Community electric vehicle orderly charging method

Abstract

The application provides a community electric vehicle orderly charging method, and the main steps are as follows: 1) a system structure analysis is carried out for a light storage and charging integrated community, and electric energy flow is summarized; 2) the demand of a power grid company and an electric vehicle user is analyzed, and a double-layer multi-objective optimization model taking the community load peak-valley difference and the minimum charging cost of the user as the optimization objectives is constructed; and 3) a mouse swarm optimization algorithm is used to solve the optimization model, and the optimal charging plan is output. The application has good universality and applicability, and can meet the charging demand of the user and guarantee the safe, economic and stable operation of the power distribution network under the condition that the transformer of the old community is not increased in capacity and is not expanded in capacity.

Description

Technical Field

[0001] This invention relates to the field of smart power distribution networks, specifically a method for orderly charging of electric vehicles in a community. Background Technology

[0002] In recent years, with the rapid development of the global economy, a large amount of fossil fuels have been extracted and used, causing environmental pollution. Electric vehicles, on the other hand, have developed rapidly due to their advantages such as being environmentally friendly and low-carbon. The increase in the number of electric vehicles can effectively reduce the use of traditional energy sources, but the large-scale connection to the power grid will inevitably bring many impacts, such as exacerbating load fluctuations, increasing the peak-to-valley load difference, and reducing the lifespan of power grid equipment.

[0003] Currently, relevant research has been conducted both domestically and internationally on orderly charging methods for electric vehicles, but the following problems still exist: the optimization objective is singular, lacking consideration for the interests of users, the power grid, and other parties; and the inclusion of distributed energy and energy storage devices has not been considered to improve the enthusiasm of user response methods and the local consumption of distributed energy.

[0004] The addition of distributed energy and energy storage devices provides a new research approach for achieving orderly charging of electric vehicles in communities. By analyzing the system structure of an integrated photovoltaic-storage-charging community, a two-layer, multi-objective orderly charging optimization model is constructed. The Rat Swarm Optimizer (RSO) algorithm is used to solve the model, obtaining the optimal charging plan. This aims to reduce the peak-valley load difference in the community, decrease user charging costs, and simultaneously ensure the safe, economical, and stable operation of the power distribution network. Summary of the Invention

[0005] The purpose of this invention is to solve the problems existing in the prior art.

[0006] To achieve the objectives of this invention, a method for orderly charging of electric vehicles in a community is proposed, which mainly includes the following steps:

[0007] 1) Conduct system structure analysis for integrated photovoltaic-storage-charging communities, and summarize the power flow, such as... Figure 1 As shown in the diagram, the arrows indicate the direction of electrical energy flow. Photovoltaic units and the grid provide electricity, while charging piles and conventional loads consume it. The energy storage unit is unique; it can both provide and consume electricity. To simplify the charging model, the energy storage unit is considered a load consuming electricity. In the calculation, a positive value indicates charging, and a negative value indicates discharging. When the photovoltaic output exceeds the charging load, the photovoltaic units first supply power to the charging piles, then to the energy storage units, and any surplus flows to the conventional loads. When the photovoltaic output is less than the charging load, the energy storage units supply power to the charging piles, and the difference in charging load is finally supplied by the grid.

[0008] 2) Construct a two-layer multi-objective optimization model that minimizes the peak-valley difference of community load and user charging costs. The first layer is the power grid layer, which takes reducing the peak-valley difference of community load as the optimization objective; the second layer is the user layer, which takes reducing user charging costs as the optimization objective, and uses the optimization results of the power grid layer as the constraint condition of the optimization model of this layer.

[0009] 2.1) In the power grid layer optimization model, the objective function and constraints are as follows:

[0010] 2.1.1) The optimization objective of the power grid layer is to reduce the peak-valley load difference in the community. The objective function is:

[0011]

[0012] In the formula: That is the total load.

[0013] 2.1.2) The constraints at the grid layer mainly include power balance constraints, total load limitation constraints, user charging demand constraints, and energy storage unit constraints, as detailed below:

[0014] Power balance constraints:

[0015]

[0016] In the formula: It refers to the photovoltaic power output. It is the charging load; It is a normal load; It is the charging and discharging power of the energy storage unit.

[0017] Total load limit constraints:

[0018]

[0019] In the formula: It refers to the maximum active power, meaning that the total load of the community cannot exceed the maximum active power of the community transformer.

[0020] User charging demand constraints are divided into charging time constraints and battery capacity constraints:

[0021]

[0022]

[0023] In the formula: It is the shortest time to fully charge an electric vehicle, that is, the time required to charge it at maximum power. It is the required charging time to fully charge; It is the longest time, that is, the time between when the car owner returns home and when they leave home; It is the state of charge of the battery when an electric vehicle begins charging. It is the state of charge of the battery when the electric vehicle finishes charging.

[0024] Energy storage unit constraints are divided into energy storage unit charge / discharge power constraints and energy storage unit capacity constraints:

[0025]

[0026]

[0027] In the formula: It is the maximum charging and discharging power; This is the maximum capacity; This is the actual capacity of the energy storage unit.

[0028] 3) The electric vehicle two-layer multi-objective ordered charging optimization model has many optimization objects and multiple constraints, which are difficult to solve using traditional methods. Therefore, this method uses the mouse swarm optimization algorithm to solve the problem.

[0029] 3.1) The rat swarm optimization algorithm simulates the predation behavior of a rat swarm. During the chase, it is assumed that the best individual in the swarm knows the location of the prey. Other individuals in the swarm can then update their own positions based on the best individual's location. The update strategy is as follows:

[0030]

[0031]

[0032]

[0033] In the formula:

[0034] Is the prey for the first From a mouse's perspective, this is its position; It is the first A mouse; It is the best individual in the rat colony; It is the first The position of the mouse in the next iteration; This is the current iteration number; It is a random number between 0 and 2; It is a random number between 1 and 5.

[0035] The mouse swarm optimization algorithm simulates the behavior of a mouse swarm chasing and attacking prey, and adjusts the parameters accordingly. and The process involves moving the mouse to different locations to complete a search of the search space, as follows: Figure 2 As shown, the initialization parameters are the time when the electric vehicle starts charging and the charging and discharging power of the energy storage unit.

[0036] 3.2) This method uses a rat swarm optimization algorithm to solve the optimization model and obtain the optimal charging plan, which includes the start time of electric vehicle charging and the charging and discharging power of the energy storage unit. The process is as follows: Figure 3 As shown.

[0037] 3.2.1) First, obtain the community's regular load and photovoltaic output forecast data for the next 24 hours, and dynamically obtain user charging information, including start charging time, end charging time, and charging amount;

[0038] 3.2.2) When a new vehicle connects or a user changes their charging information during a certain period, the grid layer optimization model uses the mouse swarm optimization algorithm to solve the problem and outputs the user's start charging time, the photovoltaic charging and discharging power, and the charging load.

[0039] 3.2.3) The charging load output by the grid layer is used as a constraint condition for the user layer optimization model. The user layer optimization model is solved using the mouse swarm optimization algorithm, and outputs the user's start charging time and the charging and discharging power of the photovoltaic system.

[0040] 3.2.4) Repeat steps 3.2.2) and 3.2.3) until the maximum number of iterations is reached, and output a new charging plan;

[0041] 3.2.5) If no new vehicle is connected or the user's charging information changes, the charging plan of the previous period will be followed;

[0042] 3.2.6) Repeat steps 3.2.2), 3.2.3), 3.2.4) and 3.2.5) until the optimization time period reaches the maximum number of time periods. Attached Figure Description

[0043] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which:

[0044] Figure 1 Structure diagram of an integrated photovoltaic, energy storage, and charging community system;

[0045] Figure 2 is a flowchart of the mouse swarm optimization algorithm;

[0046] Figure 3 is a flowchart of the two-layer multi-objective ordered charging method based on the mouse swarm optimization algorithm; Detailed Implementation

[0047] The present invention will be further described below with reference to embodiments, but it should not be construed that the scope of the present invention is limited to the following embodiments. Various substitutions and modifications made based on ordinary technical knowledge and common practices in the art without departing from the above-described technical concept of the present invention should be included within the scope of protection of the present invention.

[0048] 1) Taking a photovoltaic-storage-charging integrated community as the research object, conduct system structure analysis and summarize the power flow. For example... Figure 1 As shown, the arrows indicate the direction of electrical energy flow. Photovoltaic units and the grid provide electricity, while charging piles and conventional loads consume it. The energy storage unit is unique; it can both provide and consume electricity. To simplify the charging model, the energy storage unit is considered a load consuming electricity. In the calculation, a positive value indicates charging, and a negative value indicates discharging. When the photovoltaic output exceeds the charging load, the photovoltaic units first supply power to the charging piles, then to the energy storage units, and any surplus flows to the conventional loads. When the photovoltaic output is less than the charging load, the energy storage units supply power to the charging piles, and the difference in charging load is finally supplied by the grid.

[0049] 2) Construct a two-layer multi-objective optimization model that minimizes the peak-valley difference of community load and user charging costs. The first layer is the power grid layer, which takes reducing the peak-valley difference of community load as the optimization objective; the second layer is the user layer, which takes reducing user charging costs as the optimization objective, and uses the optimization results of the power grid layer as the constraint condition of the optimization model of this layer.

[0050] 2.1) In the power grid layer optimization model, the objective function and constraints are as follows:

[0051] 2.1.1) The optimization objective of the power grid layer is to reduce the peak-valley load difference in the community. The objective function is:

[0052] (11)

[0053] In the formula: That is the total load.

[0054] 2.1.2) The constraints at the grid layer mainly include power balance constraints, total load limitation constraints, user charging demand constraints, and energy storage unit constraints, as detailed below:

[0055] Power balance constraints:

[0056] (12)

[0057] In the formula: It refers to the photovoltaic power output. It is the charging load; It is a normal load; It is the charging and discharging power of the energy storage unit.

[0058] Total load limit constraints:

[0059] (13)

[0060] In the formula: It refers to the maximum active power, meaning that the total load of the community cannot exceed the maximum active power of the community transformer.

[0061] User charging demand constraints are divided into charging time constraints and battery capacity constraints:

[0062] (14)

[0063] (15)

[0064] In the formula: It is the shortest time to fully charge an electric vehicle, that is, the time required to charge it at maximum power. It is the required charging time to fully charge; It is the longest time, that is, the time between when the car owner returns home and when they leave home; It is the state of charge of the battery when an electric vehicle begins charging. It is the state of charge of the battery when the electric vehicle finishes charging.

[0065] Energy storage unit constraints are divided into energy storage unit charge / discharge power constraints and energy storage unit capacity constraints:

[0066] (16)

[0067] (17)

[0068] In the formula: It is the maximum charging and discharging power; This is the maximum capacity; This is the actual capacity of the energy storage unit.

[0069] 3) The electric vehicle two-layer multi-objective ordered charging optimization model has many optimization objects and multiple constraints, which are difficult to solve using traditional methods. Therefore, this method uses the mouse swarm optimization algorithm to solve the problem.

[0070] 3.1) The rat swarm optimization algorithm simulates the predation behavior of a rat swarm. During the chase, it is assumed that the best individual in the swarm knows the location of the prey. Other individuals in the swarm can then update their own positions based on the best individual's location. The update strategy is as follows:

[0071] (18)

[0072] (19)

[0073] (20)

[0074] In the formula:

[0075] It is the position of the prey relative to the i-th mouse; It is the i-th mouse; It is the best individual in the rat colony; It is the position of the i-th mouse in the next iteration; This is the current iteration number; It is a random number between 0 and 2; It is a random number between 1 and 5.

[0076] The mouse swarm optimization algorithm simulates the behavior of a mouse swarm chasing and attacking prey, and adjusts the parameters accordingly. and The process involves moving the mouse to different locations to complete a search of the search space, as follows: Figure 2 As shown, the initialization parameters are the time when the electric vehicle starts charging and the charging and discharging power of the energy storage unit.

[0077] 3.2) This method uses a rat swarm optimization algorithm to solve the optimization model and obtain the optimal charging plan, which includes the start time of electric vehicle charging and the charging and discharging power of the energy storage unit. The process is as follows: Figure 3 As shown.

[0078] 3.2.1) First, obtain the community's regular load and photovoltaic output forecast data for the next 24 hours, and dynamically obtain user charging information, including start charging time, end charging time, and charging amount;

[0079] 3.2.2) When a new vehicle connects or a user changes their charging information during a certain period, the grid layer optimization model uses the mouse swarm optimization algorithm to solve the problem and outputs the user's start charging time, the photovoltaic charging and discharging power, and the charging load.

[0080] 3.2.3) The charging load output by the grid layer is used as a constraint condition for the user layer optimization model. The user layer optimization model is solved using the mouse swarm optimization algorithm, and outputs the user's start charging time and the charging and discharging power of the photovoltaic system.

[0081] 3.2.4) Repeat steps 3.2.2) and 3.2.3) until the maximum number of iterations is reached, and output a new charging plan;

[0082] 3.2.5) If no new vehicle is connected or the user's charging information changes, the charging plan of the previous period will be followed;

[0083] 3.2.6) Repeat steps 3.2.2), 3.2.3), 3.2.4) and 3.2.5) until the optimization time period reaches the maximum number of time periods.

[0084] 4) Using the above methods, experiments were conducted on four charging scenarios: disordered charging in integrated photovoltaic-storage-charging communities, orderly charging at the grid level, orderly charging at the user level, and orderly charging at two levels with multiple objectives. The experimental results were obtained.

[0085] 4.1) A comparative analysis of the community load situation when electric vehicles in the community are charged in a disorderly and orderly manner is conducted. The specific data is shown in Table 1.

[0086] Table 1 Community Load Data

[0087] Disorderly charging in integrated photovoltaic, energy storage and charging communities 925.66 444.16 481.5 52.02% Orderly charging at the grid level 827.25 569.09 258.16 31.21% orderly charging at the user level 856.67 513.44 343.23 40.07% Double-layer multi-target ordered charging 827.25 540.64 286.61 34.65%

[0088] 4.2) A comparative analysis of the charging costs of electric vehicles in the community using disordered and ordered charging methods was conducted, and the specific data is shown in Table 2.

[0089] Table 2 Community Charging Costs

[0090] Disorderly charging in integrated photovoltaic, energy storage and charging communities 1340.7 995.37 0.76 Orderly charging at the grid level 1340.7 523.42 0.39 orderly charging at the user level 1340.7 444.69 0.33 Double-layer multi-target ordered charging 1340.7 479.96 0.36

[0091] 4.3) As can be seen from Tables 1 and 2, compared with disordered charging, the proposed method can reduce the peak-valley difference of community load by 40.47% and the average charging price by 52.63%. Compared with the single-layer ordered charging method, this method has obvious advantages in comprehensive effect, ensuring the safe and stable operation of the distribution network while taking into account the economic interests of electric vehicle users.

[0092] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims

1. A community electric vehicle orderly charging method, characterized in that, The main steps include: 1) Conduct system structure analysis for integrated photovoltaic-storage-charging communities and summarize the power flow; 2) Analyze the needs of power grid companies and electric vehicle users, and construct a two-level multi-objective optimization model that minimizes the peak-valley difference of community load and the user charging cost; 3) The mouse swarm optimization algorithm is used to solve the optimization model and output the optimal charging plan; 4) The main steps in solving the optimization model are as follows: Step S41, the rat swarm optimization algorithm, simulates the predation behavior of a rat swarm. During the chase, it assumes the optimal individual in the swarm knows the prey's location, and other individuals update their positions based on this optimal individual's location. The update strategy is as follows: (1) (2) (3) In the formula: It is the position of the prey relative to the i-th mouse; It is the i-th mouse; It is the optimal individual in the rat colony; It is the position of the i-th mouse in the next iteration; This is the current iteration number; It is a random number between 0 and 2; It is a random number between 1 and 5; The mouse swarm optimization algorithm simulates the behavior of a mouse swarm chasing and attacking prey, and adjusts the parameters accordingly. and The mouse is moved to different locations to complete the search of the search space, where the initialization parameters are the electric vehicle's charging start time and the energy storage unit's charging and discharging power. Step S42 This method uses the mouse swarm optimization algorithm to solve the optimization model and obtain the optimal charging plan, which includes the start time of electric vehicle charging and the charging and discharging power of energy storage unit. Step S421 First, obtain the community's regular load and photovoltaic output forecast data for the next 24 hours, and dynamically obtain user charging information, including start charging time, end charging time, and charging amount; Step S422 When a new vehicle connects or a user changes their charging information during a certain period, the grid layer optimization model uses the mouse swarm optimization algorithm to solve the problem and outputs the user's start charging time, the photovoltaic charging and discharging power, and the charging load. In step S423, the charging load output by the grid layer is used as a constraint condition for the user layer optimization model. The user layer optimization model is solved using the mouse swarm optimization algorithm, and the output is the user's start charging time and the charging and discharging power of the photovoltaic system. Step S424 repeats steps S422 and S423 until the maximum number of iterations is reached, and outputs a new charging plan; If no new vehicle is connected or the user's charging information changes in step S425, the charging plan of the previous period will be followed. Step S426 repeats steps S422, S423, S424 and S425 until the optimization time period reaches the maximum number of time periods.

2. The method for orderly charging of electric vehicles in a community according to claim 1, characterized in that, System structure analysis is conducted for integrated photovoltaic-storage-charging communities. Photovoltaic units and the power grid provide electricity, while charging piles and conventional loads consume electricity. The energy storage unit is unique in that it can both provide and consume electricity. To simplify the charging model, the energy storage unit is treated as a load consuming electricity. In the calculation, if the value is positive, it is in a charging state; otherwise, it is in a discharging state. When the photovoltaic output is greater than the charging load, the photovoltaic unit first supplies power to the charging piles, then supplies power to the energy storage unit, and any surplus flows to the conventional loads. When the photovoltaic output is less than the charging load, the energy storage unit supplies power to the charging piles, and the difference in charging load is finally supplied by the power grid.

3. A method for orderly charging of electric vehicles in a community according to claim 1 or claim 2, characterized in that, A two-layer multi-objective optimization model is constructed with the goal of minimizing the peak-valley difference of community load and the charging cost for users. The first layer is the power grid layer, which takes reducing the peak-valley difference of community load as the optimization objective. The second layer is the user layer, which takes reducing user charging costs as the optimization objective and uses the optimization results of the power grid layer as the constraints of the optimization model in this layer. The main steps are as follows: In step S31, the objective function and constraints in the power grid optimization model are as follows: Step S311: The optimization objective of the power grid layer is to reduce the peak-valley load difference in the community. The objective function is: (4) In the formula: It is the total load; Step S312, the constraints of the power grid layer, include power balance constraints, total load limit constraints, user charging demand constraints, and energy storage unit constraints, as detailed below: Power balance constraints: (5) In the formula: It refers to the photovoltaic power output. It is the charging load; It is a normal load; It is the charging and discharging power of the energy storage unit; Total load limit constraints: (6) In the formula: It refers to the maximum active power, meaning that the total load of the community cannot exceed the maximum active power of the community transformer. User charging demand constraints are divided into charging time constraints and battery capacity constraints: (7) (8) In the formula: It is the shortest time to fully charge an electric vehicle, that is, the time required to charge it at maximum power. It is the required charging time to fully charge; It is the longest time, that is, the time between when the car owner returns home and when they leave home; It is the state of charge of the battery when an electric vehicle begins charging. It is the state of charge of the battery when the electric vehicle finishes charging; Energy storage unit constraints are divided into energy storage unit charge / discharge power constraints and energy storage unit capacity constraints: (9) (10) In the formula: This is the maximum charging and discharging power; This is the maximum capacity; This is the actual capacity of the energy storage unit.

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