Urban power grid elasticity improvement method and system considering multi-level resource collaboration
By constructing a multi-level resource-coordinated urban power grid optimization model, the problem of poor resource coordination after the access of new energy sources and flexible loads was solved, thereby realizing the elasticity improvement and economic optimization of the urban power grid.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- STATE GRID JIANGSU ELECTRIC POWER CO LTD NANJING POWER SUPPLY COMPANY
- Filing Date
- 2025-12-04
- Publication Date
- 2026-04-10
AI Technical Summary
When faced with the integration of various new energy sources and flexible loads, urban power grids suffer from poor resource coordination and insufficient flexibility. In particular, given the uncertainty of wind and solar power output and the high randomness of electric vehicles, it is difficult to achieve optimized scheduling of multi-level resources.
A two-layer optimization model based on wind and solar power output forecasting, electric vehicle scheduling, and air conditioning satisfaction index is constructed. Through the coordinated optimization of shared energy storage systems and electric vehicle charging stations, a multi-objective urban power grid optimization scheduling is formed to improve the resilience and economy of the power grid.
It has improved the resource utilization efficiency of the power grid, enhanced its adaptability to wind and solar power output, optimized the dispatching of electric vehicles and air conditioners, and improved the overall resilience and economy of the urban power grid.
Smart Images
Figure CN121836166A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The application belongs to the technical field of power distribution systems, and particularly relates to a city power grid elasticity improvement method and system considering multi-level resource coordination. BACKGROUND
[0002] In recent years, with the development of city power grids, the past centralized power supply mode to the whole network is changing, the installed capacity of new energy represented by wind power and photovoltaic is increasing year by year, and new city power grids introduce various types of resources and distribute in different levels, mainly represented by distributed photovoltaic and energy storage devices. The large-scale popularity of electric vehicles has also brought great changes to the overall pattern of city power grids. The access point of electric vehicles is highly random, and the state of charge at the time of access is closely related to traffic conditions, road network models and other factors, and the uncertainty is strong, but electric vehicles can interact with power grids, so that electric vehicles can be applied to city power grids as virtual energy storage. At the same time, the proportion of air conditioning load in city power grids is gradually rising, and thanks to the construction of intelligent buildings, air conditioning has gradually become a dispatchable object, and the temperature set by air conditioning is adjusted to regulate load demand. At the same time, unlike the past one-way transmission of electric energy between different levels, there is also power interaction between different levels of new city power grids. In addition, with the development of climate change and power systems, the demand for city power grid elasticity is constantly expanding. Therefore, it is necessary to coordinate and optimize resources between multiple levels of city power grids to improve the elasticity of city power grids. SUMMARY
[0003] In order to solve the above problems, the application provides a city power grid elasticity improvement method and system considering multi-level resource coordination.
[0004] The application adopts the following technical scheme.
[0005] In a first aspect, the application discloses a city power grid elasticity improvement method considering multi-level resource coordination, which comprises the following steps: S1, based on the wind power and photovoltaic output prediction value of the power distribution network side, a wind power and photovoltaic equal incremental-attenuation scenario set of the power distribution network side is constructed; an electric vehicle dispatching data cluster based on user travel demand is constructed; an air conditioner satisfaction index considering the health state of power grid operation is proposed, and an intelligent building temperature control model is constructed based on the air conditioner satisfaction index; S2, based on the electric vehicle dispatching data cluster and the intelligent building temperature control model, a double-layer optimization model is constructed with the maximum shared flexibility resource operator comprehensive benefit as the upper layer and the minimum multi-intelligent building community energy consumption cost as the lower layer; the shared flexibility resource operator includes a shared energy storage power station and an electric vehicle charging station; the double-layer optimization model is solved to obtain the optimal shared energy storage system configuration capacity and the optimal electric vehicle cluster dispatching capacity; S3. Based on the optimal shared energy storage system configuration capacity and the optimal electric vehicle cluster scheduling capacity, a multi-objective urban power grid optimization scheduling model is constructed, which considers the proportional incremental-decrease scenario of wind power and photovoltaic power on the distribution network side, and takes into account the economic objectives of multiple smart building communities and the elastic objectives of the distribution network side. The multi-objective urban power grid optimization scheduling model is solved to obtain the optimal urban power grid operation scheme.
[0006] More preferably, In S1, the construction of an electric vehicle scheduling data cluster based on user travel demand specifically includes: Construct an electric vehicle trip matrix that considers terrain factors, where the rows and columns of the trip matrix correspond to traffic network nodes, and the elements in the matrix... Indicating the transportation network i , j Reference distance between two nodes; The sample data of electric vehicle trips is generated by randomizing the Monte Carlo sampling. The sample data of electric vehicle trips includes the starting point and destination, vehicle type, state of charge at departure, charging and discharging power of electric vehicle, parking time and whether it is connected to the power grid. Based on the electric vehicle travel matrix, the shortest path algorithm is used to calculate the shortest path and shortest path reference distance of the electric vehicle from the starting point to the destination, and the power consumption of the electric vehicle from the starting point to the destination is calculated through the shortest path reference distance. The electric vehicle travel sample data, together with the calculated shortest path, shortest path reference distance, and power consumption, are combined to form an electric vehicle scheduling data cluster based on user travel demand.
[0007] More preferably, The transportation network i , j Reference distance between two nodes It shall be determined in the following manner:
[0008] in, In the transportation network i , j The straight-line distance between two nodes; For the transportation network from i Node to j The node corresponds to the road's gradeability coefficient, used to equivalently convert the road's elevation changes to the road length. Its value is determined by... i , j Relative height between two nodes Confirmed, when At that time, the gradient coefficient ;when At that time, the gradient coefficient Follow It increases with the increase of, and satisfies ;in, , , They are respectively i , j The elevation data of the nodes can be obtained through a GIS system.
[0009] More preferably, In S1, the air conditioning satisfaction index that takes into account the health status of the power grid operation is specifically as follows:
[0010] in, The air conditioner satisfaction index, and ; Skin temperature; Indoor temperature; Human metabolic rate; For the thermal resistance of clothing; These are correction values obtained based on the power grid status. When the power grid is in normal operating condition, When the power grid is in a peak state with a load rate of 95% or higher, or in a fault state, .
[0011] More preferably, The intelligent building temperature control model specifically includes: When the smart building receives a peak or fault signal from the power grid, the air conditioning load is regulated, and the set temperature is increased to reduce the air conditioning electrical load. As the temperature rises... Gradually rising, when When the temperature reaches 0.49, the air conditioner maintains the current temperature setting and no longer responds to further adjustment commands.
[0012] More preferably, In S2, the interaction method of the two-layer optimization model is as follows: The energy storage service fees paid by the lower-level smart building community to the shared flexible resource operator are part of the overall benefits of the upper-level shared flexible resource operator; The shared energy storage system configuration capacity and electric vehicle cluster scheduling capacity, obtained by the upper-level shared flexible resource operator through its own objective function optimization, serve as constraints on the lower-level multi-intelligent building community optimization model. This limits the energy storage usage capacity of the lower-level multi-intelligent building community to not exceed the shared energy storage system configuration capacity, and the electric vehicle scheduling scale to not exceed the electric vehicle cluster scheduling capacity.
[0013] More preferably, In S3, the multi-objective urban power grid optimization scheduling model has a multi-intelligent building community economic target of minimizing the energy consumption cost of the multi-intelligent building community; and a distribution network side flexibility target of minimizing the sum of the wind power curtailment rate, the light curtailment rate, and the power interaction rate of the multi-intelligent building community and the distribution network and the corresponding penalty coefficients.
[0014] In a second aspect, the application discloses a city power grid resilience improvement system considering multi-level resource coordination based on the foregoing method, which comprises a basic data and model construction module, a double-layer optimization model construction and solving module, and a multi-objective power grid scheduling scheme generation module, and has the characteristics that: The basic data and model construction module constructs a wind power photovoltaic proportional increment-decay scenario set of the distribution network side based on the wind power photovoltaic output prediction value of the distribution network side; constructs an electric vehicle scheduling data cluster based on user travel demand; proposes an air conditioner satisfaction index considering the operation health state of the power grid, and constructs an intelligent building temperature control model based on the air conditioner satisfaction index; The double-layer optimization model construction and solving module constructs a double-layer optimization model with the maximum comprehensive benefit of shared flexible resource operators as the upper layer and the minimum energy consumption cost of the multi-intelligent building community as the lower layer based on the electric vehicle scheduling data cluster and the intelligent building temperature control model; the shared flexible resource operators comprise shared energy storage power stations and electric vehicle charging stations; the double-layer optimization model is solved to obtain optimal shared energy storage system configuration capacity and optimal electric vehicle cluster scheduling capacity; The multi-objective power grid scheduling scheme generation module constructs a multi-objective urban power grid optimization scheduling model considering the wind power photovoltaic proportional increment-decay scenario of the distribution network side and taking into account the economic target of the multi-intelligent building community and the flexibility target of the distribution network side based on the optimal shared energy storage system configuration capacity and the optimal electric vehicle cluster scheduling capacity, and solves the multi-objective urban power grid optimization scheduling model to obtain an optimal city power grid operation scheme.
[0015] In a third aspect, the application provides a terminal comprising a processor and a storage medium. The storage medium is used to store instructions. The processor is used to operate according to the instructions to perform the steps of the method in any one of the first aspect of the application.
[0016] In a fourth aspect, the application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method in any one of the first aspect of the application.
[0017] Compared with the prior art, the application has the following beneficial effects: The application makes a more detailed description of the road network model of the electric vehicle, optimizes the correlation of the electric vehicle scheduling model and the terrain, and makes the electric vehicle scheduling model more practical; and innovatively adds an improved air conditioner satisfaction index considering the operation health state of the power grid, and takes into account the comfort and control response degree of the user in the air conditioner control process. The prior art usually schedules flexible resources such as electric vehicles and building air conditioners alone, and the application deeply binds the scheduling logic of user-side resources (electric vehicles) and building-side resources (air conditioners) with the state of the power grid, improves the utilization efficiency of multiple types of flexible resources, and widens the boundary of the adjustable resources of the power grid. The application proposes an improved photovoltaic output uncertainty risk cost considering meteorological factors, so that the transaction loss of the smart building caused by the fluctuation of the distributed photovoltaic output in the smart building is more finely described. BRIEF DESCRIPTION OF DRAWINGS
[0018] Figure 1 A flowchart of the urban power grid resilience improvement method considering multi-level resource collaborative optimization proposed by the application; Figure 2 A road network model schematic diagram of the Dijkstra shortest path algorithm considering terrain factors in example one; Figure 3 A comparison chart of the operation results of the air conditioner satisfaction index considering the operation health state of the power grid in example one. DETAILED DESCRIPTION
[0019] To make the purpose, technical scheme and advantages of the application clearer, the technical scheme of the application will be described clearly and completely below in combination with the drawings in the embodiments of the application. The embodiments described in the application are only a part of the embodiments of the application, not all the embodiments. All other embodiments obtained by those skilled in the art without creative labor based on the spirit of the application belong to the protection scope of the application.
[0020] As shown in Figure 1 The application discloses a kind of urban power grid resilience improvement method considering multi-level resource collaborative, the method includes the following steps: S1, based on the power distribution network side wind power photovoltaic output prediction value, the power distribution network side wind power photovoltaic equal proportion increment-attenuation scene set is constructed;Electric vehicle scheduling data cluster based on user travel demand is constructed;Improved air conditioner satisfaction index considering the operation health state of the power grid is proposed, and the intelligent building temperature control model is constructed based on the air conditioner satisfaction index; The electric vehicle scheduling data cluster based on user travel demand is constructed, specifically includes: The electric vehicle travel matrix considering terrain factors is constructed, the row and column of the travel matrix correspond to the traffic network node, and the element in the matrix representing a reference distance between two nodes in a traffic network i , j representing a reference distance between two nodes in a traffic network randomly generating electric vehicle trip sample data by Monte Carlo sampling, the electric vehicle trip sample data including a starting point and an ending point extracted from an electric vehicle trip matrix, a vehicle type, a departure state of charge, an electric vehicle charging and discharging power, a parking duration, and whether to access a power grid; based on the electric vehicle trip matrix, calculating a shortest path of an electric vehicle from a starting point to an ending point, a shortest path reference distance, and an electricity consumption of the electric vehicle from the starting point to the ending point by a shortest path reference distance, by using a shortest path algorithm; combining the electric vehicle trip sample data, the calculated shortest path, shortest path reference distance, and electricity consumption as an electric vehicle scheduling data set based on user trip demand.
[0021] representing a reference distance between two nodes in a traffic network i , j representing a reference distance between two nodes in a traffic network , determined in the following manner:
[0022] wherein, representing a reference distance between two nodes in a traffic network i , j representing a reference distance between two nodes in a traffic network representing a climbing coefficient of a road corresponding to a node pair in a traffic network from a starting node to an ending node, used to equivalently convert an altitude fluctuation of the road to a road length, the value of which is determined by i representing a reference distance between two nodes in a traffic network j ; when i representing a reference distance between two nodes in a traffic network j representing a reference distance between two nodes in a traffic network is determined, when , the climbing coefficient ; when , the climbing coefficient increases with the increase of , and satisfies ; wherein, representing a reference distance between two nodes in a traffic network , representing a reference distance between two nodes in a traffic network i , j representing a reference distance between two nodes in a traffic network
[0023] The air conditioner satisfaction index considering the operation health status of the power grid is specifically:
[0024] wherein, is an air conditioner satisfaction index, and ; skin temperature; room temperature; metabolic rate of human body; clothing thermal resistance; correction value obtained based on grid state, when the grid is in normal operation state, when the grid is in peak state with load rate of 95% or above, or in fault state, .
[0025] The intelligent building temperature control model specifically comprises: When the intelligent building receives a signal of peak state or fault state of the grid, the air conditioning load is added to the regulation and control, and the set temperature is raised to reduce the air conditioning electric load, and as the temperature rises, gradually rises, and when reaches 0.49, the air conditioner maintains the current temperature state and no longer responds to further adjustment instructions, such as Figure 3 shown.
[0026] S2, based on the electric vehicle scheduling data cluster and the intelligent building temperature control model, a double-layer optimization model is constructed, with maximizing the comprehensive benefits of shared flexible resource operators as the upper layer, and minimizing the energy consumption cost of the multi-intelligent building community as the lower layer; the shared flexible resource operators include shared energy storage power stations and electric vehicle charging stations; the double-layer optimization model is solved to obtain the optimal shared energy storage system configuration capacity and the optimal electric vehicle cluster scheduling capacity; The interaction mode of the double-layer optimization model is specifically: The energy storage service fee paid by the lower layer multi-intelligent building community to the shared flexible resource operator is a component of the comprehensive benefits of the upper layer shared flexible resource operator; The shared energy storage system configuration capacity and the electric vehicle cluster scheduling capacity obtained by the upper layer shared flexible resource operator through optimization of its own objective function are constraint conditions of the lower layer multi-intelligent building community optimization model, limiting the energy storage use capacity of the lower layer multi-intelligent building community to not exceed the shared energy storage system configuration capacity, and the electric vehicle scheduling scale to not exceed the electric vehicle cluster scheduling capacity.
[0027] S3, based on the optimal shared energy storage system configuration capacity and the optimal electric vehicle cluster scheduling capacity, a multi-objective urban power grid optimization scheduling model is constructed, considering the proportional increment-decay scenarios of wind power and photovoltaic power on the distribution grid side, and taking into account the economic target of the multi-intelligent building community and the flexibility target of the distribution grid side, and the multi-objective urban power grid optimization scheduling model is solved to obtain the optimal urban power grid operation scheme.
[0028] Specifically, the multi-objective urban power grid optimization scheduling model has a multi-intelligent building community economic target of minimizing the multi-intelligent building community energy consumption cost; and a distribution network side flexibility target of minimizing the sum of the wind power curtailment rate, the photovoltaic curtailment rate, and the power interaction rate of the multi-intelligent building community and the distribution network and the corresponding penalty coefficient.
[0029] The application discloses a kind of urban power grid elasticity promotion systems considering multi-level resource coordination based on the foregoing method, including basic data and model construction module, double-layer optimization model construction and solution module and multi-objective power grid scheduling scheme generation module; Basic data and model construction module, based on distribution network side wind power photovoltaic output prediction value, distribution network side wind power photovoltaic equal proportion increment-attenuation scene set is constructed;Electric vehicle scheduling data cluster based on user travel demand is constructed;Air conditioner satisfaction index considering grid operation health state is proposed, and intelligent building temperature control model is constructed based on the air conditioner satisfaction index; Double-layer optimization model construction and solution module, based on electric vehicle scheduling data cluster and intelligent building temperature control model, double-layer optimization model with maximum shared flexibility resource operator comprehensive benefit as upper layer, with minimum multi-intelligent building community energy consumption cost as lower layer is constructed;The shared flexibility resource operator includes shared energy storage power station and electric vehicle charging station;The double-layer optimization model is solved, and optimal shared energy storage system configuration capacity and optimal electric vehicle cluster scheduling capacity are obtained; Multi-objective power grid scheduling scheme generation module, based on optimal shared energy storage system configuration capacity and optimal electric vehicle cluster scheduling capacity, multi-objective urban power grid optimization scheduling model considering distribution network side wind power photovoltaic equal proportion increment-attenuation scene, and taking into account multi-intelligent building community economic target and distribution network side flexibility target is constructed, the multi-objective urban power grid optimization scheduling model is solved, and optimal urban power grid operation scheme is obtained.
[0030] Embodiment one: A kind of urban power grid elasticity promotion method considering multi-level resource coordination optimization, as shown in Figure 1 The specific construction steps are as follows: S1, based on distribution network side wind power photovoltaic output prediction value, distribution network side wind power photovoltaic equal proportion increment-attenuation scene set is constructed;Electric vehicle scheduling data cluster based on user travel demand is constructed;Air conditioner satisfaction index considering grid operation health state is proposed, and intelligent building temperature control model is constructed based on the air conditioner satisfaction index; S101: distribution network side wind power photovoltaic equal proportion increment-attenuation scene set is constructed, specifically including: For abnormal weather conditions between extreme weather and normal weather, typical daily normal wind power photovoltaic output prediction value is used as wind power photovoltaic risk boundary; Set an increment ratio coefficient greater than or equal to 1. Based on this coefficient and the aforementioned risk boundary, a proportionally proportional positive generation set of wind and solar power considering the risk boundary is generated; a decay ratio coefficient less than 1 is set. Based on the coefficient and the risk boundary, a wind power and solar power proportional negative set is generated considering the risk boundary. The positive set and the negative set together form the wind power and solar power set. Finally, the corresponding proportional coefficient is multiplied by the predicted output value of wind and solar power on the distribution network side to obtain the set of scenarios for proportional increase and decrease of wind and solar power.
[0031] S102: Construct an electric vehicle scheduling data cluster based on user travel demand, specifically including: Construct an electric vehicle trip matrix that considers terrain factors, where the rows and columns of the trip matrix correspond to traffic network nodes, and the elements in the matrix... Indicating the transportation network i , j Reference distance between two nodes; Specifically, a travel matrix is proposed that considers terrain factors and is based on a travel chain and road network model to construct a travel matrix representing the travel volume from all origins to destinations in the transportation network. The rows and columns of this travel matrix represent nodes in the transportation network. i , j If there is a direct path between two nodes, then the corresponding coordinates have values and represent the direct distance between the two points. Otherwise, no value exists, and the straight-line distance is corrected based on the terrain factors obtained from the GIS system to obtain the reference distance. : (1) in, In the transportation network i , j The straight-line distance between two nodes; For the transportation network from i Node to j The node corresponds to the road's gradeability coefficient, used to equivalently convert the road's elevation changes to the road length. Its value is determined by... i , j Relative height between two nodes Confirmed, when At that time, the gradient coefficient ;when At that time, the gradient coefficient Follow It increases with the increase of, and satisfies ;in, , , They are respectively i , jThe altitude data of the node can be obtained by a GIS system.
[0032] The electric vehicle trip matrix is obtained by the following steps: collecting the electric vehicle trip data of the target area, and establishing the electric vehicle trip matrix based on the collected electric vehicle trip data. Based on the electric vehicle trip matrix, the shortest path algorithm is used to calculate the shortest path and the shortest path distance of the electric vehicle from the starting point to the ending point, and the power consumption of the electric vehicle from the starting point to the ending point is calculated by the reference distance corresponding to the path. Specifically, based on the electric vehicle trip matrix, the Dijkstra shortest path algorithm is used to obtain the shortest path and the shortest path distance of the electric vehicle from the starting point to the ending point, as shown in Figure 2 The electric vehicle driving time and the power consumption during driving based on the trip chain and the road network model are obtained, and thus the power state and the arrival time of the electric vehicle after arriving at the intelligent building community are obtained. The terrain factor is also considered: (2) wherein, is the power state of the electric vehicle at the initial moment, is the power consumption per unit distance, unit , is the shortest path from the starting point to the ending point.
[0033] The electric vehicle trip sample data is combined with the calculated shortest path, shortest path distance and power consumption as an electric vehicle scheduling data set based on user trip demand.
[0034] S103, an air conditioner satisfaction index considering the health state of power grid operation is proposed, and an intelligent building temperature control model is constructed based on the air conditioner satisfaction index, specifically including Based on the heat radiation of the intelligent building, an indoor heat stability model of the intelligent building is constructed, and an improved air conditioner satisfaction index considering the health state of power grid operation is proposed : (3) wherein, is the air conditioner satisfaction index, and ; is the skin temperature; is the indoor temperature; is the metabolic rate of human body; is the clothing thermal resistance; For the correction value based on the grid state, when the grid is in normal operation state, When the grid is in peak state with load rate of 95% or above, or in fault state, .
[0035] Specifically, in the present embodiment, the skin temperature The conventional value in the field of human thermal comfort is adopted, and the typical value in the resting state is 33.5-34.5℃; the metabolic rate of the human body , and the typical value in the resting state is 1.0 met; the clothing thermal resistance is selected according to the ASHRAE 55 standard.
[0036] Subsequently, the improved air conditioner satisfaction index is used as a flexible constraint condition to adjust the adjustable range of the air conditioning load in real time, and the user comfort and dynamic response capability are taken into account to form an improved intelligent building temperature control model.
[0037] When the intelligent building receives a signal of the peak state or the fault state of the grid, the air conditioning load is added to the regulation and control, and the set temperature is increased to reduce the air conditioning electrical load, and as the temperature rises, gradually rises, and when the air conditioner maintains the current temperature state and no longer responds to further adjustment instructions when the air conditioner satisfaction index reaches 0.49.
[0038] Further, the air conditioner satisfaction index corresponding to the indoor temperature is calculated according to formula (3), and the electrical power at this time is obtained by querying the air conditioner factory parameters (such as the power-temperature characteristic curve). Further, the real-time electrical load value of the air conditioner participating in the grid regulation and control is determined.
[0039] S2, based on the electric vehicle scheduling data set and the intelligent building temperature control model, a double-layer optimization model is constructed, with the maximum shared flexible resource operator comprehensive benefit as the upper layer and the minimum multi-intelligent building community energy consumption cost as the lower layer; the shared flexible resource operator includes a shared energy storage power station and an electric vehicle charging station; the double-layer optimization model is solved to obtain the optimal shared energy storage system configuration capacity and the optimal electric vehicle cluster scheduling capacity; The intelligent building contains physical energy storage, distributed photovoltaic and central air conditioner, and the distribution network contains shared energy storage power station and electric vehicle charging station. The intelligent building, shared energy storage power station and electric vehicle charging station are used as flexible resources to construct a city grid system containing multiple flexible resources.
[0040] S201. Combine the flexible resources included in the smart building side, such as physical energy storage, distributed photovoltaics and central air conditioning, with the shared flexible resources operators on the distribution network side, including shared energy storage power stations and electric vehicle charging stations, to establish a two-layer optimization model with shared flexible resources operators as the main body and smart building communities as the subordinate. The upper layer uses the maximization of operator profits from shared flexibility resources as its objective function, specifically: (4) (5) (6) (7) (8) in, The revenue that shared flexibility resource operators obtain by supplying power to multiple smart building communities, aggregating electric vehicles to provide power to the lower-level communities, and charging electric vehicles is the energy storage fee paid by the lower-level model. To share the flexibility of resources and reduce the operating and maintenance costs of operators; The cost of charging and discharging energy storage devices and electric vehicles, namely the cost of energy loss during charging and discharging and the cost of electricity price differences. The investment cost of the energy storage device is the product of the investment cost per unit capacity of energy storage and the energy storage capacity. , , , For shared flexible resource operators to smart buildings t Real-time electricity sales price, aggregated electric vehicle electricity sales price to smart buildings, electric vehicle charging price, and grid time-of-use electricity price; , , , , , These are, respectively, the electricity sales power from shared flexible resource operators to smart buildings, the electricity sales power from aggregated electric vehicles to smart buildings, the charging power of electric vehicles, the total aggregated power of electric vehicles, the charging power of energy storage stations in shared flexible resource operators, and the discharging power of energy storage stations in shared flexible resource operators; Fixed costs for labor and equipment depreciation; , These are the unit compensation cost for electric vehicles participating in dispatch and the unit capacity charging and discharging cost of energy storage among operators of shared flexibility resources; , These are the charging efficiency and discharging efficiency of shared energy storage power stations operated by shared flexibility resource providers, respectively. ECapacity of the shared energy storage power station in the shared flexibility resource operator; Investment cost of the energy storage unit capacity in the shared flexibility resource operator; Optimization step; T Total number of time points of scheduling.
[0041] Wherein, the total power of the aggregated electric vehicles is the sum of the power sold to the smart building and the charging power of the aggregated electric vehicles.
[0042] The constraint condition of the upper optimization model is specifically: (9) (10) (11) (12) (13) Wherein, Capacity of the shared energy storage power station in the shared flexibility resource operator; , are respectively t、 t- State of charge of the shared energy storage power station at time 1; , are respectively the upper limit and the lower limit of the state of charge of the shared energy storage power station; , are respectively t, t- Average state of charge of the electric vehicle cluster at time 1; , are respectively the charging efficiency and the discharging efficiency of the electric vehicle cluster; Total scheduling capacity of the electric vehicle cluster; , are respectively the upper limit and the lower limit of the electricity selling price of the shared flexibility resource operator to the lower layer smart building; , are respectively the average state of charge of the electric vehicle cluster at the start time and the end time of the scheduling period; , are respectively the state of charge of the shared energy storage power station at the start time and the end time of the scheduling period.
[0043] The lower layer takes the minimum energy consumption cost of the smart building as the objective function, and the objective function is Specifically: (14) (15) (16) (17) (18) wherein, is the cost of buying and selling electricity of the multi-intelligent building community and the power distribution network, is the energy storage service fee paid by the multi-intelligent building community to the shared flexibility resource operator; is the intelligent building transaction loss caused by the fluctuation of the distributed photovoltaic output configured in the intelligent building; is the scheduling cost of the flexibility resource inside the intelligent building community, which is the cost generated by the central air conditioner and the physical energy storage inside the intelligent building participating in scheduling, obtained by multiplying the power of each resource participating in scheduling by the unit price specified in the scheduling agreement signed by the user; are the electricity prices of the intelligent building community buying and selling electricity from the power distribution network, respectively; are the buying and selling power of the intelligent building community interacting with the power distribution network, respectively; is the agreement compensation unit price of the air conditioner participating in scheduling; is the air conditioner power after scheduling; is the air conditioner reference power; is the scheduling unit price of the physical energy storage inside the intelligent building; is the charging and discharging power inside the intelligent building, is the intelligent building transaction loss caused by the fluctuation of the distributed photovoltaic output configured in the intelligent building at t moment.
[0044] Among them, the photovoltaic output has strong uncertainty, so that the intelligent building bears certain risk in the process of energy interaction in the purchase and sale of electricity, which is usually manifested as the intelligent building selling power greater than or less than the set upper and lower limit, and the purchased power greater than or less than the set upper and lower limit. The uncertainty of photovoltaic output is related to meteorological factors such as irradiance, air temperature, wind speed, etc. In sunny and windless weather, the uncertainty of photovoltaic output should be the lowest; in rainy and windy weather, the uncertainty of photovoltaic output should be the highest. If the uncertainty is the lowest, the photovoltaic encounters rare failure, and the photovoltaic causes the intelligent building to bear loss in the purchase and sale of electricity, at this time the risk penalty unit cost should be appropriately adjusted; on the contrary, the risk penalty unit cost should be appropriately adjusted. At this time, the fluctuation of the distributed photovoltaic output configured in the intelligent building at t moment causes the intelligent building transaction loss Specifically, (19) In the formula, is the meteorological correction coefficient, is the risk penalty unit cost, is the intelligent building int Sales power at any given moment For smart buildings t Purchase power at any time , For smart buildings t The upper and lower bounds of the power sold at any given time. , Smart buildings t The upper and lower bounds of the power purchase at any given time.
[0045] This is used to quantify the economic risks faced by smart building communities in the event of uncertainties arising from solar power generation surges or rare malfunctions.
[0046] Among them, meteorological correction coefficient Related to meteorological conditions, its value is determined based on real-time meteorological conditions. The power output should be dynamically adjusted within the specified range. When weather conditions are clear, with few clouds and stable sunlight, the photovoltaic output should also remain stable. The power purchased and sold in smart building transactions should not exceed the set upper and lower limits. Adjust upwards, with a range of values. When weather conditions are unstable, such as cloudy, overcast, or rainy, photovoltaic power output fluctuates drastically. This increases the likelihood that the power purchased or sold in smart building transactions will exceed the set upper and lower limits. From a fairness perspective, the limits should be reduced. Take values such that their range is In this embodiment, on a sunny day... Take 1.1, cloudy weather Take 1.05, on a cloudy day. Take 1.0, during precipitation Take 0.95, during extreme precipitation. Take 0.9.
[0047] The constraints of the lower-level optimization model are as follows: (20) (twenty one) (twenty two) (twenty three) (twenty four) (25) (26) in, The photovoltaic output power of the smart building itself; Power interaction between smart buildings and power distribution networks; The power of the internal energy storage of the intelligent building is discharged; The power of the internal energy storage of the intelligent building is charged; The power of the air conditioner in the intelligent building is discharged; The power of the remaining required electrical load in the intelligent building is discharged; , The minimum and maximum power of the air conditioner load that can be adjusted under the premise of ensuring user comfort are respectively , The minimum and maximum power of the air conditioner load that can be adjusted under the premise of ensuring user comfort are respectively t The state of charge of the internal energy storage of the intelligent building at the moment t The state of charge of the internal energy storage of the intelligent building at the moment -1; The capacity of the internal energy storage of the intelligent building; The upper limit of the charging and discharging power of the internal energy storage of the intelligent building; The maximum transmission power of the connection line between the intelligent building community and the power distribution network; The upper limit of the power supplied by the shared flexibility resource operator to the building.
[0048] S202, based on the double-layer optimization model, the Karush-Kuhn-Tucker condition is used to replace the lower-layer multi-intelligent building community energy consumption cost model as an additional constraint of the upper-layer model, and the original double-layer optimization problem is converted into a single-layer nonlinear optimization problem for solving; The optimal shared energy storage system configuration capacity and the optimal electric vehicle cluster scheduling capacity are obtained.
[0049] S3, based on the optimal shared energy storage system configuration capacity and the optimal electric vehicle cluster scheduling capacity, a multi-objective urban power grid optimization scheduling model considering the proportional increment-decay scenario of wind power and photovoltaic power on the power distribution network side, and taking into account the economic target of the multi-intelligent building community and the flexibility target of the power distribution network side is constructed, and the multi-objective urban power grid optimization scheduling model is solved to obtain the optimal urban power grid operation scheme.
[0050] Specifically, on the basis of the energy storage capacity configuration result optimized in S2, an elasticity index considering the wind power and photovoltaic power consumption rate in the urban power grid and the power interaction rate between the intelligent building and the power distribution network is proposed, which is added to the urban power grid operation model in the form of a penalty function to generate a power distribution network side flexibility target: (27) In the formula: is a penalty function based on the wind power and photovoltaic power consumption rate in the urban power grid and the power interaction rate between the intelligent building and the power distribution network, and are the wind power penalty coefficient and the light power penalty coefficient respectively, is the penalty coefficient of the power distribution network to the intelligent building, and are the wind power and photovoltaic output values in the urban power grid, and This represents the actual absorption value of wind and solar power in the urban power grid. This represents the power supplied from the distribution network to the smart building. It is positive when the distribution network supplies power to the smart building and negative otherwise. For the total load of smart buildings, This refers to the power interaction rate between the smart building and the power distribution network.
[0051] Economic goals of multi-intelligent building communities for: (28) in, for t The cost of purchasing and selling electricity for smart building communities and power distribution networks at all times; for t The energy storage service fees paid by MomentSmart Building Communities to operators of shared flexible resources; for t The scheduling costs of two flexible resources within a smart building community: central air conditioning and physical energy storage. for t The value of transaction losses in improved smart buildings caused by uncertainties in photovoltaics at any given time.
[0052] The overall objective function of the multi-objective urban power grid optimization scheduling model, which takes into account both the economic objectives of intelligent building communities and the flexibility objectives of the distribution network, is: (29) The constraints, as described above, include renewable energy absorption rate constraints, smart building air conditioning comfort index range constraints, electric vehicle dispatching constraints, and energy storage operation constraints. These constraints have been given above. In addition, the following constraints must also be followed: (30) (31) in, yes t It can share the electricity stored in the energy storage power station at all times; Configure the capacity of the optimal shared energy storage system obtained in step S2; This is the minimum amount of electricity that can be stored within a shared energy storage power station. for t The capacity of the electric vehicle cluster scheduling at any given time; The optimal electric vehicle cluster scheduling capacity obtained in step S2; This represents the minimum scheduling capacity for electric vehicle clusters.
[0053] The present disclosure can be a system, a method, and / or a computer program product. The computer program product can include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present disclosure.
[0054] The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium can be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium include the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or punched tape, a
[0055] The computer readable program instructions described herein can be downloaded to respective computing / processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and / or a wireless network. The network can comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and / or edge servers. A network adapter card or network interface in each computing / processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing / processing device.
[0056] Computer readable program instructions for carrying out operations of the present disclosure can be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, and conventional procedural programming languages such as the "C" programming language or similar programming languages. The computer readable program instructions can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate array (FPGA), or programmable logic array (PLA) can execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present disclosure.
[0057] Finally, it should be noted that the above-mentioned embodiments are merely used to illustrate the technical solutions of the present application, but not to limit it. Although the present application has been described in detail with reference to the above-mentioned embodiments, those skilled in the art should understand that the specific embodiments of the present application can be modified or replaced, and any modification or replacement without departing from the spirit and scope of the present application should be covered in the protection scope of the claims of the present application.
Claims
1. A method for enhancing the resilience of urban power grids considering multi-level resource coordination, characterized in that, The method includes the following steps: S1. Based on the predicted output of wind and solar power on the distribution network side, construct a set of proportional incremental-decrease scenarios for wind and solar power on the distribution network side; construct an electric vehicle dispatch data cluster based on user travel demand; propose an air conditioning satisfaction index that takes into account the health status of the power grid operation, and construct an intelligent building temperature control model based on the air conditioning satisfaction index. S2. Based on the electric vehicle scheduling data cluster and the intelligent building temperature control model, a two-layer optimization model is constructed, with maximizing the comprehensive benefits of shared flexible resource operators as the upper layer and minimizing the energy cost of multiple intelligent building communities as the lower layer; the shared flexible resource operators include shared energy storage power stations and electric vehicle charging stations; the two-layer optimization model is solved to obtain the optimal shared energy storage system configuration capacity and the optimal electric vehicle cluster scheduling capacity. S3. Based on the optimal shared energy storage system configuration capacity and the optimal electric vehicle cluster scheduling capacity, a multi-objective urban power grid optimization scheduling model is constructed, which considers the proportional incremental-decrease scenario of wind power and photovoltaic power on the distribution network side, and takes into account the economic objectives of multiple smart building communities and the elastic objectives of the distribution network side. The multi-objective urban power grid optimization scheduling model is solved to obtain the optimal urban power grid operation scheme.
2. The urban power grid resilience enhancement method considering multi-level resource coordination according to claim 1, characterized in that, In S1, the construction of an electric vehicle scheduling data cluster based on user travel demand specifically includes: Construct an electric vehicle trip matrix that considers terrain factors, where the rows and columns of the trip matrix correspond to traffic network nodes, and the elements in the matrix... Indicating the transportation network i , j Reference distance between two nodes; The sample data of electric vehicle trips is randomly generated using Monte Carlo sampling. The sample data of electric vehicle trips includes the starting point and destination, vehicle type, state of charge at departure, charging and discharging power of electric vehicle, parking time and whether it is connected to the power grid, which are extracted from the electric vehicle trip matrix. Based on the electric vehicle travel matrix, the shortest path algorithm is used to calculate the shortest path and shortest path reference distance of the electric vehicle from the starting point to the destination, and the power consumption of the electric vehicle from the starting point to the destination is calculated through the shortest path reference distance. The electric vehicle travel sample data, together with the calculated shortest path, shortest path reference distance, and power consumption, are combined to form an electric vehicle scheduling data cluster based on user travel demand.
3. The urban power grid resilience enhancement method considering multi-level resource coordination according to claim 2, characterized in that, The transportation network i , j Reference distance between two nodes It shall be determined in the following manner: in, In the transportation network i , j The straight-line distance between two nodes; For the transportation network from i Node to j The node corresponds to the road's gradeability coefficient, used to equivalently convert the road's elevation changes to the road length. Its value is determined by... i , j Relative height between two nodes Confirmed, when At that time, the gradient coefficient ;when At that time, the gradient coefficient Follow It increases with the increase of, and satisfies ;in, , , They are respectively i , j The elevation data of the nodes can be obtained through a GIS system.
4. The urban power grid resilience enhancement method considering multi-level resource coordination according to claim 1, characterized in that, In S1, the air conditioning satisfaction index that takes into account the health status of the power grid operation is specifically as follows: in, The air conditioner satisfaction index, and ; Skin temperature; Indoor temperature; Human metabolic rate; For the thermal resistance of clothing; These are correction values obtained based on the power grid status. When the power grid is in normal operating condition, When the power grid is in a peak state with a load rate of 95% or higher, or in a fault state, .
5. The urban power grid resilience enhancement method considering multi-level resource coordination according to claim 4, characterized in that, The intelligent building temperature control model specifically includes: When the smart building receives a peak or fault signal from the power grid, the air conditioning load is regulated, and the set temperature is increased to reduce the air conditioning electrical load. As the temperature rises... Gradually rising, when When the temperature reaches 0.49, the air conditioner maintains the current temperature setting and no longer responds to further adjustment commands.
6. The urban power grid resilience enhancement method considering multi-level resource coordination according to claim 5, characterized in that, In S2, the interaction method of the two-layer optimization model is as follows: The energy storage service fees paid by the lower-level smart building community to the shared flexible resource operator are part of the overall benefits of the upper-level shared flexible resource operator; The shared energy storage system configuration capacity and electric vehicle cluster scheduling capacity, obtained by the upper-level shared flexible resource operator through its own objective function optimization, serve as constraints on the lower-level multi-intelligent building community optimization model. This limits the energy storage usage capacity of the lower-level multi-intelligent building community to not exceed the shared energy storage system configuration capacity, and the electric vehicle scheduling scale to not exceed the electric vehicle cluster scheduling capacity.
7. The urban power grid resilience enhancement method considering multi-level resource coordination according to claim 6, characterized in that, In S3, the economic objective of the multi-intelligent building community in the multi-objective urban power grid optimization scheduling model is to minimize the energy cost of the multi-intelligent building community; the elastic objective on the distribution network side is to minimize the sum of the wind curtailment rate, solar curtailment rate, power interaction rate between the multi-intelligent building community and the distribution network, and their respective penalty coefficients.
8. A resilient urban power grid system considering multi-level resource coordination based on the method of any one of claims 1-7, comprising a basic data and model construction module, a two-level optimization model construction and solution module, and a multi-objective power grid dispatching scheme generation module, characterized in that: The basic data and model building module constructs a set of proportional incremental-decrease scenarios for wind and solar power output on the distribution network side based on the predicted output values of wind and solar power on the distribution network side; it also constructs an electric vehicle scheduling data cluster based on user travel demand; and proposes an air conditioning satisfaction index that takes into account the health status of the power grid operation, and constructs an intelligent building temperature control model based on the air conditioning satisfaction index. The two-layer optimization model construction and solution module, based on the electric vehicle scheduling data cluster and the intelligent building temperature control model, constructs a two-layer optimization model with maximizing the comprehensive benefits of shared flexible resource operators as the upper layer and minimizing the energy costs of multiple intelligent building communities as the lower layer; the shared flexible resource operators include shared energy storage power stations and electric vehicle charging stations; solving the two-layer optimization model yields the optimal shared energy storage system configuration capacity and the optimal electric vehicle cluster scheduling capacity; The multi-objective power grid dispatching scheme generation module constructs a multi-objective urban power grid optimization dispatching model based on the optimal shared energy storage system configuration capacity and the optimal electric vehicle cluster dispatching capacity. This model considers the proportional incremental-decrease scenario of wind and solar power on the distribution network side, and takes into account the economic objectives of multiple smart building communities and the elastic objectives of the distribution network side. The module then solves the multi-objective urban power grid optimization dispatching model to obtain the optimal urban power grid operation scheme.
9. A terminal, comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the steps of the method according to any one of claims 1-7.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the method according to any one of claims 1-7.