An in-station control system and method for a power service area battery swap station

Abstract

The present application relates to the technical field of battery swap station in-station control, and particularly relates to a power service area battery swap station in-station control system and method, which solves the technical problem that the service area battery swap station in the prior art is difficult to effectively predict and schedule when multiple devices are connected to power, and elaborates on the regional charging station coordination and scheduling system from the aspects of system structure, load prediction, coordination control strategy, etc. The system is based on reality, and according to the hierarchical characteristics of the electric vehicle charging and discharging service network in structure, applies a hierarchical and partitioned electric vehicle scheduling method, establishes an electric vehicle charging station coordination control strategy, and realizes different scheduling requirements, including scheduling control of the charging and discharging station.

Description

Technical Field

[0001] The present invention relates to the field of in-station control technology for battery swap stations, and in particular to an in-station control system and method for a battery swap station in an electric power service area. Background Art

[0002] Battery swap stations in power service areas provide services for various types of new energy electric vehicles. As the number of electric vehicles continues to grow, the random and disorderly access of large numbers of electric vehicles to the power grid in power service areas for charging and discharging will have a certain impact on the operation of the grid. Allowing electric vehicle users to charge in an disorderly manner may not only affect the economic efficiency of grid operation, but also seriously deteriorate power quality, cause voltage offsets at access nodes, and affect the security and stability of the grid. If electric vehicle users can charge in an orderly manner, it will not only not burden the grid but also achieve peak load shifting and valley filling, reducing electricity purchase costs. Considering that charging stations are the carriers for the centralized access of electric vehicles to the grid, a hierarchical and regional scheduling method can be adopted. The upper layer is the regional dispatch center that issues scheduling plans for charging stations, and the lower layer implements charging control strategies within the charging stations to achieve orderly charging of electric vehicles.

[0003] Based on the above, it is of great significance to establish an in-station control system for battery swap stations with charging station load demand prediction functions, multiple scheduling objectives, inter-network control functions under peak shaving and valley filling, and orderly charging control functions within the station. Summary of the Invention

[0004] The present invention aims to solve the technical problem in the prior art that it is difficult to effectively predict and schedule the power consumption of multiple devices in a service area power swap station, and provides an in-station control system and method for a power service area power swap station.

[0005] In order to solve the above technical problems, the technical solutions of the present invention are as follows:

[0006] A control system and method for a power battery swap station in a power service area, comprising:

[0007] The input data layer provides data support for the algorithms in the business logic layer 2.

[0008] The algorithms in the business logic layer include:

[0009] Historical charging data algorithm analysis library, parameter correction library, charging facility equipment library, electric vehicle data, battery data, real-time data interface, and dispatch instruction interface;

[0010] The support data provided by the historical charging data algorithm analysis library to the business logic layer includes: historical charging data, which includes: load forecast learning data, load forecast data, regional level scheduling data, station level coordinated control data, and parameter correction data;

[0011] The parameter correction library includes: parameter correction strategies, correction schemes and correction results that have been applied;

[0012] The business logic layer generates prediction results and scheduling processes for charging and discharging stations in the region;

[0013] The business logic layer includes a prediction learning module, a load prediction module, a model parameter correction module, and a regional scheduling module; and

[0014] Simulation evaluation module;

[0015] The prediction learning module continuously adjusts the load prediction module and parameters using the historical charging data algorithm analysis library;

[0016] The load forecasting data performed by the load forecasting module includes:

[0017] Station-level and regional-level forecast data;

[0018] The model parameter correction module corrects the load forecast model and the parameters in the model according to the load forecast data and real-time data to ensure that the load forecast data is close to the real data;

[0019] The regional scheduling module decomposes the upper-level scheduling instructions and combines them with the load forecast data of each region to form a scheduling plan for each region. Through station-level coordinated control, it forms a coordinated control strategy within the station based on the load forecast data and charging demand within the station.

[0020] The simulation evaluation module simulates the forecast and scheduling results by simulating different parameters and conditions, and evaluates the coordinated control strategy from the perspective of peak shaving and valley filling and economic operation;

[0021] Afterwards, after receiving the data from the business logic layer, the application layer can display the prediction results, scheduling plans and simulation evaluation results to the user in concise and clear charts, curves, tables and other forms, forming an orderly charging control plan and coordinated control evaluation results within the station.

[0022] Specifically, the load forecasting module is constructed as a load forecasting model;

[0023] The load forecasting model is a load demand forecast for the charging station, and the forecast data information provided includes: the number and type of electric vehicles;

[0024] The load forecasting model adopts an AR (Auto-Regressive Model) model or an ARMA (AutoRegressive and Moving Average Model) model for forecasting under the condition of a mainly stable sequence load.

[0025] Specifically, when the ARMA (AutoRegressive and Moving Average Model) model is used for prediction based on the EBL (Explanation-based Learning) machine learning method, the model correction process is as follows:

[0026] Step S101, preset and read an instance;

[0027] Step S102, searching for response rules and forming a causal explanation;

[0028] Step S103, summarizing the overall extraction judgment control method;

[0029] Step S104, determining whether the new data parameters need to be adjusted, if the result is N, then the process ends;

[0030] In step S105, when the result is Y, the parameters are diverged in different directions and dimensions, and compared with the instance accuracy to see whether the parameters meet the requirements. If the judgment is yes, the process ends; if the judgment is no, step S105 is repeated.

[0031] Specifically, it also includes: coordinated control mode:

[0032] The process of the coordinated control method is:

[0033] Each day is divided into n time periods. There are two types of coordinated control decisions. One is to reduce the peak-valley difference of the power grid load and thus alleviate the peak-valley pressure of the power grid; the other is to minimize the electricity purchase cost and maximize the profit of the charging station. The coordinated control decision adopts the Lemke algorithm to control the coordinated control decision.

[0034] Specifically, the execution process of the Lemke algorithm is:

[0035] S201, after initialization, find a quasi-complementary basic feasible solution;

[0036] S202, find a new complementary basic feasible solution by the pivot elimination method;

[0037] S203, when it is determined that a non-basic variable is found, the process ends; when it is determined that no non-basic variable is found, the process goes to step S202.

[0038] Specifically, it also includes: the deployment of control systems within battery swap stations in the power service area, specifically: a hierarchical deployment approach is adopted, with deployment carried out in the centralized control center and each charging and swap station. The main system of the centralized control center obtains scheduling information through the interface with the power grid dispatching system, decomposes the scheduling plan and sends it to each charging and swap station, and then the station-level system of each charging and swap station arranges the charging of electric vehicles.

[0039] The present invention has the following beneficial effects:

[0040] The regional charging station coordination and dispatching system is elaborated in detail from the aspects of system structure, load forecasting, coordination control strategy, etc. The system starts from reality and applies the layered and partitioned electric vehicle scheduling method according to the structural characteristics of the electric vehicle charging and discharging service network. It establishes a coordinated control strategy for electric vehicle charging stations to achieve different scheduling needs, including the scheduling control of charging and discharging stations (including discrete charging piles). BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0042] Figure 1 Schematic diagram of the system structure of the present invention;

[0043] Figure 2 This is an embodiment of the daily load forecast diagram of the present invention;

[0044] Figure 3 It is a parameter correction flow chart of the present invention;

[0045] Figure 4 This is a flow chart of the Lemke algorithm of the present invention. DETAILED DESCRIPTION

[0046] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention; it should be noted that in this application, for the convenience of description, the "left side" in the current view is referred to as the "first end", the "right side" is referred to as the "second end", the "upper side" is referred to as the "first end", and the "lower side" is referred to as the "second end". The purpose of such description is to clearly express the technical solution, and it should not be understood as an improper limitation on the technical solution of this application.

[0047] The present invention aims to solve the technical problem in the prior art that it is difficult to effectively predict and dispatch the power consumption of multiple devices in the service area battery swap station. It provides a control system and method for the power service area battery swap station. The regional charging station coordination control system serves as a tool for dispatchers to assist in decision-making, which can realize the prediction of the load demand and backup power of charging stations in the region. According to the two goals of smoothing the load curve and minimizing the cost of electricity purchase, the electric vehicle charging and swapping service network is structurally layered, different control measures are taken for charging stations of different levels, and scheduling plans for each charging station at multiple time and space scales in the region are given to realize coordinated scheduling of charging stations within the region. The system structure is divided into basic data layer, business logic layer and display application layer, as shown in the attached figure. Figure 1 As shown, the layers are interconnected, including: input data layer 1, which is used to provide data support for the algorithms of business logic layer 2;

[0048] The algorithms in the business logic layer include:

[0049] Historical charging data algorithm analysis library, parameter correction library, charging facility equipment library, electric vehicle data, battery data, real-time data interface, and dispatch instruction interface;

[0050] The support data provided by the historical charging data algorithm analysis library to the business logic layer 2 includes: historical charging data, which includes: load forecast learning data, load forecast data, regional level scheduling data, station level coordinated control data, and parameter correction data;

[0051] The parameter correction library includes: parameter correction strategies, correction schemes and correction results that have been applied;

[0052] The business logic layer generates prediction results and scheduling processes for charging and discharging stations in the region;

[0053] The business logic layer includes a prediction learning module, a load prediction module, a model parameter correction module, and a regional scheduling module; and

[0054] Simulation evaluation module;

[0055] The prediction learning module continuously adjusts the load prediction module and parameters using the historical charging data algorithm analysis library;

[0056] The load forecasting data performed by the load forecasting module includes:

[0057] Station-level and regional-level forecast data;

[0058] The model parameter correction module corrects the load forecast model and the parameters in the model according to the load forecast data and real-time data to ensure that the load forecast data is close to the real data;

[0059] The regional scheduling module decomposes the upper-level scheduling instructions and combines them with the load forecast data of each region to form a scheduling plan for each region. Through station-level coordinated control, it forms a coordinated control strategy within the station based on the load forecast data and charging demand within the station.

[0060] The simulation evaluation module simulates the forecast and scheduling results by simulating different parameters and conditions, and evaluates the coordinated control strategy from the perspective of peak shaving and valley filling and economic operation;

[0061] Afterwards, after receiving the data from the business logic layer, the display application layer can display the prediction results, scheduling plans and simulation evaluation results to the user in the form of concise and clear charts, curves, tables and other forms, forming an orderly charging control plan and coordinated control evaluation results within the station.

[0062] In a specific embodiment, the load forecasting module is constructed as a load forecasting model;

[0063] The load forecasting model is a load demand forecast for the charging station, and the forecast data information provided includes: the number and type of electric vehicles;

[0064] The load forecasting model adopts an AR (Auto-Regressive Model) model or an ARMA (AutoRegressive and Moving Average Model) model for forecasting under the condition of a mainly stable sequence load.

[0065] In a specific embodiment, when the ARMA (AutoRegressive and Moving Average Model) model is used for prediction based on the EBL (Explanation-based Learning) machine learning method, the model modification process is:

[0066] Step S101, preset and read an instance;

[0067] Step S102, searching for response rules and forming a causal explanation;

[0068] Step S103, summarizing the overall extraction judgment control method;

[0069] Step S104, determining whether the new data parameters need to be adjusted, if the result is N, then the process ends;

[0070] In step S105, when the result is Y, the parameters are diverged in different directions and dimensions, and compared with the instance accuracy to see whether the parameters meet the requirements. If the judgment is yes, the process ends; if the judgment is no, step S105 is repeated.

[0071] Specifically, after determining the order and parameters of the ARMA model for charging station load demand forecasting, the EBL machine learning method was chosen to modify the ARMA model parameters. Machine learning mimics the human learning mechanism by establishing a computational or cognitive model similar to the human learning process. As a type of machine learning, EBL divides the entire learning process into two steps. First, an instance is provided to the system. The system's knowledge base searches for rules related to the instance and finds and records the causal relationship explanations between the rules and the instance. The system then summarizes the causal relationship explanations, retaining the primary influencing factors and omitting the secondary influencing factors. It then appropriately combines the primary factors to ultimately form a judgment rule or control method.

[0072] In a specific embodiment, the coordinated control method is also included:

[0073] The process of the coordinated control method is:

[0074] Each day is divided into n time periods. There are two types of coordinated control decisions. One is to reduce the peak-valley difference of the power grid load and thus alleviate the peak-valley pressure of the power grid; the other is to minimize the electricity purchase cost and maximize the profit of the charging station. The coordinated control decision adopts the Lemke algorithm to control the coordinated control decision.

[0075] In a specific embodiment, the execution process of the Lemke algorithm is:

[0076] S201, after initialization, find a quasi-complementary basic feasible solution;

[0077] S202, find a new complementary basic feasible solution by the pivot elimination method;

[0078] S203, when it is determined that a non-basic variable is found, the process ends; when it is determined that no non-basic variable is found, the process goes to step S202.

[0079] The electric equipment represented by new energy vehicles, the orderly access optimization strategy of electric vehicles is mostly based on the direct scheduling of the dispatch center V2G. Considering that this top-down scheduling mode has high requirements for the data processing capacity and communication capacity of the dispatch center, therefore, after the system obtains the load prediction data, it adopts a hierarchical and partitioned scheduling mode, divides a day into n time periods, and the system decision model has two, one is to reduce the peak valley difference of power grid load to reduce the peak shaving pressure of power grid, and the other is to realize the maximum profit of charging station with the lowest purchase cost. Both scheduling models can get the scheduling instructions of the charging station. The scheduling instructions of each time period of each regional charging station are issued, then the in-station charging optimization of single charging station is carried out, and finally the multi-time period charging plan of the charging station is obtained. Since the scheduling instruction problem issued to the charging station is a quadratic programming problem involving equality and inequality constraints after modeling, Lemke algorithm is adopted to solve the model, and after obtaining the scheduling instruction issued to the charging station in each time period, the next step is to carry out orderly charging in the charging station. Since the scheduling system is mainly aimed at lithium battery electric vehicles, it is considered that the charging of each type of charging pile in the charging station keeps constant power in a unit time period, that is, the charging power of the charging station in a charging time period is a discrete value, so there will be a certain deviation between the charging plan of the charging station and the scheduling command issued by the dispatch center. On the one hand, the charging plan in the charging station should be close to the scheduling command issued by the dispatch center, and on the other hand, considering the operation time of the charging and battery replacement and the labor cost of the staff, it is hoped that the number of changes of the charging pile in the charging station is small. Therefore, for the orderly charging in the charging station, the sum of the number of changes of the charging pile in the adjacent time period under the small deviation of the scheduling command is taken as the target. A multi-objective scheduling model is established. In the allowed variation range of the first layer target, the number of changes of the charging pile is minimized to save cost and labor time.

[0080] In a specific embodiment, it also includes: system deployment, which is deployed in a hierarchical manner at the centralized control center and each charging and swapping station. The main system of the centralized control center obtains scheduling information through the interface with the power grid dispatching system, decomposes the scheduling plan and sends it to each charging and swapping station. Then, the station-level system of each charging and swapping station arranges the charging of electric vehicles. The coordinated control system of charging and swapping stations in the region operates stably and reliably, and can complete the load forecast and load demand forecast of the charging and swapping station on a single day. The prediction accuracy meets the scheduling requirements, and can realize the scheduling instructions of the charging and swapping station under different strategies of peak shaving and valley filling economic operation, complete orderly charging within the charging and swapping station, and provide coordinated control with the goals of peak shaving and valley filling and economic operation. For the above two goals, the system is simulated and verified. This technical solution elaborates on the coordinated scheduling system of regional charging stations in terms of system structure, load forecasting, coordinated control strategy, etc. The system starts from reality and applies a hierarchical and partitioned electric vehicle scheduling method according to the structural layered characteristics of the electric vehicle charging and discharging service network. A coordinated control strategy for electric vehicle charging stations is established to realize the scheduling control of charging and discharging stations (including discrete charging piles) under different scheduling requirements.

[0081] Obviously, the above embodiments are merely examples for clarity of explanation and are not intended to limit the implementation methods. Those skilled in the art will readily appreciate that other variations or modifications based on the above descriptions are possible. It is not necessary and impossible to enumerate all implementation methods here. Obvious variations or modifications arising therefrom remain within the scope of protection of the present invention.

Claims

1. A control system and method for a power exchange station in a power service area, characterized in that: include: The input data layer provides data support for the algorithms in the business logic layer; The algorithms in the business logic layer include: Historical charging data algorithm analysis library, parameter correction library, charging facility equipment library, electric vehicle data, battery data, real-time data interface, and dispatch instruction interface; The support data provided by the historical charging data algorithm analysis library to the business logic layer includes: historical charging data, which includes: load forecast learning data, load forecast data, regional level scheduling data, station level coordinated control data, and parameter correction data; The parameter correction library includes: parameter correction strategies, correction schemes and correction results that have been applied; The business logic layer generates prediction results and scheduling processes for charging and discharging stations in the region; The business logic layer includes a prediction learning module, a load prediction module, a model parameter correction module, and a regional scheduling module; and Simulation evaluation module; The prediction learning module continuously adjusts the load prediction module and parameters using the historical charging data algorithm analysis library; The load forecasting data performed by the load forecasting module includes: Station-level and regional-level forecast data; The model parameter correction module corrects the load forecast model and the parameters in the model according to the load forecast data and real-time data to ensure that the load forecast data is close to the real data; The regional scheduling module decomposes the upper-level scheduling instructions and combines them with the load forecast data of each region to form a scheduling plan for each region. Through station-level coordinated control, it forms a coordinated control strategy within the station based on the load forecast data and charging demand within the station. The simulation evaluation module simulates the forecast and scheduling results by simulating different parameters and conditions, and evaluates the coordinated control strategy from the perspective of peak shaving and valley filling and economic operation; Afterwards, after receiving the data from the business logic layer, the display application layer can present the prediction results, scheduling plan and simulation evaluation results to the user in a variety of forms such as concise charts, curves, and tables, thereby forming an orderly charging control plan and coordinated control evaluation results within the station; The load forecasting module is constructed as a load forecasting model; The load forecasting model is a load demand forecast for the charging station, and the forecast data information provided includes: the number and type of electric vehicles; The load forecasting model adopts AR model or ARMA model for forecasting under the main stable sequence load condition; When the ARMA model is used for prediction based on the EBL machine learning method, the model modification process is as follows: Step S101, preset and read an instance; Step S102, searching for response rules and forming a causal explanation; Step S103, summarizing the overall extraction judgment control method; Step S104, determining whether the new data parameters need to be adjusted, if the result is N, then the process ends; In step S105, if the result is Y, the parameters are diverged in different directions and dimensions, and compared with the instance accuracy to see if the parameters meet the requirements. If the judgment is yes, the process ends; if the judgment is no, step S105 is repeated; Also includes: Coordinated control method: The process of the coordinated control method is: Each day is divided into n time periods. The coordinated control decision-making includes two types: one is to reduce the peak load difference and thus alleviate the peak load regulation pressure of the power grid; the other is to reduce the peak load difference and thus alleviate the peak load regulation pressure of the power grid. The other one takes the lowest electricity purchase cost as the goal to achieve the maximum profit of the charging station, and the coordinated control decision adopts the Lemke algorithm to control the coordinated control decision.

2. The control system and method for the power service area battery swap station according to claim 1, characterized in that: The execution process of the Lemke algorithm is: S201, after initialization, find a quasi-complementary basic feasible solution; S202, find a new complementary basic feasible solution by the pivot elimination method; S203, when it is determined that a non-basic variable is found, the process ends; when it is determined that no non-basic variable is found, the process goes to step S202.

3. The control system and method for the power service area battery swap station according to claim 2, characterized in that: It also includes: the deployment of control systems within battery swap stations in the power service area, specifically: a hierarchical deployment approach is adopted, with deployment carried out in the centralized control center and each charging and swap station. The main system of the centralized control center obtains dispatch information through the interface with the power grid dispatch system, decomposes the dispatch plan and sends it to each charging and swap station, and then the station-level system of each charging and swap station arranges the charging of electric vehicles.

Images