measures, do not consider demand-side response strategies, and do not consider V2G strategies Plan 2: Consider centralized control measures, do not consider demand-side response strategies, and do not consider V2G strategies Plan 3: Consider centralized control measures, consider demand-side response strategies, and do not consider V2G strategies Plan 4: Consider centralized control measures, consider demand-side response strategies, and consider V2G strategies Table 1 Scheduling results of each plan

[0128] Comparing the data in Table 1, compared with the previous plan, Plan 2, Plan 3, and Plan 4 have achieved a reduction of 21.53%, 46.37%, and 32.10% respectively in load shedding, and a reduction of 23.37%, 43.07%, and 18.77% respectively in the comprehensive economic cost. This shows the effectiveness of centralized control measures, demand-side response strategies, and V2G strategies. They have significantly improved the power supply guarantee ability of the distribution network and are of great significance for saving economic costs.

[0129] In summary, for a load classification scheduling method and system in an extreme high temperature scenario of the present invention, through the coordinated scheduling of various resources, the power supply guarantee ability of the distribution network in the extreme high temperature scenario can be effectively improved.

[0130] The above content is only to illustrate the technical idea of the present invention and cannot be used to limit the protection scope of the present invention. Any modification made on the basis of the technical solution according to the technical idea proposed by the present invention falls within the protection scope of the claims of the present invention.

Claims

1. A load classification scheduling method under extreme high temperature scenarios, characterized in that: The following steps are involved: The loads at each node in the distribution network are divided into important loads, centralized control loads and general loads; The temperature control load in the centralized control load is uniformly regulated to obtain the load reduction amount ; Establish a demand-side response mechanism for general loads, interrupt or transfer part of the load to non-peak hours, and obtain the transferred and interrupted load power; When charging, electric vehicles are regarded as general loads. During peak load periods, electric vehicles discharge in the form of grid-connected vehicles, providing power support to the distribution network, and obtaining the power injected by each V2G site into the distribution network node; An optimization model is established based on the obtained load reduction amount, transferred and interrupted load power, and the power injected by each V2G site into the distribution network node to solve the minimum load loss amount and economic cost, thereby realizing load classification scheduling under extreme high temperature scenarios.

2. The load classification scheduling method under extreme high temperature scenarios according to claim 1 is characterized in that: The load reduction amount generated by centralized control at node i at time t for: in, is the aggregate power variation of the temperature control load, The change in the air conditioner set temperature, is the time set for centralized control of temperature control load, is the ambient temperature, It is the centralized control load node.

3. The load classification scheduling method under extreme high temperature scenarios according to claim 2 is characterized in that: Assuming that the rated operating power of each air conditioner is the same, the aggregate power of the temperature control load is a function related only to the external temperature and the air conditioner set temperature: in, is the proportion of temperature control load in node i, is the load power of node i at time t.

4. The load classification scheduling method under extreme high temperature scenarios according to claim 1 is characterized in that: Transfer and interrupt load power for: in, is the load power transferred from node i at time t, is the load power transferred to node i at time t, It is a general load node.

5. The load classification scheduling method under extreme high temperature scenarios according to claim 4 is characterized in that: For general load nodes, demand-side response incentives are adopted for interruptible loads and transferable loads during peak load periods. A certain price compensation is given according to the load power interrupted by the user and the load power transferred to the non-peak period. The user interruption load willingness model is defined as: in, is the ratio coefficient of the load that can be interrupted by general load users at time t, is the maximum interruptible load ratio factor, is the load interruption willingness coefficient of general load users, is the unit power compensation price for interrupted load at time t, Anticipated compensation price for users’ interrupted load; The user load transfer willingness model is defined as: in, is the ratio coefficient of load transfer that general load users can achieve at time t, is the maximum transferable load proportional coefficient, is the load transfer willingness coefficient of general load users, is the unit power compensation price for the transferred load at time t, The expected electricity price to compensate users for transferring load.

6. The load classification scheduling method under extreme high temperature scenarios according to claim 1 is characterized in that: The power injected by each V2G station into the distribution network node at time t is obtained for: in, is the number of EVs connected to the V2G station under node i at time t, is the output power of the kth electric car at time t, is the set of distribution network nodes connected to the V2G station. is the time set of electric vehicles participating in the response, is the output power of the kth electric car.

7. The load classification scheduling method under extreme high temperature scenarios according to claim 6 is characterized in that: Electric vehicle user travel chains are divided into simple chains and complex chains. Simple chains involve one or two types of nodes, while complex chains involve R, W, and C Three types of nodes, the state set of all electric vehicles in the travel chain L for: in, B 0 is the starting point of the travel chain path; B f is the end point of the travel chain path; W 0f is the overall route in the trip chain; L 0f The required driving distance for the travel link line; T 0 is the departure time of the trip chain; T f is the trip chain arrival time; T 0f For travel time; T p For parking time.

8. The load classification scheduling method under extreme high temperature scenarios according to claim 1 is characterized in that: The objective function of the optimization model is as follows: in: To balance the weight coefficient of lost load and economic cost, For Node The weight coefficient of load importance, For Node exist The load shedding at the moment, The total cost of supplying electricity to electric vehicles, is the total cost of demand-side response, For Node exist The load power that is interrupted at the moment, T A collection of time periods within a day.

9. The load classification scheduling method under extreme high temperature scenarios according to claim 8 is characterized in that: The constraints of the optimization model include: The load reduction amount generated by centralized control at node i at time t ; The power of the load that can be transferred from node i at time t ; Load power transferred from node i at time t ; Load power transferred to node i at time t ; Electric vehicle users’ willingness to participate in response: in, is the proportion coefficient of electric vehicle users’ participation in response at time t, is the electric vehicle user willingness coefficient, For The unit power compensation electricity price for electric vehicle users at the moment, The expected unit power compensation electricity price for electric vehicle users, is the number of electric vehicles actually participating in the dispatch at time t; N all is the total number of electric vehicles in the distribution network area; The power injected by each V2G station into the distribution network node at time t is obtained: in, is the power injected by the V2G station under node i at time t, is the number of electric vehicles connected to the V2G station at node i at time t, is the output power of the kth electric car at time t, is the set of distribution network nodes connected to the V2G station. is the time set of electric vehicles participating in the response, is the output power of the kth electric vehicle; Temperature control load temperature constraint: in, The minimum target temperature set for the air conditioner, The maximum target temperature set for the air conditioner; Interruptible load power upper and lower limit constraints: The upper and lower limits of transferable load power are: in, is the maximum transferable load ratio coefficient; Electric vehicle return power constraints: in, is the maximum discharge power of the electric vehicle; V2G node access quantity constraints: in, Maximum number of V2G nodes connected; Power Constraints: in, and Respectively t Node under time period i The actual injected active power and reactive power; and Respectively t Node under time period i The active and reactive output of the generator; and Respectively t Node under time period i Active power demand and reactive power demand; For the node i There is a set of nodes connected by branches; and Respectively t From the node in the time period i Flow to Node j Active power and reactive power; and Respectively t From the node in the time period j Flow to Node i Active power and reactive power; and Respectively represent branches resistance and reactance; for t Time period branch The square of the modulus of the transmitted current; f and b It is a 0-1 variable, which specifies the positive and negative direction of the branch flow. f =1, b =0 means the positive direction of the flow is selected from the node i To Node j ; Voltage Constraints: in, and Respectively represent nodes i The upper and lower limits of the voltage modulus; for t Node under time period i Voltage modulus; It is a 0-1 variable, reflecting the branch The operating status of K is a positive real number; Current Constraint: in, and Respectively represent branches The upper and lower limits of the transmission current modulus; E is a collection of branches in the distribution network; Radial Constraints: in, and All are 0-1 variables, used to represent branches exist t The actual tidal flow direction during the time period, It is the set of branches directly connected to the balancing node of the distribution network; Second-order cone constraint: in, for t From the node in the time period i Flow to Node j The active power, for t From the node in the time period i Flow to Node j of reactive power.

10. A load classification and dispatching system under extreme high temperature scenarios, characterized in that: include: Load module, which divides the load of each node in the distribution network into important load, centralized control load and general load; The control module uniformly controls the temperature control load in the centralized control load to obtain the load reduction amount; The response module establishes a demand-side response mechanism for general loads, interrupts or transfers part of the load to non-peak hours, and obtains the transferred and interrupted load power; The power module treats the electric vehicle as a general load when it is charging. During the peak load period, the electric vehicle discharges in the form of vehicle grid connection, provides power support to the distribution network, and obtains the power injected by each V2G site into the distribution network node; The scheduling module establishes an optimization model based on the obtained load reduction, transferred and interrupted load power, and the power injected by each V2G site into the distribution network node, and solves the minimum load loss and economic cost to achieve load classification scheduling under extreme high temperature scenarios.

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