A method, device, equipment and program product for optimizing scheduling of a road-power coupling system

Abstract

The application discloses a kind of road-electricity coupling system optimization scheduling method, device, equipment and program product.The method can be determined by according to traffic trip path decision model, traffic space-time distribution rule, fast charging demand space-time distribution rule and basic charging path time consumption data, and then constructs the day-to-day real-time path planning model considering new energy prediction error, to determine the target charging planning result of electric vehicle, road network real-time vehicle distribution result and new energy output scheduling information of grid side, finally based on road-electricity coupling collaborative optimization model, with distribution network operation cost and traffic network travel time cost as joint optimization target, with target charging planning result, road network real-time vehicle distribution result and new energy output scheduling information as constraint condition, determine road-electricity system scheduling result, to solve the impact problem of electric vehicle charging and driving on power grid and traffic network in the prior art, improve new energy utilization and road network traffic efficiency.

Description

Technical Field

[0001] This invention relates to the field of road-electric coupling system management technology, and in particular to a method, apparatus, equipment and program product for optimizing the scheduling of road-electric coupling systems. Background Technology

[0002] With the rapid development of the new energy vehicle industry, the number and penetration rate of electric vehicles (EVs) continue to increase. Their charging and driving behaviors pose significant challenges to the coordinated operation of urban transportation networks and power distribution networks, becoming one of the key bottlenecks restricting the high-quality development of the new energy industry.

[0003] On the transportation network side, the concentrated travel and fast charging demand of a large number of electric vehicles can easily lead to local congestion on the road network, especially the convergence of traffic around fast charging stations. This not only increases vehicle travel time but also exacerbates traffic pressure. At the same time, the charging time preferences and fast charging station selection preferences of electric vehicle users have significant spatiotemporal randomness. Traditional traffic route planning methods do not fully consider the charging route time (including travel time, fast charging queuing time, and fast charging duration), resulting in a disconnect between planning results and actual travel demand, making it difficult to improve road network traffic efficiency.

[0004] On the distribution network side, the fast charging behavior of electric vehicles is characterized by high power and concentrated time. The random access of large-scale fast charging loads can easily cause safety problems such as voltage fluctuations and branch power overloads in the distribution network. In addition, the power generation of new energy sources such as photovoltaic and wind power has strong volatility and uncertainty. Their prediction errors further increase the difficulty of grid dispatch. Traditional grid dispatch methods have not fully coordinated the traffic flow distribution and charging demand of the transportation network, resulting in low new energy absorption rate and high distribution network operating costs.

[0005] In existing technologies, optimization scheduling schemes for road-power systems often suffer from the drawback of being "independent": on the one hand, traffic route planning technology often only focuses on the efficiency of road network traffic, ignoring the impact of electric vehicle charging demand on grid operation, and also failing to consider the constraints of new energy output fluctuations on charging planning; on the other hand, grid dispatching technology mostly focuses on load balancing within the power system, without fully combining the spatiotemporal distribution patterns of traffic flow in the transportation network, making it difficult to accurately match the dynamic charging needs of electric vehicles. Summary of the Invention

[0006] This invention provides a method, apparatus, equipment, and program product for optimizing the scheduling of a road-electric coupling system, in order to solve the problem of the impact of electric vehicle charging and driving on the power grid and transportation network in the prior art, and improve the utilization rate of new energy and the efficiency of road network traffic.

[0007] According to a first aspect of the present invention, a method for optimizing scheduling of a road-electric coupling system is provided, comprising: Based on the constructed traffic flow travel path decision model, we analyze the fast charging behavior data of electric vehicles to determine the spatiotemporal distribution pattern of traffic flow, the spatiotemporal distribution pattern of fast charging demand, and the basic charging path time data. Based on the aforementioned spatiotemporal distribution patterns of traffic flow, spatiotemporal distribution patterns of fast charging demand, and basic charging path time data, an intraday-real-time path planning model considering new energy prediction errors is constructed. Based on the intraday-real-time path planning model and the set dual-time-scale scheduling strategy, the target charging planning result for electric vehicles is determined, as well as the real-time vehicle allocation result of the road network and the new energy output scheduling information on the power grid side are determined. The constructed road-electricity coupling collaborative optimization model is obtained, and the operating cost of the distribution network and the travel time cost of the transportation network are used as joint optimization objectives. The target charging planning results, real-time vehicle allocation results of the road network, and new energy power output scheduling information are used as constraints to determine the road-electricity system scheduling results matched by the road-electricity coupling collaborative optimization model. The road-electricity system scheduling results are used to improve the utilization rate of new energy in the system and the traffic efficiency of the road network, and to alleviate the impact of electric vehicle charging and driving on the power grid and transportation network.

[0008] According to a second aspect of the present invention, a circuit-electric coupling system optimization scheduling device is provided, comprising: The first determining module is used to analyze the fast charging behavior data of electric vehicles based on the constructed traffic flow travel path decision model, and determine the spatiotemporal distribution pattern of traffic flow, the spatiotemporal distribution pattern of fast charging demand, and the basic charging path time data. The construction module is used to build an intraday-real-time path planning model that takes into account the prediction error of new energy vehicles, based on the spatiotemporal distribution pattern of traffic flow, the spatiotemporal distribution pattern of fast charging demand and the basic charging path time data. The second determining module is used to determine the target charging planning result of electric vehicles based on the intraday-real-time path planning model and the set dual-time-scale scheduling strategy, and to determine the real-time vehicle allocation result of the road network and the new energy power output scheduling information of the power grid side. The optimization scheduling module is used to obtain the constructed road-electricity coupling collaborative optimization model, and to determine the road-electricity system scheduling result matched by the road-electricity coupling collaborative optimization model, with the distribution network operating cost and transportation network travel time cost as joint optimization objectives, and with the target charging planning results, real-time vehicle allocation results of the road network and new energy power output scheduling information as constraints. The road-electricity system scheduling result is used to improve the system's new energy utilization rate and road network traffic efficiency, and to alleviate the impact of electric vehicle charging and driving on the power grid and transportation network.

[0009] According to a third aspect of the present invention, an electronic device is provided, the electronic device comprising: At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to execute the circuit-electric coupling system optimization scheduling method according to any embodiment of the present invention.

[0010] According to a fourth aspect of the present invention, a computer program product is provided, the computer program product comprising a computer program that, when executed by a processor, implements the circuit-electric coupling system optimization scheduling method according to any embodiment of the present invention.

[0011] The technical solution of this invention can analyze the fast charging behavior data of electric vehicles based on a constructed traffic flow travel path decision model to determine the spatiotemporal distribution patterns of traffic flow, the spatiotemporal distribution patterns of fast charging demand, and the basic charging path time data. Then, based on the aforementioned spatiotemporal distribution patterns of traffic flow, fast charging demand, and basic charging path time data, a daily-real-time path planning model considering new energy prediction errors is constructed. Next, based on the daily-real-time path planning model and the established dual-time-scale scheduling strategy, the target charging planning result for electric vehicles is determined, along with the real-time vehicle allocation result of the road network and the new energy output scheduling information on the power grid side. The system obtains the constructed road-electricity coupling collaborative optimization model, and uses the operating cost of the distribution network and the travel time cost of the transportation network as joint optimization objectives, and the target charging planning results, real-time vehicle allocation results of the road network, and new energy power output scheduling information as constraints, to determine the road-electricity system scheduling results matched by the road-electricity coupling collaborative optimization model. The road-electricity system scheduling results are used to improve the utilization rate of new energy and the traffic efficiency of the road network, and to alleviate the impact of electric vehicle charging and driving on the power grid and transportation network, so as to solve the problem of the impact of electric vehicle charging and driving on the power grid and transportation network in the existing technology, and improve the utilization rate of new energy and the traffic efficiency of the road network.

[0012] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1This is a flowchart of an optimized scheduling method for a circuit-electric coupling system provided in Embodiment 1 of the present invention; Figure 2 This is a flowchart of an optimized scheduling method for a road-electric coupling system provided in Embodiment 2 of the present invention; Figure 3 This is a schematic diagram of the structure of an optimized scheduling device for a road-electric coupling system according to Embodiment 3 of the present invention; Figure 4 This is a schematic diagram of the structure of an electronic device that implements the circuit-electric coupling system optimization scheduling method of the present invention. Detailed Implementation

[0015] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0016] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0017] Example 1 Figure 1 This is a flowchart of a method for optimizing the scheduling of a road-electric coupling system according to Embodiment 1 of the present invention. This embodiment is applicable to the situation of optimizing the scheduling of a road-electric coupling system. The method can be executed by a road-electric coupling system optimization scheduling device, which can be implemented in hardware and / or software and can be configured in the power system dispatch center and the traffic management center. Figure 1 As shown, the method includes: S101. Based on the constructed traffic flow travel path decision model, analyze the fast charging behavior data of electric vehicles to determine the spatiotemporal distribution pattern of traffic flow, the spatiotemporal distribution pattern of fast charging demand, and the basic charging path time data.

[0018] Among them, the traffic flow travel path decision model can be a mathematical model used to describe the path selection behavior of various types of vehicles (such as fuel vehicles and electric vehicles with fast charging needs) in the road network. For example, the path selection behavior of fuel vehicles is only related to the road congestion status, while the path selection of electric vehicles is affected by the combined influence of road congestion status and the charging price of fast charging stations.

[0019] Fast charging behavior data for electric vehicles can be data generated by electric vehicles with fast charging needs during actual operation, including but not limited to charging start time, charging location (fast charging station), charging amount, and charging period.

[0020] The spatiotemporal distribution pattern of traffic flow can be defined as the statistical characteristics and changing patterns of the number of all vehicles (including fuel vehicles and electric vehicles) passing through different areas of the road network at different times of the day. The spatiotemporal distribution pattern of fast charging demand can be defined as the intensity and distribution characteristics of the demand for fast charging of electric vehicles at different times of the day and in different geographical locations (fast charging stations).

[0021] Basic charging route time data can be the estimated total time that an electric vehicle will take to complete a trip including fast charging from its origin to its destination. It can typically include driving time, possible queuing time, and charging time.

[0022] For example, based on a pre-established traffic flow travel path decision model that reflects road network dynamics and user choice behavior, the collected electric vehicle fast charging behavior data can be processed and analyzed to identify and extract the overall distribution patterns of traffic flow (including all vehicles) on the road network in terms of time and space, as well as the aggregation patterns of electric vehicle fast charging demand at specific stations and time periods, thereby determining the spatiotemporal distribution patterns of traffic flow and fast charging demand. Furthermore, by combining information such as road network capacity and charging station service capacity, the basic time consumption data set required for electric vehicles to execute typical travel paths including fast charging can be estimated, obtaining basic charging path time data.

[0023] Optionally, the analysis of electric vehicle fast-charging behavior data to determine the spatiotemporal distribution patterns of traffic flow, the spatiotemporal distribution patterns of fast-charging demand, and the basic charging path time data may include: Acquire fast charging behavior data of electric vehicles, and determine the number and distribution characteristics of electric vehicles in different time periods and regions based on the traffic flow travel path decision model, thereby forming a spatiotemporal distribution pattern of traffic flow; Based on the fast charging station location, fast charging time and fast charging power information extracted from the fast charging behavior data, the demand intensity and demand distribution characteristics of each fast charging station in different time periods are determined, forming a spatiotemporal distribution pattern of fast charging demand. Based on the spatiotemporal distribution patterns of traffic flow and fast charging demand, the travel time of electric vehicles from their origin to their destination, the queuing time at fast charging stations, and the fast charging duration are determined, and the basic charging path time data are obtained by summarizing these data.

[0024] For example, firstly, based on traffic flow travel path decision models, the number of electric vehicles passing through different time periods and road areas, and their changing trends, can be simulated or statistically analyzed to form the spatiotemporal distribution pattern of traffic flow. Secondly, key fields can be extracted from fast charging behavior data, including the geographical coordinates of fast charging stations, the start time and duration of each charge, and the charging amount. Then, the demand intensity (such as expected charging load) of each fast charging station during peak / off-peak / valley periods and its spatial distribution characteristics in the road network can be analyzed to form the spatiotemporal distribution pattern of fast charging demand. Finally, combining the above two patterns, the travel time of electric vehicles from their origin to their destination, the queuing time at fast charging stations, and the fast charging duration can be determined, and the basic charging path time data can be obtained by summarizing these data.

[0025] S102. Based on the spatiotemporal distribution patterns of traffic flow, the spatiotemporal distribution patterns of fast charging demand, and the basic charging path time data, construct an intraday-real-time path planning model that considers the prediction error of new energy vehicles.

[0026] Among them, the intraday-real-time route planning model can be used to plan the optimal travel route for electric vehicles that includes charging stations, and includes two decision levels: intraday (long period) and real-time (short period).

[0027] New energy forecasting error can be the deviation between the predicted and actual values ​​of power generation output from new energy sources such as wind and solar power, and it is an important factor affecting power grid dispatch.

[0028] For example, the established spatiotemporal distribution patterns of traffic flow, fast-charging demand, and basic charging route time data can be transformed into constraints for the model, establishing an electric vehicle route planning model that simultaneously considers intraday pre-planning and real-time dynamic adjustment across two time scales. Furthermore, this electric vehicle route planning model can incorporate the impact of new energy vehicle (NEV) prediction errors, considering not only the predicted NEV output but also its uncertainty range, thereby making the generated route planning scheme robust to NEV fluctuations.

[0029] Optionally, the step of constructing a daytime-real-time path planning model that considers the prediction error of new energy vehicles based on the spatiotemporal distribution patterns of traffic flow, the spatiotemporal distribution patterns of fast charging demand, and the basic charging path time data may include: The spatiotemporal distribution pattern of traffic flow is determined as the basic constraint. Combined with the preset time scale, the basic traffic capacity boundary of each road segment of the road network at different time periods is determined, and the road network traffic distribution constraint conditions are constructed. Based on the spatiotemporal distribution pattern of fast charging demand and combined with the road network traffic allocation constraints, a service capacity constraint model for fast charging stations is constructed. The service capacity constraint model includes constraints on the number of fast charging piles, the maximum charging power of a single pile, and the charging queuing time threshold. The basic charging path time data is decomposed into road segment travel time, fast charging queuing time, and fast charging time. Based on a preset time scale, an objective function containing the total charging path time is constructed. The objective function is used to minimize the total travel time of the electric vehicle from the origin to the destination. By integrating the time scale, the road network traffic allocation constraints, the fast charging station service capacity constraint model, and the total time objective function, the intraday-real-time path planning model considering new energy prediction errors is determined.

[0030] Among them, the traffic flow allocation constraint can be a mathematical expression that restricts the traffic flow carried by each road segment in the road network within a specific time period from exceeding the design capacity.

[0031] The service capacity constraint model can be a model that describes the physical limitations of fast charging stations, including the total number of charging piles, the maximum output power of each pile, and the upper limit of queuing time tolerance set to avoid excessively long wait times.

[0032] The objective function, which includes the total time spent on the charging route, can be used to determine the route that minimizes the total time spent by electric vehicle users from the starting point to the destination (including the charging process).

[0033] For example, firstly, the spatial and temporal distribution patterns of traffic flow can be used as a basis, combined with a preset scheduling time scale (such as a 2-hour intraday scale or a 30-minute real-time scale), to determine the maximum traffic flow that each road segment of the road network can accommodate in each time period, thereby constructing road network traffic allocation constraints.

[0034] Secondly, based on the spatiotemporal distribution pattern of fast charging demand and combined with the above-mentioned road network constraints, a service capacity constraint model for each fast charging station is constructed. This model may include: constraints on the number of fast charging piles, constraints on the maximum charging power of a single pile, and constraints on the charging queuing time threshold.

[0035] Next, the basic charging route time data is broken down into three parts: road segment travel time, charging queuing time, and charging operation time. Based on the intraday and real-time time scales, an objective function is established to minimize the total time (i.e., total travel time) required for an electric vehicle user to travel from the origin to the charging station, complete charging, and then travel to the destination after choosing a route that includes a specific charging station.

[0036] Finally, the dual-timescale framework, road network traffic allocation constraints, fast charging station service capacity constraint model, and total time objective function are integrated to form an intraday-real-time path planning model that can reflect the impact of uncertainties in new energy prediction.

[0037] S103. Based on the intraday-real-time path planning model and the set dual-time-scale scheduling strategy, determine the target charging planning result for electric vehicles, and determine the real-time vehicle allocation result of the road network and the new energy power output scheduling information of the power grid side.

[0038] Among them, the dual time scale scheduling strategy can be a strategy that divides the optimization scheduling process into two continuous stages (intraday stage and real-time stage) and formulates corresponding operating rules. In the intraday stage, a pre-scheduling plan can be formulated, and in the real-time stage, further refined dynamic corrections can be made.

[0039] The target charging plan result can be the final recommended charging time, charging location, and charging amount scheme for electric vehicles after optimization on two time scales. The real-time vehicle allocation result of the road network can refer to the distribution of all vehicles (including fuel vehicles and electric vehicles) on various road segments in real time, obtained after optimization. The renewable energy output dispatch information on the grid side can be the output plan or adjustment instructions of renewable energy power generation equipment such as wind and solar power.

[0040] For example, this step can apply a pre-defined dual-timescale scheduling strategy to drive the aforementioned intraday-real-time path planning model for solution and decision-making. In the intraday phase, based on existing prediction data and the model, a preliminary electric vehicle charging plan and corresponding road network traffic flow allocation preview are generated. Subsequently, in the real-time phase, the preliminary plan for the intraday phase is dynamically evaluated and corrected based on the latest system status information (such as more accurate renewable energy output prediction and real-time road conditions). By comparing the differences between prediction and reality, and according to strategy rules (the impact of charging path time on the effectiveness of electricity price signals), the final target charging plan result is determined. Simultaneously, based on the final target charging plan result, the matching real-time road network vehicle allocation result is derived in reverse, and renewable energy output scheduling information on the grid side is generated to guide the operation of renewable energy power generation equipment, thereby achieving a rebalancing of power generation and consumption.

[0041] Optionally, the step of determining the target charging planning result for electric vehicles and the real-time vehicle allocation result of the road network and the new energy output scheduling information on the grid side based on the intraday-real-time path planning model and the set dual-time-scale scheduling strategy may include: Based on the established dual-time-scale scheduling strategy, the intraday pre-scheduling phase and the real-time dynamic correction phase are determined. During the intraday pre-scheduling phase, the spatiotemporal distribution patterns of traffic flow, the spatiotemporal distribution patterns of fast charging demand, and the basic charging path time data are input into the intraday-real-time path planning model. Combined with the predicted output data of new energy vehicles, the intraday pre-charging planning results of electric vehicles, the intraday pre-vehicle allocation results of the road network, and the intraday pre-output scheduling plan of new energy vehicles on the grid side are determined. During the real-time dynamic correction phase, real-time traffic flow data of the road network, real-time operation data of fast charging stations, and real-time power output data of new energy are collected, and the predicted power output data of new energy are compared with the predicted power output data of new energy to determine the prediction error of new energy. Based on the new energy prediction error and the real-time traffic flow data of the road network, the constraints of the intraday-real-time route planning model are dynamically corrected to obtain the corrected real-time charging planning result, which is then used as the target charging planning result. Based on the revised real-time charging planning results, the intraday pre-vehicle allocation results of the road network are adjusted to obtain the real-time vehicle allocation results of the road network, and the intraday pre-output scheduling plan of the new energy power grid is adjusted to obtain the new energy power output scheduling information of the power grid.

[0042] The intraday pre-scheduling phase can be the first stage of the scheduling process, where preliminary plans can be formulated based on longer-term forecast information such as day-ahead load forecasts and renewable energy output forecasts. The real-time dynamic correction phase can be the second stage of the scheduling process, where intraday plans can be calibrated and adjusted based on short-term forecasts and real-time measurement data.

[0043] For example, firstly, a dual-timescale scheduling strategy can be used to clearly define the intraday pre-scheduling phase (e.g., 2 hours in advance) and the real-time dynamic correction phase (e.g., every 30 minutes). In the intraday pre-scheduling phase, the obtained spatiotemporal distribution patterns of traffic flow, spatiotemporal distribution patterns of fast charging demand, and basic charging path time data, along with the intraday forecast data of new energy power output, can be input into the intraday-real-time path planning model for solving, to obtain the intraday pre-charging planning results (i.e., preliminary charging station selection and charging time suggestions), the intraday pre-vehicle allocation results of the road network, and the intraday pre-output scheduling plan of new energy power on the grid side.

[0044] During the real-time dynamic correction phase, real-time traffic flow data of the road network, real-time operation data of fast charging stations (such as the occupancy status of charging piles), and real-time output data of new energy vehicles can be collected and compared with the intraday forecast data to calculate the forecast error of new energy vehicles.

[0045] It should be noted that, based on the prediction error of new energy sources and real-time traffic conditions, the constraints of the route planning model can be dynamically modified. For example, the travel time of road segments can be updated according to real-time traffic conditions, and the charging price signal applicable to different charging route times can be dynamically adjusted according to the latest grid balance status and prediction error (such as using real-time electricity price for charging stations that can be reached in a short time, and locking the daily electricity price for charging stations that require a long driving time).

[0046] Finally, the corrected model can be used to solve the problem again, obtaining the corrected real-time charging planning result, which can then be used as the final target charging planning result. Based on this target charging planning result, vehicle routes can be adjusted accordingly to obtain the real-time vehicle allocation result of the road network. This result can then be fed back to the power grid side to adjust the renewable energy output plan and generate the final renewable energy output scheduling information.

[0047] S104. Obtain the constructed road-electricity coupling collaborative optimization model, and take the distribution network operation cost and traffic network travel time cost as joint optimization objectives, and take the target charging planning results, real-time vehicle allocation results of the road network and new energy power output scheduling information as constraints, determine the road-electricity system scheduling results matched by the road-electricity coupling collaborative optimization model. The road-electricity system scheduling results are used to improve the system's new energy utilization rate and road network traffic efficiency, and alleviate the impact of electric vehicle charging and driving on the power grid and traffic network.

[0048] Among them, the power-circuit coupling optimization model can be a unified mathematical model that integrates the power distribution system operation optimization model and the traffic system flow allocation model, and its decision variables involve both power grid dispatching and traffic guidance.

[0049] Distribution network operating costs refer to the economic expenditures incurred by the distribution system to meet the load demands of the entire region. These costs primarily include the cost of purchasing electricity from the upstream power grid, network loss costs, and equipment maintenance costs. Transportation network travel time costs refer to the time costs incurred by all vehicles traveling within the road network; this is an important indicator for measuring the efficiency of road network operations.

[0050] The results of the power grid system scheduling can be obtained by solving the power grid coupling and co-optimization model, which is a set of decision instructions that can simultaneously guide power grid operation (such as new energy scheduling) and traffic management (such as route guidance).

[0051] For example, this step can use minimizing the operating cost of the distribution network and minimizing the travel time cost of the transportation network as joint optimization objectives. The obtained target charging planning results, real-time vehicle allocation results of the road network, and renewable energy output scheduling information from the grid side are introduced as known quantities or boundary conditions into the constraints of this road-electricity coupled collaborative optimization model. Then, a suitable optimization algorithm is used to solve the model, and the optimal solution obtained can be the road-electricity system scheduling result. This road-electricity system scheduling result can include grid-side scheduling commands (such as power instructions from each power source output point and electricity purchase point) and traffic-side guidance suggestions (such as recommended routes issued to vehicle owners).

[0052] The technical solution of this invention can analyze the fast charging behavior data of electric vehicles based on a constructed traffic flow travel path decision model to determine the spatiotemporal distribution patterns of traffic flow, the spatiotemporal distribution patterns of fast charging demand, and the basic charging path time data. Then, based on the aforementioned spatiotemporal distribution patterns of traffic flow, fast charging demand, and basic charging path time data, a daily-real-time path planning model considering new energy prediction errors is constructed. Next, based on the daily-real-time path planning model and the established dual-time-scale scheduling strategy, the target charging planning result for electric vehicles is determined, along with the real-time vehicle allocation result of the road network and the new energy output scheduling information on the power grid side. The system obtains the constructed road-electricity coupling collaborative optimization model, and uses the operating cost of the distribution network and the travel time cost of the transportation network as joint optimization objectives, and the target charging planning results, real-time vehicle allocation results of the road network, and new energy power output scheduling information as constraints, to determine the road-electricity system scheduling results matched by the road-electricity coupling collaborative optimization model. The road-electricity system scheduling results are used to improve the utilization rate of new energy and the traffic efficiency of the road network, and to alleviate the impact of electric vehicle charging and driving on the power grid and transportation network, so as to solve the problem of the impact of electric vehicle charging and driving on the power grid and transportation network in the existing technology, and improve the utilization rate of new energy and the traffic efficiency of the road network.

[0053] Based on the above embodiments, the present invention also provides an optional embodiment, which can further illustrate the construction process of the traffic flow travel path decision model in the above embodiments. The construction steps of the traffic flow travel path decision model may include: Obtain the network topology diagram of the road network system, and determine the basic road network parameter information based on the network topology diagram; Based on the basic road network parameter information, the first type of traffic flow and the second type of traffic flow in the road network system are distinguished, and a first travel cost function for the first type of traffic flow and a second travel cost function for the second type of traffic flow are constructed. Based on the first travel cost function and the second travel cost function, determine the variational inequality constraints corresponding to the road network equilibrium state. By introducing a semi-dynamic traffic flow model, the coupling relationship between traffic surplus and multi-time period traffic flow is established, and the vehicle travel demand in the basic road network parameter information is corrected. Based on the revised traffic flow travel demand and the aforementioned variational inequality constraints, road network operation constraints are formed, and these constraints are used as the basis for the constructed traffic flow travel path decision model.

[0054] The network topology diagram can be a graph model composed of nodes (intersections, charging stations, etc.) and edges (road segments), which can be used to represent the physical structure of the road network. Basic road network parameter information can include data describing the static characteristics of the road network, such as road segment length, design speed, number of lanes, traffic capacity, and free-flow time.

[0055] The first type of traffic flow refers to vehicles whose travel routes are only affected by road conditions, such as gasoline-powered vehicles. The second type of traffic flow refers to vehicles whose travel routes are affected by both road conditions and charging prices, such as electric vehicles that require fast charging.

[0056] The first travel cost function can be the sum of the travel time costs of all road segments along a given route when the first type of traffic flow chooses one. The second travel cost function can be the sum of the travel time costs of all road segments along a given route for the second type of traffic flow, plus the charging cost at the selected charging station.

[0057] Variational inequality constraints can be used to describe the conditions under which a transportation network reaches a user equilibrium state, meaning that no user can reduce their travel costs by unilaterally changing their path.

[0058] The constraints on road network operation can be the set of all mathematical relationships that the traffic flow allocation must satisfy in the traffic flow travel path decision model, which may include traffic flow conservation, road segment capacity limits, and user equilibrium conditions.

[0059] For example, first, obtain the network topology diagram of the road network system, which can typically be represented as GJ=[NJ, AJ], where NJ can be a set of nodes and AJ can be a set of directed road segments. Based on this topology diagram, basic road network parameter information for each road segment can be extracted or set.

[0060] Secondly, based on basic road network parameter information, the first type of traffic flow and the second type of traffic flow are distinguished according to whether travelers are affected by charging prices.

[0061] Next, we construct the first travel cost function for the first type of traffic flow and the second travel cost function for the second type of traffic flow. The first travel cost function can be expressed as the sum of the travel times of each segment on the path, specifically as follows: ; in, w can represent the travel cost of the first type of traffic flow; w can represent the unit travel time cost of the traffic flow. This can represent the travel time of the first type of traffic flow on the road segment during time period t; This can represent the path-segment correlation coefficient of the first type of traffic flow; It can represent the set of road segments that represent the feasible paths of the first type of traffic flow.

[0062] The second travel cost function can add the charging fee of the selected charging station to the travel time cost, specifically: ; in, w can represent the travel cost of the second type of traffic flow; w can represent the unit travel time cost of the traffic flow. This can represent the travel time of the second type of traffic flow on the road segment during time period t; This can represent the path-segment correlation coefficient of the second type of traffic flow; The set of road segments that can represent feasible paths for the second type of traffic flow; This can represent the charging cost of the second type of vehicle flow.

[0063] Then, based on the two cost functions mentioned above, the variational inequality constraints corresponding to the road network equilibrium state can be determined. For example, travelers tend to choose the route with the shortest travel time, but as traffic volume increases, road sections will become congested. This congestion effect will affect travelers' route selection and charging decisions. When the travel time of all types of traffic cannot be reduced by unilaterally adjusting their own travel paths, the road network reaches an equilibrium state.

[0064] Subsequently, a semi-dynamic traffic flow model is introduced to establish the coupling relationship between traffic surplus and multi-time period traffic flow, thereby correcting the vehicle travel demand in the basic road network parameter information. Specifically, considering that the travel time of some vehicles may exceed the time scale of the road network's static traffic allocation, vehicles that have not completed their journeys will affect the traffic conditions in the next time period. Therefore, a semi-dynamic traffic flow model can be used to establish the coupling relationship between traffic surplus and multi-time period traffic flow.

[0065] Finally, based on the revised traffic flow travel demand and the aforementioned variational inequality constraints, road network operation constraints are formed, and these constraints are used as the constructed traffic flow travel path decision model.

[0066] Example 2 Figure 2 This is a flowchart of an optimized scheduling method for a road-electric coupling system provided in Embodiment 2 of the present invention. This embodiment can further optimize step S104 of the above embodiment based on the above embodiment. For example... Figure 2 As shown, the method includes: S201. Based on the constructed traffic flow travel path decision model, analyze the fast charging behavior data of electric vehicles to determine the spatiotemporal distribution pattern of traffic flow, the spatiotemporal distribution pattern of fast charging demand, and the basic charging path time data.

[0067] S202. Based on the spatiotemporal distribution patterns of traffic flow, the spatiotemporal distribution patterns of fast charging demand, and the basic charging path time data, construct an intraday-real-time path planning model that considers the prediction error of new energy vehicles.

[0068] S203. Based on the intraday-real-time path planning model and the set dual-time-scale scheduling strategy, determine the target charging planning result for electric vehicles, and determine the real-time vehicle allocation result of the road network and the new energy power output scheduling information of the power grid side.

[0069] S204. The joint optimization objective is characterized by the operating cost of the distribution network and the travel time cost of the transportation network, forming a comprehensive quantitative expression for the joint optimization objective.

[0070] Among them, the comprehensive quantitative expression of the joint optimization objective refers to integrating the operating cost of the distribution network and the travel time cost of the transportation network into a single scalar expression or explicit optimization framework that can be numerically calculated and compared through mathematical methods.

[0071] For example, the objective function of the road-power coupling collaborative optimization model is the same in both the intraday scheduling phase and the real-time scheduling phase. Both aim to optimize the operating cost of the distribution network and the travel time cost of the transportation network. To avoid repetition, we can discuss them together as follows: ; in, ; ; , , These can be represented as the total cost of the road-electricity coupling network, the operating cost of the distribution network, and the travel time cost of the transportation network, respectively. It can represent the total traffic flow on road segment a; This can represent the travel time for road segment a; It can be calculated based on the cost per unit of purchased electricity; The power purchased can be the power of the distribution network; T can represent the total time period set; A J It can represent a set of road network segments.

[0072] S205. Using the target charging planning results, real-time vehicle allocation results of the road network, and new energy power output scheduling information, construct quantifiable constraints.

[0073] For example, this step can effectively embed the target charging planning results, real-time vehicle allocation results of the road network, and renewable energy power output scheduling information into the collaborative optimization model. For instance, the total charging demand of a certain fast charging station in the target charging planning results for a specific time period can be transformed into a lower limit constraint on the total charging power of that charging station during that time period; the traffic flow of a certain road segment in the real-time vehicle allocation results of the road network can be transformed into a reference or upper and lower bound for the traffic flow variable of that road segment; and the renewable energy power output scheduling information can be transformed into a planned output constraint for renewable energy power generation units in the corresponding time period.

[0074] S206. Input the associated joint optimization objective function and quantifiable constraints into the road-electric coupling collaborative optimization model, and obtain the road-electric system scheduling results through model solving. The road-electric system scheduling results include at least the grid-side charging power scheduling instructions, the new energy output adjustment instructions, and the traffic network-side traffic flow path guidance instructions.

[0075] Among them, charging power scheduling commands can be control commands for the total charging power or power curve of each fast charging station in the distribution network within a specific time period. New energy output adjustment commands can be commands to adjust the actual power generation of new energy power generation equipment such as wind farms and photovoltaic power stations to match system optimization needs. Traffic flow path guidance commands can be used to issue real-time route suggestions to drivers to guide traffic flow according to optimized plans.

[0076] For example, the joint optimization objective function and quantifiable constraints can be input into the road-electricity coupled collaborative optimization model. Through the model solution process, the optimal values ​​of all decision variables are calculated. These optimal values ​​are then parsed and converted into charging power scheduling commands for the grid side, which can be used to control the spatiotemporal distribution of fast-charging loads; new energy output adjustment commands for the power supply side, which can maximize or balance system power absorption; and traffic flow path guidance commands for the transportation network side, which can be used to achieve the optimized road network traffic allocation.

[0077] The technical solution of this embodiment can further characterize the joint optimization objective by using the operating cost of the distribution network and the travel time cost of the transportation network, thus forming a comprehensive quantitative expression for the joint optimization objective. Then, using the target charging planning results, the real-time vehicle allocation results of the road network, and the new energy power output scheduling information, quantifiable constraints are constructed. The associated joint optimization objective function and quantifiable constraints are input into the road-electric coupling collaborative optimization model. The road-electric system scheduling results are obtained by solving the model. The road-electric system scheduling results include at least the grid-side charging power scheduling command, the new energy power output adjustment command, and the transportation network-side traffic flow path guidance command. The control commands that drive the coordinated operation of the power grid and the transportation network can be further output, thereby ensuring the executability of the entire optimized scheduling scheme.

[0078] Based on the above embodiments, the present invention also provides an optional embodiment, which can further illustrate the construction process of the circuit-electric coupling cooperative optimization model in the above embodiments. The construction steps of the circuit-electric coupling cooperative optimization model may include: A joint optimization objective function is constructed, which aims to minimize the operating cost of the distribution network and the travel time cost of the transportation network. The operating cost of the distribution network includes the cost of new energy consumption, the cost of grid loss, the operation and maintenance cost of fast charging stations and the cost of peak-valley electricity price difference. The travel time cost of the transportation network includes the overall travel time cost of the road network and the queuing time cost of fast charging stations. Obtain the grid-side constraints corresponding to the joint optimization objective function. The grid-side constraints include at least the distribution network node voltage constraints, branch power transmission constraints, new energy output upper limit constraints, fast charging station charging power constraints, and distribution network safe operation constraints. Obtain the traffic network-side constraints corresponding to the joint optimization objective function. The traffic network-side constraints include at least the traffic capacity constraints of each road segment, the traffic flow path flow conservation constraints, the fast charging station service capacity constraints, and the electric vehicle travel time threshold constraints. Based on the joint optimization objective function, grid-side constraints, and transportation network-side constraints, decision variables are determined, including electric vehicle charging power scheduling variables, road network traffic flow allocation variables, new energy power output scheduling variables, and fast charging station operation scheduling variables. The target charging planning results, real-time vehicle allocation results of the road network, and new energy power output scheduling information are used as boundary constraints to form a road-electricity coupling collaborative optimization model that includes the boundary constraints, the objective function, and the decision variables.

[0079] For example, firstly, a joint optimization objective function can be constructed to minimize the total system cost. The distribution network operating cost mainly includes: the cost of renewable energy consumption (or energy curtailment penalty) set to promote renewable energy absorption; the grid loss cost characterizing power transmission efficiency; the operation and maintenance cost of fast charging stations; and the peak-valley price difference cost affected by time-of-use pricing. The transportation network travel time cost mainly includes: the total travel time cost of traffic flow on all road segments of the network, and the time cost of vehicles queuing at fast charging stations.

[0080] Secondly, grid-side constraints can be obtained. These constraints include at least: node voltage constraints that require each node in the distribution network to maintain its voltage within a safe range; branch power transmission constraints that require each line (branch) to transmit power that does not exceed its thermal stability limit; upper limit constraints that require the actual output of new energy units to not exceed their predicted maximum output; fast charging station charging power constraints that require the total charging power of each fast charging station to not exceed the total capacity of its transformer and charging piles; and distribution network safety operation constraints that ensure the balance of active and reactive power in the system.

[0081] Next, the traffic network-side constraints can be obtained. These constraints include at least: the road segment capacity constraint that the actual traffic flow of each road segment must not exceed its maximum capacity; the traffic flow path conservation constraint that all traffic flow from each origin to the destination must be allocated to each path; the fast charging station service capacity constraint that the number of vehicles served by the fast charging station is limited by the number of its charging piles; and the electric vehicle travel time threshold constraint that the total time of a single trip (including driving, queuing, and charging) does not exceed the maximum acceptable travel time for the user.

[0082] Then, the decision variables can be determined. Based on the above objective function and constraints, the core variables that need to be optimized in the model are identified, mainly including: electric vehicle charging power scheduling variables for each fast charging station in each time period, road network traffic flow allocation variables on each path in the road network, new energy power output scheduling variables for each new energy unit in each time period, and adjustable fast charging station operation scheduling variables (such as charging service fees or incentive electricity prices).

[0083] Finally, the target charging planning results, real-time vehicle allocation results of the road network, and new energy power output scheduling information are used as boundary constraints to form a road-electricity coupling collaborative optimization model that includes boundary constraints, objective function, and decision variables.

[0084] Example 3 Figure 3 This is a schematic diagram of the structure of an optimized scheduling device for a road-electric coupling system provided in Embodiment 3 of the present invention. Figure 3 As shown, the device includes: The first determining module 31 can be used to analyze the fast charging behavior data of electric vehicles based on the constructed traffic flow travel path decision model, and determine the spatiotemporal distribution pattern of traffic flow, the spatiotemporal distribution pattern of fast charging demand, and the basic charging path time data. Module 32 can be used to construct an intraday-real-time path planning model that takes into account the prediction error of new energy vehicles, based on the spatiotemporal distribution pattern of traffic flow, the spatiotemporal distribution pattern of fast charging demand and the basic charging path time data. The second determining module 33 can be used to determine the target charging planning result of electric vehicles based on the intraday-real-time path planning model and the set dual time scale scheduling strategy, and to determine the real-time vehicle allocation result of the road network and the new energy output scheduling information of the power grid side. The optimization scheduling module 34 can be used to obtain the constructed road-electric coupling collaborative optimization model, and take the distribution network operation cost and transportation network travel time cost as joint optimization objectives, and the target charging planning results, real-time vehicle allocation results of the road network and new energy power output scheduling information as constraints, to determine the road-electric system scheduling results matched by the road-electric coupling collaborative optimization model. The road-electric system scheduling results are used to improve the system's new energy utilization rate and road network traffic efficiency, and alleviate the impact of electric vehicle charging and driving on the power grid and transportation network.

[0085] The technical solution of this embodiment can analyze the fast charging behavior data of electric vehicles based on the constructed traffic flow travel path decision model to determine the spatiotemporal distribution patterns of traffic flow, the spatiotemporal distribution patterns of fast charging demand, and the basic charging path time data. Then, based on the spatiotemporal distribution patterns of traffic flow, the spatiotemporal distribution patterns of fast charging demand, and the basic charging path time data, a daily-real-time path planning model considering the prediction error of new energy vehicles is constructed. Next, based on the daily-real-time path planning model and the set dual-time-scale scheduling strategy, the target charging planning result of electric vehicles is determined, and the real-time vehicle allocation result of the road network and the new energy power output scheduling on the grid side are determined. The information is used to obtain the constructed road-electricity coupling collaborative optimization model. The joint optimization objectives are the operating cost of the distribution network and the travel time cost of the transportation network. The constraints are the target charging planning results, the real-time vehicle allocation results of the road network, and the new energy power output scheduling information. The road-electricity system scheduling results are used to improve the utilization rate of new energy and the traffic efficiency of the road network, and to alleviate the impact of electric vehicle charging and driving on the power grid and transportation network. This solves the problem of the impact of electric vehicle charging and driving on the power grid and transportation network in the existing technology, and improves the utilization rate of new energy and the traffic efficiency of the road network.

[0086] Optionally, the steps for constructing the traffic flow travel path decision model may include: Obtain the network topology diagram of the road network system, and determine the basic road network parameter information based on the network topology diagram; Based on the basic road network parameter information, the first type of traffic flow and the second type of traffic flow in the road network system are distinguished, and a first travel cost function for the first type of traffic flow and a second travel cost function for the second type of traffic flow are constructed. Based on the first travel cost function and the second travel cost function, determine the variational inequality constraints corresponding to the road network equilibrium state. By introducing a semi-dynamic traffic flow model, the coupling relationship between traffic surplus and multi-time period traffic flow is established, and the vehicle travel demand in the basic road network parameter information is corrected. Based on the revised traffic flow travel demand and the aforementioned variational inequality constraints, road network operation constraints are formed, and these constraints are used as the basis for the constructed traffic flow travel path decision model.

[0087] Optionally, the first determining module 31 can be used to acquire fast charging behavior data of electric vehicles, and determine the number and distribution characteristics of electric vehicles in different time periods and different areas according to the traffic flow travel path decision model, so as to form a traffic flow spatiotemporal distribution pattern. Based on the fast charging station location, fast charging time and fast charging power information extracted from the fast charging behavior data, the demand intensity and demand distribution characteristics of each fast charging station in different time periods are determined, forming a spatiotemporal distribution pattern of fast charging demand. Based on the spatiotemporal distribution patterns of traffic flow and fast charging demand, the travel time of electric vehicles from their origin to their destination, the queuing time at fast charging stations, and the fast charging duration are determined, and the basic charging path time data are obtained by summarizing these data.

[0088] Optionally, the construction module 32 can be used to determine the spatiotemporal distribution pattern of traffic flow as a basic constraint, and in combination with a preset time scale, determine the basic traffic capacity boundary of each road segment of the road network at different time periods, and construct road network traffic distribution constraints. Based on the spatiotemporal distribution pattern of fast charging demand and combined with the road network traffic allocation constraints, a service capacity constraint model for fast charging stations is constructed. The service capacity constraint model includes constraints on the number of fast charging piles, the maximum charging power of a single pile, and the charging queuing time threshold. The basic charging path time data is decomposed into road segment travel time, fast charging queuing time, and fast charging time. Based on a preset time scale, an objective function containing the total charging path time is constructed. The objective function is used to minimize the total travel time of the electric vehicle from the origin to the destination. By integrating the time scale, the road network traffic allocation constraints, the fast charging station service capacity constraint model, and the total time objective function, the intraday-real-time path planning model considering new energy prediction errors is determined.

[0089] Optionally, the second determining module 33 can be specifically used to determine the intraday pre-scheduling stage and the real-time dynamic correction stage based on the set dual-time-scale scheduling strategy. During the intraday pre-scheduling phase, the spatiotemporal distribution patterns of traffic flow, the spatiotemporal distribution patterns of fast charging demand, and the basic charging path time data are input into the intraday-real-time path planning model. Combined with the predicted output data of new energy vehicles, the intraday pre-charging planning results of electric vehicles, the intraday pre-vehicle allocation results of the road network, and the intraday pre-output scheduling plan of new energy vehicles on the grid side are determined. During the real-time dynamic correction phase, real-time traffic flow data of the road network, real-time operation data of fast charging stations, and real-time power output data of new energy are collected, and the predicted power output data of new energy are compared with the predicted power output data of new energy to determine the prediction error of new energy. Based on the new energy prediction error and the real-time traffic flow data of the road network, the constraints of the intraday-real-time route planning model are dynamically corrected to obtain the corrected real-time charging planning result, which is then used as the target charging planning result. Based on the revised real-time charging planning results, the intraday pre-vehicle allocation results of the road network are adjusted to obtain the real-time vehicle allocation results of the road network, and the intraday pre-output scheduling plan of the new energy power grid is adjusted to obtain the new energy power output scheduling information of the power grid.

[0090] Optionally, the steps for constructing the circuit-electricity coupling collaborative optimization model may include: A joint optimization objective function is constructed, which aims to minimize the operating cost of the distribution network and the travel time cost of the transportation network. The operating cost of the distribution network includes the cost of new energy consumption, the cost of grid loss, the operation and maintenance cost of fast charging stations and the cost of peak-valley electricity price difference. The travel time cost of the transportation network includes the overall travel time cost of the road network and the queuing time cost of fast charging stations. Obtain the grid-side constraints corresponding to the joint optimization objective function. The grid-side constraints include at least the distribution network node voltage constraints, branch power transmission constraints, new energy output upper limit constraints, fast charging station charging power constraints, and distribution network safe operation constraints. Obtain the traffic network-side constraints corresponding to the joint optimization objective function. The traffic network-side constraints include at least the traffic capacity constraints of each road segment, the traffic flow path flow conservation constraints, the fast charging station service capacity constraints, and the electric vehicle travel time threshold constraints. Based on the joint optimization objective function, grid-side constraints, and transportation network-side constraints, decision variables are determined, including electric vehicle charging power scheduling variables, road network traffic flow allocation variables, new energy power output scheduling variables, and fast charging station operation scheduling variables. The target charging planning results, real-time vehicle allocation results of the road network, and new energy power output scheduling information are used as boundary constraints to form a road-electricity coupling collaborative optimization model that includes the boundary constraints, the objective function, and the decision variables.

[0091] Optionally, the second determining module 33 can be specifically used to characterize the joint optimization objective by using the distribution network operating cost and the transportation network travel time cost, thereby forming a comprehensive quantitative expression for the joint optimization objective; Using the target charging planning results, real-time vehicle allocation results of the road network, and new energy power output scheduling information, quantifiable constraints are constructed. The associated joint optimization objective function and quantifiable constraints are input into the road-electric coupling collaborative optimization model. The road-electric system scheduling results are obtained by solving the model. The road-electric system scheduling results include at least the grid-side charging power scheduling instructions, the new energy output adjustment instructions, and the traffic network-side traffic flow path guidance instructions.

[0092] The circuit-electric coupling system optimization scheduling device provided in the embodiments of the present invention can execute the circuit-electric coupling system optimization scheduling method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method execution.

[0093] Example 4 Figure 4 A schematic diagram of an electronic device 40 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0094] like Figure 4 As shown, the electronic device 40 includes at least one processor 41 and a memory, such as a read-only memory (ROM) 42 or a random access memory (RAM) 43, communicatively connected to the at least one processor 41. The memory stores computer programs executable by the at least one processor. The processor 41 can perform various appropriate actions and processes based on the computer program stored in the ROM 42 or loaded from storage unit 48 into the RAM 43. The RAM 43 can also store various programs and data required for the operation of the electronic device 40. The processor 41, ROM 42, and RAM 43 are interconnected via a bus 44. An input / output (I / O) interface 45 is also connected to the bus 44.

[0095] Multiple components in electronic device 40 are connected to I / O interface 45, including: input unit 46, such as keyboard, mouse, etc.; output unit 47, such as various types of monitors, speakers, etc.; storage unit 48, such as disk, optical disk, etc.; and communication unit 49, such as network card, modem, wireless transceiver, etc. Communication unit 49 allows electronic device 40 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0096] Processor 41 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 41 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 41 performs the various methods and processes described above, such as circuit-coupled system optimization scheduling methods.

[0097] In some embodiments, the circuit-electric coupling system optimization scheduling method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 48. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 40 via ROM 42 and / or communication unit 49. When the computer program is loaded into RAM 43 and executed by processor 41, one or more steps of the circuit-electric coupling system optimization scheduling method described above may be performed. Alternatively, in other embodiments, processor 41 may be configured to execute the circuit-electric coupling system optimization scheduling method by any other suitable means (e.g., by means of firmware).

[0098] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0099] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0100] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0101] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0102] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0103] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0104] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0105] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for optimizing the scheduling of a road-electric coupling system, characterized in that, include: Based on the constructed traffic flow travel path decision model, we analyze the fast charging behavior data of electric vehicles to determine the spatiotemporal distribution pattern of traffic flow, the spatiotemporal distribution pattern of fast charging demand, and the basic charging path time data. Based on the aforementioned spatiotemporal distribution patterns of traffic flow, spatiotemporal distribution patterns of fast charging demand, and basic charging path time data, an intraday-real-time path planning model considering new energy prediction errors is constructed. Based on the intraday-real-time path planning model and the set dual-time-scale scheduling strategy, the target charging planning result for electric vehicles is determined, as well as the real-time vehicle allocation result of the road network and the new energy output scheduling information on the power grid side are determined. The constructed road-electricity coupling collaborative optimization model is obtained, and the operating cost of the distribution network and the travel time cost of the transportation network are used as joint optimization objectives. The target charging planning results, real-time vehicle allocation results of the road network, and new energy power output scheduling information are used as constraints to determine the road-electricity system scheduling results matched by the road-electricity coupling collaborative optimization model. The road-electricity system scheduling results are used to improve the utilization rate of new energy in the system and the traffic efficiency of the road network, and to alleviate the impact of electric vehicle charging and driving on the power grid and transportation network.

2. The method according to claim 1, characterized in that, The steps for constructing the traffic flow travel path decision model include: Obtain the network topology diagram of the road network system, and determine the basic road network parameter information based on the network topology diagram; Based on the basic road network parameter information, the first type of traffic flow and the second type of traffic flow in the road network system are distinguished, and a first travel cost function for the first type of traffic flow and a second travel cost function for the second type of traffic flow are constructed. Based on the first travel cost function and the second travel cost function, determine the variational inequality constraints corresponding to the road network equilibrium state. By introducing a semi-dynamic traffic flow model, the coupling relationship between traffic surplus and multi-time period traffic flow is established, and the vehicle travel demand in the basic road network parameter information is corrected. Based on the revised traffic flow travel demand and the aforementioned variational inequality constraints, road network operation constraints are formed, and these constraints are used as the basis for the constructed traffic flow travel path decision model.

3. The method according to claim 1, characterized in that, The analysis of electric vehicle fast-charging behavior data determines the spatiotemporal distribution patterns of traffic flow, the spatiotemporal distribution patterns of fast-charging demand, and the basic charging path time data, including: Acquire fast charging behavior data of electric vehicles, and determine the number and distribution characteristics of electric vehicles in different time periods and regions based on the traffic flow travel path decision model, thereby forming a spatiotemporal distribution pattern of traffic flow; Based on the fast charging station location, fast charging time and fast charging power information extracted from the fast charging behavior data, the demand intensity and demand distribution characteristics of each fast charging station in different time periods are determined, forming a spatiotemporal distribution pattern of fast charging demand. Based on the spatiotemporal distribution patterns of traffic flow and fast charging demand, the travel time of electric vehicles from their origin to their destination, the queuing time at fast charging stations, and the fast charging duration are determined, and the basic charging path time data are obtained by summarizing these data.

4. The method according to claim 1, characterized in that, The intraday-real-time path planning model, which considers the prediction error of new energy vehicles, is constructed based on the spatiotemporal distribution patterns of traffic flow, the spatiotemporal distribution patterns of fast charging demand, and the basic charging path time data. This includes: The spatiotemporal distribution pattern of traffic flow is determined as the basic constraint. Combined with the preset time scale, the basic traffic capacity boundary of each road segment of the road network at different time periods is determined, and the road network traffic distribution constraint conditions are constructed. Based on the spatiotemporal distribution pattern of fast charging demand and combined with the road network traffic allocation constraints, a service capacity constraint model for fast charging stations is constructed. The service capacity constraint model includes constraints on the number of fast charging piles, the maximum charging power of a single pile, and the charging queuing time threshold. The basic charging path time data is decomposed into road segment travel time, fast charging queuing time, and fast charging time. Based on a preset time scale, an objective function containing the total charging path time is constructed. The objective function is used to minimize the total travel time of the electric vehicle from the origin to the destination. By integrating the time scale, the road network traffic allocation constraints, the fast charging station service capacity constraint model, and the total time objective function, the intraday-real-time path planning model considering new energy prediction errors is determined.

5. The method according to claim 1, characterized in that, The process of determining the target charging plan result for electric vehicles based on the intraday-real-time path planning model and the established dual-time-scale scheduling strategy, and determining the real-time vehicle allocation result of the road network and the new energy power output scheduling information on the grid side, includes: Based on the established dual-time-scale scheduling strategy, the intraday pre-scheduling phase and the real-time dynamic correction phase are determined. During the intraday pre-scheduling phase, the spatiotemporal distribution patterns of traffic flow, the spatiotemporal distribution patterns of fast charging demand, and the basic charging path time data are input into the intraday-real-time path planning model. Combined with the predicted output data of new energy vehicles, the intraday pre-charging planning results of electric vehicles, the intraday pre-vehicle allocation results of the road network, and the intraday pre-output scheduling plan of new energy vehicles on the grid side are determined. During the real-time dynamic correction phase, real-time traffic flow data of the road network, real-time operation data of fast charging stations, and real-time power output data of new energy are collected, and the predicted power output data of new energy are compared with the predicted power output data of new energy to determine the prediction error of new energy. Based on the new energy prediction error and the real-time traffic flow data of the road network, the constraints of the intraday-real-time route planning model are dynamically corrected to obtain the corrected real-time charging planning result, which is then used as the target charging planning result. Based on the revised real-time charging planning results, the intraday pre-vehicle allocation results of the road network are adjusted to obtain the real-time vehicle allocation results of the road network, and the intraday pre-output scheduling plan of the new energy power grid is adjusted to obtain the new energy power output scheduling information of the power grid.

6. The method according to claim 1, characterized in that, The construction steps of the circuit-electric coupling collaborative optimization model include: A joint optimization objective function is constructed, which aims to minimize the operating cost of the distribution network and the travel time cost of the transportation network. The operating cost of the distribution network includes the cost of new energy consumption, the cost of grid loss, the operation and maintenance cost of fast charging stations and the cost of peak-valley electricity price difference. The travel time cost of the transportation network includes the overall travel time cost of the road network and the queuing time cost of fast charging stations. Obtain the grid-side constraints corresponding to the joint optimization objective function. The grid-side constraints include at least the distribution network node voltage constraints, branch power transmission constraints, new energy output upper limit constraints, fast charging station charging power constraints, and distribution network safe operation constraints. Obtain the traffic network-side constraints corresponding to the joint optimization objective function. The traffic network-side constraints include at least the traffic capacity constraints of each road segment, the traffic flow path flow conservation constraints, the fast charging station service capacity constraints, and the electric vehicle travel time threshold constraints. Based on the joint optimization objective function, grid-side constraints, and transportation network-side constraints, decision variables are determined, including electric vehicle charging power scheduling variables, road network traffic flow allocation variables, new energy power output scheduling variables, and fast charging station operation scheduling variables. The target charging planning results, real-time vehicle allocation results of the road network, and new energy power output scheduling information are used as boundary constraints to form a road-electricity coupling collaborative optimization model that includes the boundary constraints, the objective function, and the decision variables.

7. The method according to claim 1, characterized in that, The method of determining the road-electricity system scheduling result matched by the road-electricity coupling collaborative optimization model, which uses the operating cost of the distribution network and the travel time cost of the transportation network as joint optimization objectives, and the charging planning results, real-time vehicle allocation results of the road network, and new energy power output scheduling information as constraints, includes: The joint optimization objective is represented by the operating cost of the power distribution network and the travel time cost of the transportation network, forming a comprehensive quantitative expression for the joint optimization objective; Using the target charging planning results, real-time vehicle allocation results of the road network, and new energy power output scheduling information, quantifiable constraints are constructed. The associated joint optimization objective function and quantifiable constraints are input into the road-electric coupling collaborative optimization model. The road-electric system scheduling results are obtained by solving the model. The road-electric system scheduling results include at least the grid-side charging power scheduling instructions, the new energy output adjustment instructions, and the traffic network-side traffic flow path guidance instructions.

8. A circuit-electric coupling system optimization scheduling device, characterized in that, include: The first determining module is used to analyze the fast charging behavior data of electric vehicles based on the constructed traffic flow travel path decision model, and determine the spatiotemporal distribution pattern of traffic flow, the spatiotemporal distribution pattern of fast charging demand, and the basic charging path time data. The construction module is used to build an intraday-real-time path planning model that takes into account the prediction error of new energy vehicles, based on the spatiotemporal distribution pattern of traffic flow, the spatiotemporal distribution pattern of fast charging demand and the basic charging path time data. The second determining module is used to determine the target charging planning result of electric vehicles based on the intraday-real-time path planning model and the set dual-time-scale scheduling strategy, and to determine the real-time vehicle allocation result of the road network and the new energy power output scheduling information of the power grid side. The optimization scheduling module is used to obtain the constructed road-electricity coupling collaborative optimization model, and to determine the road-electricity system scheduling result matched by the road-electricity coupling collaborative optimization model, with the distribution network operating cost and transportation network travel time cost as joint optimization objectives, and with the target charging planning results, real-time vehicle allocation results of the road network and new energy power output scheduling information as constraints. The road-electricity system scheduling result is used to improve the system's new energy utilization rate and road network traffic efficiency, and to alleviate the impact of electric vehicle charging and driving on the power grid and transportation network.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the circuit-electric coupling system optimization scheduling method according to any one of claims 1-7.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the circuit-electric coupling system optimization scheduling method as described in any one of claims 1-7.

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