An Application Designed for Guiding the Coordinated Charging of Electric Vehicles

Abstract

Guiding the coordinated charging of electric vehicles can alleviate the load fluctuation of power systems within a local area caused by uncoordinated charging of electric vehicles and greatly reduce the cost of power system operation. This will become an inevitable development trend of future energy system transformation. In this paper, a new mobile application is built to realize the dynamic adjustment of electric vehicle charging prices according to the change in weather conditions to guide the coordinated charging of electric vehicles. After systematically introducing the structure and data flow process of the application, we simulate the fluctuation of charging prices under various weather conditions using the electricity load data of North China and verify the good performance of the application. We believe that this application can help power systems to achieve low-carbon transformation by adopting a new dynamic time-of-use pricing charging model.

1. Introduction

The vigorous development of electric vehicles (EVs) can get rid of the environmental damage and dependence on petroleum resources caused by conventional petrol vehicles [1]. Without the guidance of basic policies or related regulations to guide the charging behavior of EV owners, the time and space required for charging EVs will be uncertain under the influence of seasonal and geographical factors [2]. Uncoordinated charging behavior can directly affect the magnitude of electric load fluctuations in local areas, increase the cost of power system operation [3], and reduce the utilization of fossil fuels.

The rapid development of wind and photovoltaic (PV) power generation on the supply side of the power system has become a common development trend worldwide. However, the energy supply side, whether wind or PV power generation technology, can be equally affected by seasonal and geographical factors, etc. Electricity supply has inherent characteristics, such as unstable supply, low energy density, and difficulty in accurate prediction. These uncertainties can put great pressure on the grid’s supply-side dispatching capabilities [4,5].

In the future, if we want to make the power system run smoothly and realize the steady transition of energy production methods from traditional thermal power generation to wind and PV power generation, we must solve the above two kinds of uncertainties. One is the uncertainty of the charging time on the electricity consumption side, and the other is the uncertainty of the power generation capacity on the generation side affected by environmental factors. In essence, it is to solve the mismatch between the supply and demand time of the two, which can improve energy utilization and reduce power storage and transportation costs [6].

Vehicle-to-grid (V2G) is the inevitable trend of low-carbon energy transition and the key to solving the above two problems. The current research focuses on unidirectional V2G, i.e., coordinated charging mode. Unidirectional scheduling of EVs’ charging to consume excess power can help the grid shave peaks and fill valleys, achieving a win-win situation.

The demand-side energy management strategies include pricing approaches [7]. Specifically, the aggregators or EV owners can shift their load according to the announced electricity price mechanism designed by the utility grid, and the total load curves can then be regulated accordingly [8,9]. The application of time-of-use (TOU) charging pricing to guide EV owners’ charging behavior in the world is mostly at the stage of static TOU prices [8,10]. In China, for example, most of the current urban residential electricity consumption is charged by sectional tiered prices. The industrial sector divides prices by seasons and fixed hours, dividing different periods of different seasons into peak hours, flat hours, and valley hours. However, it cannot respond quickly to the problem of load fluctuations in the power system due to the weather-related effects of renewable energy generation.

To address the above issues, this study designed a mobile application for cell phones. This application was designed to match the fluctuating load curve of the renewable energy generation system by guiding the charging behavior of EV owners through real-time fluctuating price changes. This means that the renewable energy generation power signal is converted into a price signal in real-time to provide EV owners, guiding them to assist in peak and valley reduction of the power system.

The application connects the beginning of the data stream to the power plant, which outputs the electricity price every two hours based on the weather and the amount of electricity generated. It is published on the application platform as quickly as possible to achieve a real-time presentation of the electricity price for the users. At the same time, the power plant can also give a price forecast based on the forecast weather conditions of the coming week, allowing consumers to choose their future charging times. This format satisfies the new charging model of the electricity system.

This article will systematically introduce the composition structure and data flow process of the application, which helps the electric power system to realize the energy structure transformation. The structure of the article is as follows: Section 2 reviews the literature on the relationship between EVs and the grid, and the studies of the electricity price regulation mechanism are also reviewed; Section 3 presents the specific structure of the application program, business processes, and data flow directions. Section 4 examines and analyzes the simulation results of the application. Section 5 summarizes the paper. Section 6 presents the limitations of this study and offers future perspectives.

2. Literature Review

2.1. EV Charging Price Mechanism

The effectiveness of coordinating the charging time of EVs through a price mechanism, thus reducing the load on the power system, has achieved a consensus in most research-based papers [11]. However, multiple research theories exist on specific price-setting methodologies.

Current methods of mainstream charging price setting include dynamic pricing and static TOU pricing [12]. Dynamic pricing mainly constructs an algorithm model to calculate the electricity price by collecting the charging information of EVs and the load power of the grid with an aggregator, also including the charging demand habits of vehicle owners [13,14]. Some of the common algorithmic models are the interior point method [15], the particle swarm optimization (PSO) algorithm [16,17], and the genetic algorithm. The grid indirectly coordinates the charging behavior of all EVs through a real-time power variation pricing scheme [18] to minimize the charging cost. Furthermore, a unidirectional communication network is necessary to ensure the price information can be broadcast to EV owners [13]. TOU charging models reduce EV owners’ electricity bills by shifting charging times from peak load periods to valley load periods [19,20,21]. The charging price depends on time only, and its range and corresponding period are predetermined according to consumer behavior and the objectives to be achieved through TOU pricing [22]. From the perspective of grid load management, Ma et al. [23] constructed an optimized rates model and showed that TOU prices show great advantages in reducing costs and flattening the grid load curve. The regional TOU price model can effectively reduce the charging cost of customers and mitigate peak-valley load differences and network losses [24].

2.2. Application of V2G

The charging demand of EVs will increase the peak load of the power grid [25], and their large-scale uncoordinated charging will put enormous pressure on the power supply, thus affecting the safety and stability of the whole power system. To solve this problem, it is necessary to optimize the charging of large EVs [26]. From a technical point of view, the V2G scheme is an important transformation path [27]. In V2G, the aggregated power from a group of EVs can be used to support the grid by providing regulation services (to stabilize voltage and frequency) or reserve services (to meet sudden increases in demand or generator set outages). V2G modes can be divided into bidirectional V2G and unidirectional V2G. Bidirectional V2G means that while conventional charging piles supply power to the car, the EV power battery is also regarded as a decentralized energy storage unit of the power system. Reasonable utilization of EVs’ battery energy to achieve reverse power supply will alleviate the load shock of the grid. Thus, the EV is not only a movable load, but also a distributed energy source. This mode can be used to provide services such as frequency regulation or peak shaving of power grids [12]. However, due to technical and cost problems, it has not been possible to promote it on a large scale for the time being.

Unidirectional V2G is used to guide EVs to coordinated charging by cooperating with the grid operation rules. There are no reserve services. What’s more, the unidirectional V2G services can help consume the abundant renewable energy sources, such as solar and wind energy, by the coordinated charging strategy [28]. Unidirectional V2G can reduce power consumption during peak hours, improve power utilization during valley hours, and alleviate the impact of random charging demand on the grid [5]. Although the unidirectional V2G services still face some obstacles, solutions have been in process. Additionally, unidirectional V2G would build a solid foundation to implement bidirectional V2G in the future [28].

From the literature reviewed so far, it appears that EV charging puts a lot of pressure on the grid. Effective measures can be taken to alleviate the pressure by adjusting the rate of electricity to guide the charging behavior of EV owners. However, the current unidirectional V2G model does not achieve real-time dynamic pricing, but rather a fixed time-period matching fixed pricing method. One important reason is the lack of a user-oriented system that can dynamically match charging prices and charging times, which is the focus of this study.

3. Methodology and Programming

3.1. Design Objectives

The application defines the service category as external service and internal management based on the different audiences it is facing (

Table 1. Application Composition.

Service Category	Results	Service Users	Function Details
External Services	User-side App	EV Users	Charging pile finding, route planning, forecast price inquiry, transaction payment, order management
Internal Management	Operations Management Platform	Operations Management	Predictive price processing, fund management, charging pile information management, system maintenance

). The carrier of external services is mainly the user-side app, which serves the users of EVs. While providing innovative features, such as real-time and predictive electricity price inquiry, it retains conventional functions such as charging pile retrieval, route planning navigation, transaction payment, and order management. The internal management target is the operations management personnel. The data interface reserved for the operations management side is directly connected to the power plant, thus enabling the presentation of dynamic electricity prices on the user side.

3.2. Organizational Structure

The functions of the user-side app and the operations management system are subdivided and improved respectively (Figure 1).

Among them, the user-side forecast price inquiry function can not only check the current real-time electricity price but also predict the electricity price of the next week in advance. The system performs a basic analysis based on the electricity rate provided by the power plant and can provide a prediction of the optimal charging time. The transaction payment mainly involves the identification function of the corresponding charging pile and the selection of the payment method. The order management module allows for querying and dealing with past transactions.

The key function of the operations management side is the processing of electricity prices. One is the data interface reserved for the power plant and the other is the data export reserved for the user side. The management of charging piles needs to consider the input of new pile information, the monitoring of the real-time operation status of piles, and the modification of pile information. The backend of the money side will be the unified statistics and custody of the amount paid by the user on the user side. The application acts as a money transfer station and is responsible for financial settlement with power plants and power grids after the transaction is completed.

3.3. Business Processes

3.3.1. User-Side App Business Process Analysis

(1)

Forecast Electricity Price Inquiry

Users can check the price of electricity on the same day and the price of electricity in the coming week, so that they can choose a cheaper time to charge. The application will also recommend the best charging time for the users by combining previous charging data and guiding the charging behavior of the car owner through price changes.

(2)

Charging Pile Search

It mainly completes tasks such as charging pile search, charging pile usage query, and charging process monitoring. Users can choose to select charging piles from favorites in the personal center or input expected charging pile keywords such as charging pile location, charging type, charging station brand name, etc. from the search interface to search. The system will retrieve eligible charging piles in the background database for display, and users can further select and view them in the displayed list of charging piles.

(3)

Route Planning

It mainly completes the tasks of positioning and navigation, route planning, and so on. Considering the actual situation in driving, the navigation interface can distinguish the types of charging piles according to different search criteria. Clicking on a charging pile can display its parameters, real-time usage status, charging rates, parking charges, etc. The GPS directly uses a plug-in from Baidu Maps that can provide the system with the current road condition and traffic information to help it plan the route. Based on the information obtained, the system will make different plans for the user to choose, such as the shortest driving distance or the shortest driving time, and give advice on the choice. The user can start navigation after selecting a path. The navigation dynamically adjusts to the user’s real-time movements during the drive, with the option to end when the destination is reached.

(4)

Transaction Payment

The main function is to complete the transaction payment. Users can identify charging stake information by scanning the exclusive QR code on the current charging stake or manually entering the exclusive terminal code. Then simply click to start charging and wait for the charging to be completed. After completion, the system will calculate the charging amount based on the charging time and the current kilowatt-hour price. Then the corresponding order will be generated. The user can select the payment method after confirming the correctness. After the charging transaction is completed by the third-party platform, the payment voucher for this order can be generated.

(5)

Order Management

It mainly completes tasks such as transaction inquiry and order management (Figure 2). The system and database record all orders generated from transactions, and extract key information to form the corresponding classification table on the order interface. Users can query transaction records by date, location, and other conditions, and support fuzzy query. Order management can sort orders according to different keywords for a customized sorting display.

3.3.2. Business Process Analysis of Operations Management Platform

(1)

Electricity Price Processing

The system integrates the original price of electricity provided by the power plant with the grid company’s toll fee to calculate the price of charging for the customer for that day. In ideal conditions, the power plant can combine the weather conditions (wind or solar) of the location and calculate the charging price for the coming week by predicting the power generation. Then the function of predicting the price can also be implemented. By integrating the collation, the rates are then displayed and updated on the user’s side.

(2)

Charging Pile Management

The charging pile management module can realize basic functions such as charging pile information entry, modification, and integration. At the same time, the system will detect the status of charging piles at any time. When the charging pile has a communication failure, it will open the offline mode and save the current transaction data when the breakdown occurs independently. The system will keep trying to restore communication with the charging pile. If it recovers communication, the charging pile will report the offline transaction data. If the failure lasts more than 30 min, a repair work order will be generated to start the repair process.

(3)

Funds Management

This function will monitor the whole process of fund transactions (Figure 3). The charging fees will be collected by the application first. Eventually, the program aggregates the detailed amount and its information of each order to generate the corresponding bill. Subsequently, the funds will be settled with the power company and power plant according to a certain percentage, then a final settlement list will be generated.

3.4. Data Flow

3.4.1. User-Side App Data Flow Analysis

The user-side app data flow mainly involves the data information flow between the charging pile, the user side, and the user (Figure 4). The charging pile and the user side mainly transfer order data and price data between them. The user side is responsible for feeding back its charging stake location data according to the retrieval data provided by the user.

A list of charging pile information, route planning scheme, order detail form, and other form information will be generated in the user-side app data flow (Figure 5).

3.4.2. Data Flow Analysis of Operations Management Platform

The data flow on the operations management platform primarily involves data exchange between the power plant, grid, and operations management personnel. The power plant and grid are responsible for delivering the price signal data to the operations management platform, and the platform feeds back the data from the power plant and grid to settle the amount with customers. The backend operations manager is mainly responsible for processing the work information provided by the operations side and feeding the processing result to the backend (Figure 6).

The data transfer process of the operations management platform mainly generates form information such as charging pile price information sheet, charging pile status data, transaction data, and maintenance work order (Figure 7).

3.5. Entity-Relationship Model

The system mainly involves four entities: user, EV, charging pile, and internal management. The specific entity-relationship (E-R) diagram is shown in Figure 8.

Users drive EVs and also enjoy the services of the system administrator. One administrator can serve more than one user, and one user can be served by more than one administrator. The contact type is m:n (m and n indicate the number of individuals in Figure 8, many-to-many). In the system, the user mainly has the attributes of the account number, password, nickname, daily charging location, and real personal information with real name authentication.

EVs are driven by one driver, but one driver can drive many different Evs, so their contact type is 1:n (one-to-many). Evs mainly have model, rated voltage, battery type, battery capacity, license plate model, license plate number, and range attributes.

The charging pile entity can charge the EV while being monitored by the administrator and is in a transactional relationship with the user. Charging piles can find multiple entities corresponding to them in all three, so they all belong to the m:n (many-to-many) connection between them. The main attributes are: geographic coordinates, number, model, adapted model, real-time status, power level, maximum power, output-input voltage, output current, and payment method.

The administrator serves the users and has the responsibility of managing the charging piles. Their main attributes are account number, password, employee number, real name information, and the department they belong to.

3.6. Programming

The program uses Client/Server (C/S), which is a major mainstream technology in today’s web development architecture. The system includes three parts: database, Windows management and operation side, and Android user side. The overall structure of the multi-platform distributed system is shown in Figure 9.

The development of the program involved three different development environments, depending on the architecture required: SQL Server 2012, a comprehensive and integrated data solution, which has the advantages of reliability, flexibility and compatibility, was used for the database. Windows 7 Ultimate was chosen as the operating system platform for the management operation side. NET platform is efficiently supported by the Windows operating system, allowing applications to communicate and share data across platforms via the Internet. Developed in C language, it relies on the powerful .NET Framework hosted code collection classes. Visual Studio 2013 was used as the development environment and includes .NET Framework 4.5, the next generation of .NET platform, which can better support WCF. The Android user side selects Eclipse, a Java IDE that runs under Windows. The system includes plug-ins such as ADT 23.0, android SDK 4.2.2, and external jar packages such as gson-2.2.2.jar, Baidu Map application program interface, etc., to support the user side for data communication and call Baidu map, etc. See File S1 for detailed code.

4. Simulations

The application narrows the real-time electricity price interval to two hours, and each day is divided into 12 time periods. The application example simulates different weather conditions based on two weather factors that affect renewable energy generation: sunshine and wind. For North China, as an example, a full set of price data was set up to test the program designed in Section 3, by combining the average daily electricity generation of wind and PV throughout the year (Figure 10) and the electricity price for residential use at different times by region.

4.1. Predicted Electricity Price for Next Week

A variety of weather scenarios are set up as needed for the application setup as follows. Next week will have cloudy weather overall from Monday to Tuesday, rainy weather on Wednesday and Thursday, and sunny weather overall from Friday to Sunday. According to the notice issued by China’s National Development and Reform Commission on The approval of transmission and distribution prices for provincial power grids for 2020–2022, the average price ($P_{avg}^{TD}$) of 0.26 RMB/kWh is taken as the toll of the transmission and distribution used in the simulation of our study.

The original power purchase price provided by the power plant is entered on the operations management platform and the program processes the price as follows: The raw power purchase price ($P_t^{orig}$) is used to add the electricity transmission and distribution price to generate the electricity price $(P_t^{fml})$ and trend for the entire next week (

Table 2. Forecasted electricity prices for the coming week (RMB).

Weather Conditions	Cloudy and Windy		Cloudy and Windy		Rainy Day with Wind		Rainy Day		Windy on a Sunny Day		No Wind on a Clear Day		Windy On a Sunny Day
Time Interval	Monday		Tuesday		Wednesday		Thursday		Friday		Saturday		Sunday
	PP Price	CUS Price	PP Price	CUS Price	PP Price	CUS Price	PP Price	CUS Price	PP Price	CUS Price	PP Price	CUS Price	PP Price	CUS Price
0–2	0.43	0.69	0.45	0.71	0.40	0.66	0.47	0.73	0.42	0.68	0.92	1.18	0.55	0.81
2–4	0.36	0.62	0.38	0.64	0.38	0.64	0.40	0.66	0.33	0.59	0.88	1.14	0.46	0.72
4–6	0.45	0.71	0.42	0.68	0.36	0.62	0.39	0.65	0.38	0.64	0.85	1.11	0.36	0.62
6–8	0.78	1.04	0.80	1.06	0.52	0.78	0.75	1.01	0.68	0.94	0.70	0.96	0.60	0.86
8–10	0.82	1.08	0.79	1.05	0.67	0.93	0.83	1.09	0.72	0.98	0.67	0.93	0.76	1.02
10–12	0.98	1.24	0.95	1.21	0.85	1.11	0.92	1.18	0.70	0.96	0.54	0.80	0.72	0.98
12–14	1.00	1.26	1.02	1.28	0.95	1.21	1.01	1.27	0.73	0.99	0.48	0.74	0.80	1.06
14–16	0.88	1.14	0.92	1.18	0.83	1.09	0.95	1.21	0.67	0.93	0.56	0.82	0.70	0.96
16–18	0.76	1.02	0.80	1.06	0.72	0.98	0.79	1.05	0.71	0.97	0.72	0.98	0.75	1.01
18–20	0.67	0.93	0.70	0.96	0.82	1.08	0.72	0.98	0.78	1.04	0.85	1.11	0.83	1.09
20–22	0.40	0.66	0.43	0.69	0.87	1.13	0.53	0.79	0.82	1.08	0.90	1.16	0.90	1.16
22–24	0.36	0.62	0.42	0.68	0.60	0.86	0.50	0.76	0.70	0.96	1.00	1.26	0.73	0.99

Power Plant (PP), Customer (CUS).

).

$$P_t^{fml}=P_t^{orig}+P_{avg}^{TD}\hspace{1em}\hspace{1em}\forall t\in T$$

(1)

The program performs the processing on the original electricity price entered. Then the electricity price for the next week is presented on the customer side.

4.2. Real-Time Electricity Price Display for the Week

The real-time price of electricity for each day of the week is inputted directly through the electricity price interface directly connected to the power plant, left on the operations management side and displayed on the user side of the program based on the change of real-time power generation (

Table 3. Electricity prices for the week (RMB).

Weather Conditions	No Wind on Cloudy Days		No Wind on Cloudy Days		Rainy Day with Wind		Rainy Day		No Wind on a Clear Day		Windy on a Sunny Day		Cloudy and Windy
Time Interval	Monday		Tuesday		Wednesday		Thursday		Friday		Saturday		Sunday
	PP Price	CUS Price	PP Price	CUS Price	PP Price	CUS Price	PP Price	CUS Price	PP Price	CUS Price	PP Price	CUS Price	PP Price	CUS Price
0–2	0.42	0.68	0.42	0.68	0.45	0.71	0.49	0.75	0.89	1.15	0.56	0.82	0.50	0.76
2–4	0.40	0.66	0.39	0.65	0.42	0.68	0.45	0.71	0.86	1.12	0.50	0.76	0.44	0.70
4–6	0.41	0.67	0.39	0.65	0.39	0.65	0.43	0.69	0.82	1.08	0.48	0.74	0.46	0.72
6–8	0.57	0.83	0.55	0.81	0.56	0.82	0.70	0.96	0.72	0.98	0.52	0.78	0.79	1.05
8–10	0.65	0.91	0.67	0.93	0.68	0.94	0.80	1.06	0.68	0.94	0.68	0.94	0.83	1.09
10–12	0.78	1.04	0.80	1.06	0.86	1.12	0.93	1.19	0.60	0.86	0.70	0.96	0.89	1.15
12–14	0.98	1.24	1.01	1.27	0.99	1.25	0.98	1.24	0.55	0.81	0.78	1.04	0.97	1.23
14–16	0.88	1.14	0.92	1.18	0.87	1.13	1.00	1.26	0.67	0.93	0.72	0.98	0.90	1.16
16–18	0.70	0.96	0.78	1.04	0.79	1.05	0.87	1.13	0.70	0.96	0.75	1.01	0.84	1.10
18–20	0.92	1.18	0.89	1.15	0.85	1.11	0.80	1.06	0.78	1.04	0.90	1.16	0.75	1.01
20–22	0.95	1.21	0.99	1.25	0.77	1.03	0.67	0.93	0.85	1.11	0.92	1.18	0.56	0.82
22–24	0.72	0.98	0.75	1.01	0.60	0.86	0.52	0.78	0.82	1.08	0.80	1.06	0.48	0.74

Power Plant (PP), Customer (CUS).

).

The three most representative weather conditions (sunny day without wind, sunny day with wind, and cloudy day without wind) are selected separately and displayed on the user side (Figure 11).

To fit the currently adopted tiered prices for large industries with the real-time price obtained through the application, the most representative three weather conditions are still used: sunny day without wind, sunny day with wind, and cloudy day without wind (Figure 12).

It can be seen that under different weather conditions, influenced by solar and wind energy factors, the electricity price simulated by the system is significantly different from the existing conventional tiered electricity price. It is possible to use the price advantage to guide the charging behavior of EV consumers.

5. Conclusions

Demand-side management through charging price mechanisms for guiding EV charging has become a significant policy instrument. The application designed in this paper is dedicated to the real-time presentation of electricity prices. The data interface is directly connected to wind and PV power plants because the vigorous development of PV and wind power generation will gradually replace thermal power. The system acts as a bridge between the user and the power plant, and the power plant directly specifies the price according to the amount of current power generation, which is displayed on the user side.

When designing this program, considering the integrity of the application function, the program was divided into two parts according to the different audiences: the user-side app and the operations management platform. With reference to the functions of existing charging service apps on the market, combined with its own characteristics and fully considered user needs, the basic functions of the program were finally determined. According to the basic functions, the detailed business processes and data flow chart were made for detailed analysis. The relationship between each entity and attribute is also clarified through E-R diagram.

This study takes the average kilowatt-hour price of electricity from six power grids in North China as the overcharge. Combined with the average daily electricity generation of wind and PV power throughout the year, and the electricity price for large industries, the data for electricity prices under 12 different weather conditions in a week are simulated and set. These data were entered into the system for testing and the designed application proved to run successfully. The experiments obtained the expected results.

Although there is no shortage of big brands with a large number of users in the charging application field, there is no relatively mature program that can use the price mechanism to guide the charging behavior of EVs. A more detailed time price division to make the otherwise disordered charging time orderly is the advantage of the program in this paper. While significantly reducing the cost of storing and transporting electricity, the forecast time for electricity prices has been reduced to one week.

The application built in this study follows the trends in development of the new energy era. Combined with the dynamic charging rate model, our app can guide EV consumers to charge in an orderly manner and can better promote the transformation of the energy structure of the power system.

6. Limitations

The research in this paper has three limitations. Firstly, the functionality of the application needs to be further improved. The current application is only at the primary stage of being able to handle price data and needs to be fully tested for system stability when it is put onto the market. The current database capacity can only accommodate the TOU price data of the pilot project in Northern China, and the database system needs to be expanded and maintained for a larger market and wider application. Secondly, this paper only considers the impact of renewable energy generation on the power system due to the influence of weather factors. Other stochastic factors, such as large-scale electricity consumption during holidays and large-scale electricity consumption under the influence of extreme weather, are not included in the programming presented in this paper. In addition, the application designed in this paper is structurally dependent on the power system being able to provide real-time and forecasted cost of electricity generation. However, China’s domestic electricity pricing system is still in the process of reform, so access to power system data could be a potential obstacle to our program.

The above factors warrant further improvement in subsequent studies. In the future, it is also necessary to consider how to optimize the algorithm to improve the response speed in extreme weather or under large-scale concentrated charging demand scenarios for EVs. In addition, if we can improve the storage and processing capacity of historical electricity prices and weather data, the constraints of power plant data on our app will be greatly reduced.