A multi-time scale source-grid-load-storage coordinated interaction scheduling method

Abstract

The application discloses a kind of multi-time scale source network load storage coordination interaction scheduling method, it is related to electric power system field.The method includes determining the daily charging load time sequence curve of electric vehicle;Establish distribution network carrying capacity evaluation system, determine the distribution network carrying capacity promotion optimization scheduling model of day-ahead time scale;According to daily charging load time sequence curve, with the maximum electric vehicle carrying capacity and the highest distribution network operation cost as optimization goal, solve distribution network carrying capacity promotion optimization scheduling model, obtain intra-day power flow calculation result;According to intra-day power flow calculation result, determine the time-of-use carbon price of distribution network;Establish the low-carbon optimization scheduling model of distribution network of intra-day time scale;According to time-of-use carbon price, with the lowest carbon emission cost and the lowest equipment adjustment cost in distribution network as optimization goal, solve distribution network low-carbon optimization scheduling model, obtain the multi-time scale optimal scheduling result of source network load storage.The application can be optimized in space-time dimension Source network load storage, realize low-carbon operation.

Description

Technical Field

[0001] This application relates to the field of power systems, and in particular to a scheduling method for coordinated interaction between power generation, grid, load and storage across multiple time scales. Background Technology

[0002] The rapid development of electric vehicles (EVs) both domestically and internationally has brought severe challenges to the safety, stability, and reliability of power distribution networks due to their large-scale integration. EV charging behavior is characterized by spatiotemporal randomness and high power concentration, easily creating a "peak-on-peak" effect in local areas and specific time periods. This can lead to problems such as exceeding line capacity limits and excessive node voltage deviations, seriously threatening the safe, stable, and reliable operation of the power distribution network. Furthermore, the low-carbon benefits of EVs are highly dependent on the source of their charging electricity. If charging activity is concentrated during periods of high carbon emission factors in the power grid, their total life-cycle carbon emissions may increase rather than decrease, contradicting the original intention of developing EVs and making it difficult to achieve true low-carbon transportation.

[0003] Existing research often assesses EV load capacity from a single dimension, lacking a comprehensive quantitative index system and scoring model for the distribution network's capacity to support EV loads. This makes it difficult for grid operators to accurately grasp the network's "capacity floor" and "optimization space," failing to provide clear and quantifiable target guidance for dispatch decisions. Furthermore, most dispatch models either aim for economic optimization, peak shaving, or treat carbon emission reduction as a simple constraint, lacking a framework that explicitly optimizes "improving distribution network capacity" as an optimization objective and coordinates it with "reducing carbon emissions." Summary of the Invention

[0004] The purpose of this application is to provide a scheduling method for coordinated interaction between multiple time scales of power generation, grid, load and storage, which can collaboratively optimize power generation, grid, load and storage in the spatiotemporal dimensions to achieve low-carbon operation.

[0005] To achieve the above objectives, this application provides the following solution: This application provides a scheduling method for coordinated interaction between source, grid, load, and storage across multiple time scales, including: Determine the daily charging load time-series curve for electric vehicles; Establish a power distribution network carrying capacity assessment system; Based on the aforementioned distribution network carrying capacity assessment system, a day-ahead time-scale distribution network carrying capacity enhancement and optimization scheduling model is determined. Based on the daily charging load time-series curve, with the optimization objectives of maximizing the electric vehicle carrying capacity and maximizing the distribution network operating cost, the distribution network carrying capacity enhancement optimization scheduling model is solved to obtain the intraday power flow calculation results. Based on the intraday power flow calculation results, the time-of-use carbon price of the distribution network is determined; Establish a low-carbon optimization scheduling model for the distribution network on an intraday time scale; Based on the time-of-use carbon price, with the optimization objectives of minimizing carbon emission costs and minimizing equipment adjustment costs in the distribution network, the low-carbon optimization scheduling model of the distribution network is solved to obtain the optimal scheduling results of multiple time scales of source, grid, load and storage.

[0006] According to the specific embodiments provided in this application, this application has the following technical effects: This application provides a multi-timescale source-grid-load-storage coordinated scheduling method. The established distribution network carrying capacity assessment system can effectively and comprehensively quantify the distribution network's carrying capacity for electric vehicle loads after electric vehicles are connected to the distribution network. On the day-ahead timescale, the goal is to maximize the distribution network's capacity for electric vehicle access by improving its carrying capacity and reducing its operating costs. On the intraday timescale, carbon costs are embedded into the real-time scheduling objectives to precisely guide the coordinated optimization of electric vehicles, flexible loads, and energy storage systems in the spatiotemporal dimensions, thereby achieving low-carbon operation. Attached Figure Description

[0007] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0008] Figure 1 A flowchart illustrating a multi-timescale source-grid-load-storage coordinated scheduling method provided in this application embodiment; Figure 2 This is a schematic diagram illustrating the principle of a multi-timescale source-grid-load-storage coordinated scheduling method provided in this application embodiment. Detailed Implementation

[0009] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0010] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0011] In one exemplary embodiment, such as Figure 1As shown, a multi-timescale source-grid-load-storage coordinated scheduling method is provided. This method is executed by computer equipment, specifically by a terminal or server alone, or by both a terminal and a server. Addressing the impact of electric vehicle charging loads on the distribution network, the multi-timescale source-grid-load-storage coordinated scheduling method provided in this application includes steps 101 to 107.

[0012] Step 101: Determine the daily charging load time series curve of the electric vehicle.

[0013] Step 102: Establish a power distribution network carrying capacity assessment system.

[0014] Step 103: Based on the aforementioned distribution network carrying capacity assessment system, determine the day-ahead time-scale distribution network carrying capacity enhancement and optimization scheduling model.

[0015] Step 104: Based on the daily charging load time-series curve, with the optimization objectives of maximizing the carrying capacity of electric vehicles and maximizing the operating cost of the distribution network, solve the distribution network carrying capacity enhancement optimization scheduling model to obtain the intraday power flow calculation results.

[0016] Step 105: Determine the time-of-use carbon price of the distribution network based on the intraday power flow calculation results.

[0017] Step 106: Establish a low-carbon optimization scheduling model for the distribution network on an intraday time scale.

[0018] Step 107: Based on the time-of-use carbon price, with the optimization objectives of minimizing carbon emission costs and minimizing equipment adjustment costs in the distribution network, solve the low-carbon optimization scheduling model of the distribution network to obtain the optimal scheduling results of source, grid, load and storage at multiple time scales.

[0019] Implementing steps 101 to 107 above, firstly, a distribution network carrying capacity score is proposed to establish a distribution network carrying capacity assessment system. This system effectively quantifies the distribution network's performance in terms of flexibility, reliability, security, and stability in relation to electric vehicle load after electric vehicles are connected to the network. Secondly, a multi-timescale scheduling model is proposed for the day-ahead to intraday period to improve the distribution network's carrying capacity and reduce carbon emissions. Within the day-ahead timescale, the objective function is to maximize the electric vehicle carrying capacity and optimize economic efficiency. Based on the intraday scheduling power flow results and carbon emission flow theory, the distribution network operator determines the time-of-use carbon price for each node in the distribution network to guide carbon emission reduction at the intraday timescale. At the intraday timescale, scheduling is performed with the objective function of reducing carbon emission costs and minimizing the adjustment amount of each device.

[0020] In another exemplary embodiment of this application, step 101 described above may be replaced by steps 201 to 203.

[0021] Step 201: Establish a charging load model for electric vehicles.

[0022] For example, the charging load model for electric vehicles includes: the probability density function of the time it takes for an electric vehicle to arrive at a charging station and the probability density function of the charging demand for an electric vehicle.

[0023] The probability density function for the arrival time of an electric vehicle at a charging station is: ; In the formula, Let be the probability density function of the time it takes for an electric vehicle to arrive at a charging station; The time it takes for an electric vehicle to arrive at a charging station. and Let be the standard deviation and expected value of the probability density function of the time it takes for an electric vehicle to arrive at a charging station, respectively.

[0024] The probability density function of electric vehicle charging demand is: ; In the formula, The probability density function of the demand for charging electric vehicles. This refers to the daily mileage of electric vehicles. and denoted as the standard deviation and expected value of the probability density function of electric vehicle charging demand, respectively.

[0025] Step 202: Use the Monte Carlo method to sample the charging load model of electric vehicles and obtain the daily charging load curve of a single electric vehicle.

[0026] Step 203: Overlay the daily charging load curves of multiple electric vehicles to obtain the daily charging load time-series curve of the electric vehicles.

[0027] The charging load timing curve can be denoted as: .

[0028] In another exemplary embodiment of this application, the load-bearing capacity score for the distribution network after the connection of electric vehicles as an impact load is considered mainly from four basic performance aspects of distribution network operation: flexibility, reliability, safety, and stability. This comprehensive evaluation of the distribution network's operation after the connection of new energy sources quantifies the load-bearing capacity of the distribution network for electric vehicle charging loads. Therefore, the indicators in the distribution network load-bearing capacity assessment system include: flexibility, reliability, safety, and stability.

[0029] (1) Flexibility is defined as the ability of a distribution network to quickly respond to uncertain changes in new energy sources and loads by adjusting the output of various equipment within a certain time scale. Sub-indicators of flexibility include: photovoltaic absorption rate and photovoltaic power generation ratio.

[0030] The formula for calculating the photovoltaic grid integration rate is: ; In the formula, For photovoltaic power absorption rate, For nodes exist The absorption capacity of photovoltaic units on a time-scale within a given day. For nodes exist The power output forecast of photovoltaic units is based on the recent short-term power forecast. The scheduling cycle is within the day. This refers to the set of nodes connected to the photovoltaic (PV) generator.

[0031] The formula for calculating the proportion of photovoltaic power generation is: ; In the formula, The proportion of photovoltaic power generation, for The amount of electricity purchased by the distribution network from the upper-level power grid during the day.

[0032] (2) Sub-indicators of reliability include: voltage qualification rate and voltage fluctuation rate.

[0033] The formula for calculating the voltage qualification rate is: ; In the formula, For voltage qualification rate, The total number of load nodes. This represents the number of nodes among all nodes that satisfy the voltage range of 0.95 pu-1.05 pu.

[0034] The formula for calculating voltage fluctuation rate is: ; In the formula, For voltage fluctuation rate, For nodes exist Voltage at time +1 For nodes exist Voltage at time, For nodes The rated voltage. It is set to 1 in per-unit value.

[0035] (3) Sub-indicators of safety include: line heavy load rate and line light load rate.

[0036] The formula for calculating the line load rate is: ; In the formula, For line overload rate, The total number of branch roads, A line whose transmission power is above 85% of its rated transmission capacity.

[0037] The formula for calculating the light load rate of a line is: ; In the formula, For light load rate of the line, For lines whose transmission power is below 25% of the line's rated transmission capacity.

[0038] (4) Sub-indicators of stability include: electric vehicle charging load ratio and user load satisfaction rate.

[0039] The formula for calculating the proportion of charging load for electric vehicles is: ; In the formula, The proportion of charging load for electric vehicles, For nodes exist The actual charging power of electric vehicles at the current time scale. For nodes exist Predicted charging power of electric vehicles at the day-to-day scale.

[0040] The formula for calculating the user load satisfaction rate is: ; In the formula, For user load satisfaction rate, For nodes exist The actual node load value satisfied at any given time. For nodes exist The load forecast value at any given time.

[0041] In another exemplary embodiment of this application, step 103 described above may be replaced by steps 301 to 305.

[0042] Step 301: Use the analytic hierarchy process (AHP) to determine the weights of each indicator in the power distribution network carrying capacity assessment system.

[0043] In the Analytic Hierarchy Process (AHP), a comparison matrix satisfying reciprocity is constructed. After calculating the largest eigenvalue and eigenvector, a consistency check is performed (if the consistency ratio is less than 0.1, it passes; otherwise, the matrix is ​​modified). Finally, the eigenvectors are normalized to obtain the subjective weight vector. .

[0044] Step 302: Determine the day-ahead time-scale distribution network carrying capacity enhancement optimization scheduling model, including the electric vehicle carrying capacity objective function, the distribution network operation cost objective function, and day-ahead constraints.

[0045] Considering the multiple stakeholders involved in power generation, grid, load, and storage, flexible resources from the power generation, grid, load, and storage sides are scheduled to participate in distribution network scheduling. With the objective functions of improving distribution network carrying capacity, optimizing economics, and maximizing photovoltaic absorption, and taking into account node voltage constraints, power flow constraints, and equipment operation constraints, the start-up and shutdown scheduling periods for electric vehicles, energy storage, and flexible loads are determined to guide intraday optimization scheduling.

[0046] Step 303: Based on the weights of each indicator in the power distribution network carrying capacity assessment system, establish the objective function for electric vehicle carrying capacity as follows: .

[0047] In the formula, For the load-bearing capacity of electric vehicles, For the first The weight of each indicator, For the first Standardized data for each indicator For the set of nodes that connect to electric vehicles, For nodes exist The actual charging power of electric vehicles at the current time scale. K This refers to the number of indicators.

[0048] Step 304: Establish the objective function for the operation cost of the distribution network as follows: In the formula, For the operating costs of the distribution network, Penalty costs for photovoltaic power grid integration For energy storage dispatch and operation costs, To reduce the operating costs of electric vehicle dispatching, To reduce the cost of flexible load scheduling, Constraints on electricity purchases for the distribution network.

[0049] For example, the penalty cost for photovoltaic power grid integration The expression is: ; In the formula, This represents the cost coefficient for photovoltaic curtailment penalties. For nodes exist The absorption capacity of photovoltaic units on a time-scale within a given day. For nodes exist The power output forecast of photovoltaic units is based on the recent short-term power forecast. The scheduling cycle is within the day. A set of nodes connected to photovoltaic (PV) generators; Energy storage dispatch and operation costs The expression is: ; In the formula, This is the dispatch cost coefficient for energy storage devices. , The charging and discharging power of the energy storage device, The number of energy storage devices; Electric vehicle dispatching and operation costs The expression is: ; In the formula, This represents the electric vehicle dispatch cost coefficient. For nodes exist Predicted charging power of electric vehicles at the day-ahead scale; Flexible load dispatching costs The expression is: ; In the formula, This is the dispatch cost coefficient for energy storage devices. For nodes exist The day-ahead forecast power of flexible loads at any given time. For nodes exist Actual daytime power after flexible load adjustment A set of nodes connected to an energy storage device; Power purchase constraints for distribution networks The expression is: ; In the formula, This is the dispatch cost coefficient for energy storage devices. for The distribution network purchases electricity from the main grid at all times.

[0050] Step 305: Construct day-ahead constraints including photovoltaic output constraints, energy storage operation constraints, electric vehicle dispatch constraints, flexible load power adjustment constraints, safe operation constraints, main grid power purchase constraints, and power flow constraints.

[0051] For example, photovoltaic output constraints: ; Energy storage operation constraints: ; ; ; ; In the formula: , These are the upper and lower limits of the charging power of the energy storage device, respectively. , These are the upper and lower limits of the charging power of the energy storage device, respectively. For energy storage State of charge at time, , For energy storage, the upper and lower limits of the state of charge computer, , For energy storage charging and discharging efficiency, , Represents a node Energy storage The charging and discharging state at any given moment.

[0052] Electric vehicle scheduling constraints: ; ; In the formula: , Indicates electric vehicles Time and The state of participating in distribution network dispatch at time -1 Electric vehicle scheduling ,otherwise, , This refers to the minimum continuous operating time of an electric vehicle.

[0053] Flexible load power adjustment constraints: ; ; In the formula: , This indicates the upper and lower limits of flexible load power adjustment on a day-ahead timescale. Represents a node exist The status of flexible loads participating in distribution network dispatch at all times. Flexible loads participating in distribution network dispatch at any time ,otherwise, ; This indicates the minimum continuous operating time for controllable load scheduling.

[0054] Safe operation constraints: ; In the formula: , These are the upper and lower limits of the current in branch ij, respectively; , They are nodes The upper and lower limits of voltage, , These represent the current and voltage of branch ij, respectively.

[0055] Main grid power purchase constraints: ; In the formula: This indicates the maximum electricity purchase capacity of the distribution network.

[0056] Current constraints: ; ; ; ; ; ; ; In the formula: , They represent time The active and reactive power consumed by the node; , Representing the nodes respectively The set of first and last nodes of connected branches; , , , express The currents of time; , Let represent the resistance and reactance of branch ij, respectively; , express The node voltage at time t; , Representing nodes respectively exist Active and reactive loads at all times; express Constantly purchase reactive power from the main network; , Representing nodes respectively exist The active and reactive power of photovoltaic and wind power generation at any given moment; , Represents a node exist The charging and discharging power of the stored energy at any given time; For nodes exist The normal load at any given time.

[0057] In another exemplary embodiment of this application, the process of solving the distribution network carrying capacity enhancement optimization scheduling model in step 104 above can be replaced by steps 401 to 403.

[0058] Step 401: Using the formula The electric vehicle carrying capacity objective function is normalized to obtain a normalized electric vehicle carrying capacity objective function, which, together with the distribution network operating cost objective function and the day-ahead constraints, constitutes the final distribution network carrying capacity improvement optimization scheduling model; where, The final objective function for the load-bearing capacity of electric vehicles is... For electric vehicle load capacity The weight, and For electric vehicle load capacity The maximum and minimum values; , For distribution network operating costs The maximum and minimum values.

[0059] because and Since the dimensions are different, the objective function needs to be normalized to form the final objective function.

[0060] Step 402: Obtain the data of each indicator in the power distribution network carrying capacity assessment system, and standardize and unify them to obtain unified data for each indicator.

[0061] (1) Standardization Since the order of magnitude and units of each indicator may differ, min-max standardization is used to standardize the matrix after the normalization process. .

[0062] (2) Standardization of indicators The evaluation indicators are standardized based on unified standards: indicators with larger values ​​and better results are designated as positive indicators, while those with smaller values ​​and better results are designated as negative indicators. The indicators are now standardized and unified. Among them, photovoltaic absorption rate, photovoltaic power generation ratio, voltage qualification rate, electric vehicle charging load ratio, and user load satisfaction rate are positive indicators, while voltage fluctuation rate, line heavy load rate, and line light load rate are negative indicators.

[0063] Positive indicators: .

[0064] Negative indicators: .

[0065] Step 403: Based on the standardized data of each indicator, with the maximum carrying capacity of electric vehicles and the highest operating cost of the distribution network as the optimization objectives, solve the final distribution network carrying capacity improvement optimization scheduling model to obtain the intraday power flow calculation results.

[0066] Standardized data for each indicator and input of indicator weights In the middle, the load-bearing capacity of electric vehicles can be calculated. .

[0067] In another exemplary embodiment of this application, in order to clarify the carbon emission responsibility of the distribution network load and guide the demand-side response to participate in system scheduling, the application establishes a correlation between unit carbon price and node carbon potential based on carbon emission flow theory, and distributes the results to each node of the distribution network according to the intraday power flow calculation. The time-of-use carbon price at any given moment. The formula for calculating the time-of-use carbon price of a distribution network is: ; ; In the formula, For nodes exist The node carbon potential at a given moment. For active power inflow nodes The set of branch paths, The active power of the branch circuit. The carbon flux density of the branch is... For nodes The connected power supply is active. Equivalent carbon intensity for power generation; The carbon price represents the carbon price per unit of carbon emissions. express The nodal carbon potential matrix at time t, for Elements in; express The carbon price at all nodes at any given time.

[0068] In another exemplary embodiment of this application, within the framework of intraday distribution network optimization scheduling, this application aims to mitigate the impact of intraday fluctuations in wind power and load by adjusting energy storage and flexible loads, while minimizing carbon emission costs.

[0069] The intraday timescale low-carbon optimization scheduling model for distribution networks includes: a low-carbon optimization objective function and intraday constraints.

[0070] The objective function for low-carbon optimization is: ; In the formula, To optimize total cost for low-carbon development, For carbon emission costs, Penalty costs for photovoltaic power grid integration Adjusting costs for energy storage Costs for adjusting controllable loads.

[0071] ; ; ; ; In the formula, for The carbon price of all nodes at any given time represents the node representing the electric vehicle charging station. exist Carbon price at any given moment intraday timescale nodes exist The amount of electricity charged by an electric vehicle at any given moment. for The carbon price of all nodes at time t represents the node. Flexible loads Carbon price at any given time point intraday timescale nodes exist The actual controllable load at any given time The scheduling period for electric vehicles is determined by the previous time scale. The scheduling period for controllable loads is determined by the day-ahead timescale. For the set of nodes that connect to electric vehicles, A set of nodes connected to an energy storage device; intraday timescale nodes Photovoltaic units in The ability to absorb information in a short period of time. For nodes based on intraday ultra-short-term power prediction Photovoltaic units in Forecast of intraday power output at any given time. The scheduling cycle is within the day. A set of nodes connected to photovoltaic (PV) generators; intraday timescale nodes Energy storage Charging power at any time intraday timescale nodes Energy storage Discharge power at any given time , The charging and discharging power of the energy storage device, The scheduling period for energy storage devices is determined by the day-ahead timescale. This is the dispatch cost coefficient for energy storage devices. The number of energy storage devices; This is the dispatch cost coefficient for energy storage devices. Intraday timescale Node of time Controllable load.

[0072] Intraday constraints include: electric vehicle power adjustment constraints, energy storage power adjustment constraints, flexible load power adjustment constraints, and power flow constraints.

[0073] (1) Electric vehicle power adjustment constraints: ; In the formula: , This represents the upper and lower limits of the electric vehicle adjustment amount at node j on an intraday timescale.

[0074] (2) Energy storage power adjustment constraints: ; ; In the formula: , This indicates the upper and lower limits of the energy storage discharge power adjustment on a daily timescale. , This indicates the upper and lower limits of the energy storage discharge power adjustment on a daily timescale.

[0075] (3) Flexible load power adjustment constraints: ; In the formula: , This indicates the upper and lower limits of flexible load power adjustment on an intraday timescale.

[0076] (4) Power flow constraints: Voltage, current, and branch power flow constraints are the same as those in the day-ahead timescale model. The node power balance constraints change as follows: .

[0077] In another exemplary embodiment of this application, the above model is solved by calling the Gurobi solver based on Matlab simulation software, and the schedules are output. Optimization variables at time , , , , The 24-hour optimization variables constitute the electric vehicle charging time series curve, photovoltaic power absorption curve, energy storage charging and discharging curve, and flexible load curve.

[0078] This application can also ultimately yield the power purchase timing curves, node voltages, and branch power flow of the distribution network.

[0079] Figure 2 This is a schematic diagram illustrating the principle of the method proposed in this application. The method of this application mainly consists of four steps: establishing a charging model for electric vehicles; establishing evaluation indicators for the carrying capacity of the distribution network; calculating the time-of-use carbon price of the distribution network; constructing a day-ahead dispatch model for the distribution network; constructing an intraday dispatch model for the distribution network; and solving the model.

[0080] This application addresses the challenges of stochastic load surges brought about by the large-scale integration of electric vehicles into the power distribution network, as well as the shortcomings of traditional control methods in terms of rigid time scales and insufficient multi-entity coordination. It proposes an innovative multi-time-scale source-grid-load-storage coordinated interactive scheduling method.

[0081] 1. This application establishes a four-dimensional quantitative assessment system for the carrying capacity of distribution networks, encompassing flexibility, reliability, security, and stability. This system comprehensively evaluates the distribution network's capacity to accommodate electric vehicles (EVs) and accurately quantifies the impact of EV access on the power grid. Unlike previous methods that only simply assess the carrying capacity of distribution networks, this application embeds the carrying capacity score into the optimization objective function to guide the dispatching decisions of the distribution network. This enables a refined assessment and improvement of the EV capacity, providing a clear and feasible path for enhancing the carrying capacity of distribution networks.

[0082] 2. A multi-timescale collaborative optimization framework is proposed, encompassing day-ahead and intraday timescales. At the day-ahead scale, the goal is to enhance the distribution network's carrying capacity and reduce economic costs, maximizing the network's ability to connect to electric vehicles. At the intraday scale, a node-level time-sharing carbon pricing mechanism is designed based on carbon emission flow theory, embedding carbon costs into real-time scheduling objectives. This precisely guides the collaborative optimization of electric vehicles, flexible loads, and energy storage systems across time and space, achieving low-carbon operation.

[0083] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0084] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A scheduling method for coordinated interaction between source, grid, load, and storage across multiple time scales, characterized in that, include: Determine the daily charging load time-series curve for electric vehicles; Establish a power distribution network carrying capacity assessment system; Based on the aforementioned distribution network carrying capacity assessment system, a day-ahead time-scale distribution network carrying capacity enhancement and optimization scheduling model is determined. Based on the daily charging load time-series curve, with the optimization objectives of maximizing the electric vehicle carrying capacity and maximizing the distribution network operating cost, the distribution network carrying capacity enhancement optimization scheduling model is solved to obtain the intraday power flow calculation results. Based on the intraday power flow calculation results, the time-of-use carbon price of the distribution network is determined; Establish a low-carbon optimization scheduling model for the distribution network on an intraday time scale; Based on the time-of-use carbon price, with the optimization objectives of minimizing carbon emission costs and minimizing equipment adjustment costs in the distribution network, the low-carbon optimization scheduling model of the distribution network is solved to obtain the optimal scheduling results of multiple time scales of source, grid, load and storage.

2. The scheduling method for multi-timescale source-grid-load-storage coordinated interaction according to claim 1, characterized in that, Determine the daily charging load time-series curve for electric vehicles, specifically including: Establish a charging load model for electric vehicles; The Monte Carlo method was used to sample the charging load model of electric vehicles to obtain the daily charging load curve of a single electric vehicle. By overlaying the daily charging load curves of multiple electric vehicles, the time-series curve of the daily charging load of electric vehicles is obtained.

3. The scheduling method for multi-timescale source-grid-load-storage coordinated interaction according to claim 2, characterized in that, The charging load model for electric vehicles includes: the probability density function of the time it takes for an electric vehicle to arrive at a charging station and the probability density function of the charging demand for electric vehicles. The probability density function for the arrival time of an electric vehicle at a charging station is: ； In the formula, Let be the probability density function of the time it takes for an electric vehicle to arrive at a charging station; The time it takes for an electric vehicle to arrive at a charging station. and These are the standard deviation and expected value of the probability density function of the time it takes for an electric vehicle to arrive at a charging station, respectively. The probability density function of electric vehicle charging demand is: ； In the formula, The probability density function of the demand for charging electric vehicles. This refers to the daily mileage of electric vehicles. and denoted as the standard deviation and expected value of the probability density function of electric vehicle charging demand, respectively.

4. The scheduling method for multi-timescale source-grid-load-storage coordination and interaction according to claim 1, characterized in that, The indicators in the power distribution network carrying capacity assessment system include: flexibility, reliability, security, and stability; The sub-indicators of flexibility include: photovoltaic grid integration rate and photovoltaic power generation ratio; Sub-indicators of reliability include: voltage qualification rate and voltage fluctuation rate; Sub-indicators of safety include: line heavy load rate and line light load rate; Sub-indicators of stability include: electric vehicle charging load ratio and user load satisfaction rate.

5. The scheduling method for multi-timescale source-grid-load-storage coordinated interaction according to claim 4, characterized in that, The formula for calculating the photovoltaic grid integration rate is: ； In the formula, For photovoltaic power absorption rate, For nodes exist The absorption capacity of photovoltaic units on a time-scale within a given day. For nodes exist The power output forecast of photovoltaic units is based on the recent short-term power forecast. The scheduling cycle is within the day. A set of nodes connected to photovoltaic (PV) generators; The formula for calculating the proportion of photovoltaic power generation is: ； In the formula, The proportion of photovoltaic power generation, for The amount of electricity purchased by the distribution network from the superior power grid within a given day; The formula for calculating the voltage qualification rate is: ； In the formula, For voltage qualification rate, The total number of load nodes. The number of nodes among all nodes that satisfy the voltage range of 0.95 pu-1.05 pu; The formula for calculating voltage fluctuation rate is: ； In the formula, For voltage fluctuation rate, For nodes exist Voltage at time +1 For nodes exist Voltage at time, For nodes The rated voltage; The formula for calculating the line load rate is: ； In the formula, For line overload rate, The total number of branch roads, For lines whose transmission power is above 85% of the line's rated transmission capacity; The formula for calculating the light load rate of a line is: ； In the formula, For light load rate of the line, For lines whose transmission power is below 25% of the line's rated transmission capacity; The formula for calculating the proportion of charging load for electric vehicles is: ； In the formula, The proportion of charging load for electric vehicles, For nodes exist The actual charging power of electric vehicles at the current time scale. For nodes exist Predicted charging power of electric vehicles at the day-ahead scale; The formula for calculating the user load satisfaction rate is: ； In the formula, For user load satisfaction rate, For nodes exist The actual node load value satisfied at any given time. For nodes exist The load forecast value at any given time.

6. The scheduling method for multi-timescale source-grid-load-storage coordinated interaction according to claim 1, characterized in that, Based on the aforementioned distribution network carrying capacity assessment system, a day-ahead time-scale distribution network carrying capacity enhancement and optimization scheduling model is determined, specifically including: The weights of each indicator in the power distribution network carrying capacity assessment system are determined using the analytic hierarchy process (AHP). The optimized scheduling model for improving the carrying capacity of the distribution network at the day-ahead time scale includes the objective function of electric vehicle carrying capacity, the objective function of distribution network operating cost, and day-ahead constraints. Based on the weights of each indicator in the aforementioned power distribution network carrying capacity assessment system, the objective function for electric vehicle carrying capacity is established as follows: In the formula, For the load-bearing capacity of electric vehicles, For the first The weight of each indicator, For the first Standardized data for each indicator For the set of nodes that connect to electric vehicles, For nodes exist The actual charging power of electric vehicles at the current time scale. K For the number of indicators; The objective function for the operating cost of the power distribution network is established as follows: In the formula, For the operating costs of the distribution network, Penalty costs for photovoltaic power grid integration For energy storage dispatch and operation costs, To reduce the operating costs of electric vehicle dispatching, To reduce the cost of flexible load scheduling, Constraints on power purchases for the distribution network; The day-ahead constraints include photovoltaic output constraints, energy storage operation constraints, electric vehicle dispatch constraints, flexible load power adjustment constraints, safe operation constraints, main grid power purchase constraints, and power flow constraints.

7. The scheduling method for multi-timescale source-grid-load-storage coordinated interaction according to claim 6, characterized in that, Photovoltaic grid integration penalty costs The expression is: ； In the formula, This represents the cost coefficient for photovoltaic curtailment penalties. For nodes exist The absorption capacity of photovoltaic units on a time-scale within a given day. For nodes exist The power output forecast of photovoltaic units is based on the recent short-term power forecast. The scheduling cycle is within the day. A set of nodes connected to photovoltaic (PV) generators; Energy storage dispatch and operation costs The expression is: ； In the formula, This is the dispatch cost coefficient for energy storage devices. , The charging and discharging power of the energy storage device, The number of energy storage devices; Electric vehicle dispatching and operation costs The expression is: ； In the formula, This represents the electric vehicle dispatch cost coefficient. For nodes exist Predicted charging power of electric vehicles at the day-ahead scale; Flexible load dispatching costs The expression is: ； In the formula, This is the dispatch cost coefficient for energy storage devices. For nodes exist The day-ahead forecast power of flexible loads at any given time. For nodes exist Actual daytime power after flexible load adjustment A set of nodes connected to an energy storage device; Power purchase constraints for distribution networks The expression is: ； In the formula, This is the dispatch cost coefficient for energy storage devices. for The distribution network purchases electricity from the main grid at all times.

8. The scheduling method for multi-timescale source-grid-load-storage coordinated interaction according to claim 6, characterized in that, Based on the daily charging load time-series curve, and with the optimization objectives of maximizing electric vehicle carrying capacity and maximizing distribution network operating costs, the distribution network carrying capacity enhancement optimization scheduling model is solved to obtain intraday power flow calculation results, specifically including: Using formula The electric vehicle carrying capacity objective function is normalized to obtain a normalized electric vehicle carrying capacity objective function, which, together with the distribution network operating cost objective function and the day-ahead constraints, constitutes the final distribution network carrying capacity improvement optimization scheduling model; where, The final objective function for the load-bearing capacity of electric vehicles is... For electric vehicle load capacity The weight, and For electric vehicle load capacity The maximum and minimum values; , For distribution network operating costs The maximum and minimum values; Data for each indicator in the power distribution network carrying capacity assessment system are obtained, and then standardized and unified to obtain unified data for each indicator. Based on the standardized data of each indicator, with the optimization objectives of maximizing the carrying capacity of electric vehicles and maximizing the operating cost of the distribution network, the final distribution network carrying capacity enhancement optimization scheduling model is solved to obtain the intraday power flow calculation results.

9. The scheduling method for multi-timescale source-grid-load-storage coordinated interaction according to claim 1, characterized in that, The formula for calculating the time-of-use carbon price of a power distribution network is: ； ； In the formula, For nodes exist The node carbon potential at a given moment. For active power inflow nodes The set of branch paths, The active power of the branch circuit. The carbon flux density of the branch is... For nodes The connected power supply is active. Equivalent carbon intensity for power generation; The carbon price represents the carbon price per unit of carbon emissions. express The nodal carbon potential matrix at time t, for Elements in; express The carbon price at all nodes at any given time.

10. The scheduling method for multi-timescale source-grid-load-storage coordinated interaction according to claim 1, characterized in that, The intraday timescale low-carbon optimization scheduling model for distribution networks includes: low-carbon optimization objective function and intraday constraints. Intraday constraints include: electric vehicle power adjustment constraints, energy storage power adjustment constraints, flexible load power adjustment constraints, and power flow constraints. The objective function for low-carbon optimization is: ； In the formula, To optimize total cost for low-carbon development, For carbon emission costs, Penalty costs for photovoltaic power grid integration Adjusting costs for energy storage Cost adjustment for controllable load; ； ； ； ； In the formula, for The carbon price of all nodes at any given time represents the node representing the electric vehicle charging station. exist Carbon price at any given moment intraday timescale nodes exist The amount of electricity charged by an electric vehicle at any given moment. for The carbon price of all nodes at time t represents the node. Flexible loads Carbon price at any given time point intraday timescale nodes exist The actual controllable load at any given time The scheduling period for electric vehicles is determined by the previous time scale. The scheduling period for controllable loads is determined by the day-ahead timescale. For the set of nodes that connect to electric vehicles, A set of nodes connected to an energy storage device; intraday timescale nodes Photovoltaic units in The ability to absorb information in a short period of time. For nodes based on intraday ultra-short-term power prediction Photovoltaic units in Forecast of intraday power output at any given time. The scheduling cycle is within the day. A set of nodes connected to photovoltaic (PV) generators; intraday timescale nodes Energy storage Charging power at any time intraday timescale nodes Energy storage Discharge power at any given time , The charging and discharging power of the energy storage device, The scheduling period for energy storage devices is determined by the day-ahead timescale. This is the dispatch cost coefficient for energy storage devices. The number of energy storage devices; This is the dispatch cost coefficient for energy storage devices. Intraday timescale Node of time Controllable load.

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