A collaborative optimization configuration method for self-consistent multi-microgrid systems on highways considering electric vehicle charging loads

Abstract

The present invention discloses a method for collaborative optimization configuration of a highway self-consistent multi-microgrid system taking into account the charging load of electric vehicles, comprising: S1, analyzing the highway self-consistent multi-microgrid system to obtain the topological structure of the multi-microgrid system; S2, analyzing the power characteristics of the multi-microgrid system according to the topological structure to obtain a first characteristic set, including photovoltaic power characteristics, wind power characteristics, energy storage charging and discharging characteristics, and electric vehicle charging load characteristics; S3, dividing the sub-microgrids in the multi-microgrid system according to the topological structure and the first characteristic set to obtain a service area sub-microgrid and a tunnel sub-microgrid; the loads in the service area sub-microgrid and the tunnel sub-microgrid are divided according to their importance; S4, obtaining a control strategy of the multi-microgrid system according to the topological structure, the first characteristic set, the service area sub-microgrid, and the tunnel sub-microgrid; constructing a first model based on the control strategy in consideration of the objective function and the constraint conditions; S5, solving the first model to obtain a collaborative optimization configuration scheme of the multi-microgrid system.

Description

Technical Field

[0001] The present invention relates to the field of transportation energy multi-microgrid systems, and in particular to a method for collaborative optimization configuration of a highway self-consistent multi-microgrid system taking into account electric vehicle charging loads. Background Art

[0002] As the process of social modernization continues to advance, problems such as resource shortages, climate change and environmental pollution have brought severe challenges to mankind. As the most important basic industries of the social and economic system and a scientific and technological field with the characteristics of a master, energy and transportation are in urgent need of major changes to lead and shape the economic development model. Promoting the interconnection between the road transportation system and the energy system is the key to achieving the full integration and coordinated development of road transportation and energy.

[0003] At present, domestic and foreign scholars have conducted relevant research on the green energy system of highway transportation. Yang Penghao, Chen Shixuan and others analyzed the current status of research on photovoltaic power generation on highway slopes and discussed the development trend in this field; Yu Zhenwei analyzed the power load of highway service areas and tunnels, and then proposed a highway microgrid wind, light and storage optimization configuration model; Xiang Yue, Liu Junyong and others considered the economic dispatch problem under the scenario with the maximum interaction cost between microgrid and distribution network, built a min-max robust optimization model, and used the scenario generation method to convert it into a single-layer optimization problem for solution; Liu Yixin, Guo Li and others established a two-stage robust optimization model of min-max-min structure for the uncertainty of renewable energy and load in microgrids, and obtained the dispatching scheme with the lowest operating cost under the worst scenario. However, the existing microgrid optimization schemes mainly focus on the resources on the power supply side, and the potential of demand-side resources is not explored and paid enough attention; at the same time, the existing research on highway micro-energy systems is mostly aimed at a single microgrid structure, and fails to fully consider the problem of coordinated optimization of multi-microgrid systems.

[0004] Therefore, a new technical solution is urgently needed to solve the technical problem of how to achieve coordinated optimization configuration of self-consistent multi-microgrid systems on highways while fully considering the resources on the power side and the demand side. Summary of the invention

[0005] The present invention provides a method for collaborative optimization configuration of a highway self-consistent multi-microgrid system taking into account the charging load of electric vehicles, so as to solve the technical problem of how to achieve collaborative optimization configuration of a highway self-consistent multi-microgrid system while fully considering the resources on the power supply side and the demand side.

[0006] To achieve the above object, the present invention provides a method for collaborative optimization configuration of a highway self-consistent multi-microgrid system taking into account electric vehicle charging load, comprising the following steps:

[0007] S1. Analyze the self-consistent multi-microgrid system of highway and obtain the topological structure of the multi-microgrid system.

[0008] S2. Analyze the power characteristics of the multi-microgrid system according to the topological structure to obtain a first characteristic set; the first characteristic set includes photovoltaic power characteristics, wind power characteristics, energy storage charging and discharging characteristics, and electric vehicle charging load characteristics.

[0009] S3. According to the topological structure and the first characteristic set, the sub-microgrids in the multi-microgrid system are pre-divided into service area sub-microgrids and tunnel sub-microgrids; the loads in the service area sub-microgrid and the tunnel sub-microgrid are divided according to the importance.

[0010] S4. Obtain a control strategy for the multi-microgrid system according to the topological structure, the first characteristic set, the service area sub-microgrid, and the tunnel sub-microgrid; and construct a first model by considering the control strategy simultaneously according to a preset objective function and preset constraints.

[0011] S5. Solve the first model, obtain the coordinated optimization configuration plan of the multi-microgrid system, and complete the optimization configuration.

[0012] Preferably, S3 includes:

[0013] The sub-microgrids in the multi-microgrid system are classified into service area sub-microgrids and tunnel sub-microgrids. The service area sub-microgrid and tunnel sub-microgrid are powered by wind and solar renewable energy on the source side and store energy in batteries on the storage side. The load side is divided according to the characteristics and level of the actual load; the actual load includes electric vehicle charging load, road traffic system infrastructure and service facilities; electric power can be transmitted between each sub-microgrid.

[0014] Each sub-microgrid has an energy trading channel with the electricity market. The electricity market is used to provide a preset limit of power deficiency compensation for each sub-microgrid in the multi-microgrid system under the constraints of voltage level transmission distance and capacity.

[0015] Preferably, S3 further includes:

[0016] The loads in the service area sub-microgrid and the tunnel sub-microgrid are divided into importance levels according to preset conditions, including:

[0017] According to preset conditions, the loads in the service area sub-microgrid and the tunnel sub-microgrid are divided into three categories: important load, transferable load and interruptible load. Among them, the important load is the first in importance, the transferable load is the second in importance, and the interruptible load is the third in importance.

[0018] Preferably, the photovoltaic power characteristics include:

[0019] The photovoltaic power generation model can be expressed by the following relationship:

[0020] in, Output power for photovoltaic power generation; is the power derating factor of the photovoltaic power generation system; is the rated power of the photovoltaic power generation system; is the actual light intensity; is the light intensity under standard test conditions; is the power temperature coefficient; is the actual surface temperature; The temperature is the standard test condition.

[0021] Preferably, the wind power characteristics include:

[0022] The wind power generation model can be expressed by the following relationship:

[0023] in, and are the shape and size parameters of the fan respectively; is the rated output power of the wind turbine; The real-time wind speed when the fan is working; is the rated wind speed; is the cut-in wind speed; To cut out the wind speed; Specific parameters for the job; is the rated power of wind power.

[0024] Preferably, the energy storage charge and discharge characteristics include:

[0025] The battery energy storage model can be expressed by the following relationship:

[0026] in, The maximum allowable charging power of the battery; is the maximum allowable discharge power of the battery; The maximum allowable charging power of the battery at the initial moment; It is the maximum allowable charging power under the constraint of the maximum charging current of the battery; The maximum allowable charging power under the maximum charging rate constraint of the battery; The maximum allowable discharge power of the battery at the initial moment; The battery charging efficiency; is the battery discharge efficiency.

[0027] Preferably, the electric vehicle charging load characteristics include:

[0028] Monte Carlo method is used to simulate the charging load of electric vehicles:

[0029] Daily mileage of electric vehicles from highway entrance to charging station in service area Obey the lognormal distribution, probability density function It can be expressed by the following relationship:

[0030] in, , They are The mean and standard deviation of .

[0031] Initial charge state of electric vehicles entering highways Obey uniform distribution, probability density function It can be expressed by the following relationship:

[0032] in, They are lower and upper limits.

[0033] The state of charge of an electric vehicle when it arrives at a charging station It can be expressed by the following relationship:

[0034] in, is the battery capacity of the electric vehicle; It is the power consumption of electric vehicles per 100 kilometers.

[0035] The time when electric vehicles start charging at the station Obeys normal distribution, probability density function It can be expressed by the following relationship:

[0036] in, , They are The mean and standard deviation of .

[0037] according to The charging time of electric vehicles can be calculated for:

[0038] in, is the rated charging power of the charging pile; Charging efficiency for electric vehicles.

[0039] Charging end time for: Therefore, the charging power requirement of each electric vehicle is:

[0040] The Monte Carlo method is used for simulation. After multiple simulations, the average value is obtained. That is the charging load characteristics of electric vehicles on highways:

[0041] in, is the number of simulations; is the number of electric vehicles; is the total number of simulations; is the total number of electric vehicles, i.e., the traffic volume.

[0042] Preferably, the control strategy includes:

[0043] A1. Determine whether the photovoltaic and wind power outputs in the multi-microgrid system are greater than the load in the multi-microgrid system.

[0044] If the output of photovoltaic and wind power is greater than the load in the multi-microgrid system, each sub-microgrid determines the state of the energy storage capacity and whether there is any surplus power. If so, it sells electricity to the power market; if not, it enters A6.

[0045] If the output of photovoltaic and wind power is greater than the load in the multi-microgrid system, the process goes to A2.

[0046] A2: Determine whether power interaction is possible between the sub-microgrids within the multi-microgrid system; if so, proceed to A3; if not, each sub-microgrid determines the state of the energy storage capacity and performs energy dispatch according to the importance of the load, and proceeds to A5.

[0047] A3. Determine whether the remaining power of each sub-microgrid can meet the important load of the multi-microgrid system; if so, proceed to A6; if not, proceed to A4.

[0048] A4. Each sub-microgrid determines the energy storage capacity status and enters A5.

[0049] A5. Determine whether power balance is achieved between each microgrid. If so, proceed to A6; if not, proceed to A7.

[0050] A6. Determine whether it is necessary to purchase and sell electricity based on the time-of-use electricity price, and purchase and sell electricity adaptively.

[0051] A7. Purchase electricity from the electricity market.

[0052] Preferably, the preset objective function includes:

[0053] The optimization goal is to minimize the total cost of the multi-microgrid system. The preset objective function can be expressed by the following relationship:

[0054] in, For the Daily investment cost of energy storage system of each microgrid; is the trading session in hours; is the total number of trading sessions per day; is the number of microgrids in the system; for The interaction cost between multiple microgrids in a time period, in yuan; is the transaction cost of each microgrid power market, in yuan; For the The operating cost of each microgrid and micro-power source, in yuan; and Respectively The discomfort cost caused by the transferable load and the compensation cost of the interruptible load in each microgrid, in yuan.

[0055] The daily investment cost of energy storage system includes:

[0056] in, For the The planned capacity of the battery, The investment cost of the battery per unit rated power, in yuan; is the battery life, in days; is the funding discount rate.

[0057] The interaction costs between multiple microgrids include:

[0058] in, For micro-grid i The interactive transmission cost between microgrids is in yuan; For Microgrid and Microgrid Unit power transmission cost coefficient, unit is yuan\kW; for Time Microgrid and Microgrid The amount of electricity traded, in kW.

[0059] Electricity market transaction costs include:

[0060] in, and They are The price of electricity purchase and sale in the electricity market during the period, in yuan; and They are Time Microgrid The amount of electricity purchased and sold from the electricity market, in kW.

[0061] Micro power supply operating costs include:

[0062] in, for Time period The wind power output of each microgrid, in kW; for Time period The photovoltaic output of each microgrid, in kW; for Time period The battery storage output of each microgrid, in kW; It is the operation and maintenance cost per unit power of the micro power source, in Yuan\kW.

[0063] The discomfort costs of shiftable loads and the compensation costs of interruptible loads include:

[0064] in, and is the user discomfort coefficient; For the Indoor temperature of users in each microgrid; For the The most comfortable temperature for users in each microgrid; For the Interruptible load value in each microgrid; For the The load benchmark value within each microgrid.

[0065] Preferably, the preset constraints include:

[0066] (1) Interaction constraints among multiple microgrids

[0067] in, express Moment by Ziwei Network Flow to sub-microgrid of electrical energy; express Moment by Ziwei Network Flow to sub-microgrid of electrical energy.

[0068] (2) Micro power output constraints

[0069] in, , and They are During the period, the sub-micro network The actual output power of photovoltaic, wind power and battery energy storage in the house, in kW; , and They are the maximum output power of photovoltaic, wind power and battery energy storage in the sub-microgrid, respectively, in kW.

[0070] (3) Power balance constraints

[0071] in, express Time period Individual microgrid infrastructure loads; express Time period The transferable load value within each microgrid.

[0072] (4) EV charging station constraints

[0073] in, The maximum power of EV charging load; is the charging power margin; The maximum permitted construction capacity for EV charging stations; The load power for EV charging.

[0074] The present invention has the following beneficial effects:

[0075] The method for collaborative optimization configuration of a highway self-consistent multi-microgrid system taking into account the charging load of electric vehicles of the present invention adopts a microgrid power supply mode, and collaboratively plans distributed photovoltaic, wind power, electric vehicle charging loads, energy storage devices and controllable loads within a certain range, fully considering the power supply side and demand side resources, to form a power network that can operate in parallel with the large power grid and can operate independently of the large power grid or in an isolated island. The collaborative optimization configuration of the highway self-consistent multi-microgrid system is achieved, the low-carbon and clean level of the highway transportation system is improved, and it has important theoretical and practical significance for promoting the energy integration and coordinated development of highway transportation.

[0076] In addition to the above-described purposes, features and advantages, the present invention has other purposes, features and advantages. The present invention will be further described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] The drawings constituting a part of this application are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0078] Figure 1 It is a flow chart of a preferred embodiment of the present invention.

[0079] Figure 2 is an exemplary topological diagram of a preferred embodiment of the present invention.

[0080] Figure 3 It is a schematic diagram of the control strategy of a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0081] The embodiments of the present invention are described in detail below with reference to the accompanying drawings, but the present invention can be implemented in many different ways as defined and covered by the claims.

[0082] See also Figure 1 In a preferred embodiment of the present invention, a method for collaborative optimization configuration of a highway self-consistent multi-microgrid system taking into account electric vehicle charging load is provided, comprising the following steps:

[0083] S1. Analyze the self-consistent multi-microgrid system of highway and obtain the topological structure of the multi-microgrid system.

[0084] S2. Analyze the power characteristics of the multi-microgrid system according to the topological structure to obtain a first characteristic set; the first characteristic set includes photovoltaic power characteristics, wind power characteristics, energy storage charging and discharging characteristics, and electric vehicle charging load characteristics. Specifically including:

[0085] In a preferred embodiment of the present invention, the photovoltaic power characteristics include:

[0086] The photovoltaic power generation model can be expressed by the following relationship:

[0087] in, Output power for photovoltaic power generation; is the power derating factor of the photovoltaic power generation system, which is generally taken as 0.9; is the rated power of the photovoltaic power generation system; is the actual light intensity; is the light intensity under standard test conditions; is the power temperature coefficient; is the actual surface temperature; The temperature is the standard test condition.

[0088] In a preferred embodiment of the present invention, the wind power characteristics include:

[0089] Wind power generation It is mainly related to the real-time wind speed, and the wind speed approximately obeys the Weibull two-parameter distribution. The wind power generation model can be expressed by the following relationship:

[0090] in, and are the shape and size parameters of the fan respectively; is the rated output power of the wind turbine; The real-time wind speed when the fan is working; is the rated wind speed; is the cut-in wind speed; To cut out the wind speed; Specific parameters for the job; is the rated power of wind power.

[0091] In a preferred embodiment of the present invention, the energy storage charging and discharging characteristics include:

[0092] The battery energy storage model can be expressed by the following relationship:

[0093] in, The maximum allowable charging power of the battery; is the maximum allowable discharge power of the battery; The maximum allowable charging power of the battery at the initial moment; It is the maximum allowable charging power under the constraint of the maximum charging current of the battery; The maximum allowable charging power under the maximum charging rate constraint of the battery; The maximum allowable discharge power of the battery at the initial moment; The battery charging efficiency; is the battery discharge efficiency.

[0094] In a preferred embodiment of the present invention, the electric vehicle charging load characteristics include:

[0095] Since the data of the starting charging time and charging time of each electric vehicle on the highway are disordered and uncertain, the Monte Carlo method is used to simulate the charging load of electric vehicles:

[0096] Daily mileage of electric vehicles from highway entrance to charging station in service area Obey the lognormal distribution, probability density function It can be expressed by the following relationship:

[0097] in, , They are The mean and standard deviation of

[0098] Initial charge state of electric vehicles entering highways Obey uniform distribution, probability density function It can be expressed by the following relationship:

[0099] in, They are The lower and upper limits of

[0100] The state of charge of an electric vehicle when it arrives at a charging station It can be expressed by the following relationship:

[0101] in, is the battery capacity of the electric vehicle; The power consumption of electric vehicles per 100 kilometers;

[0102] The time when electric vehicles start charging at the station Obeys normal distribution, probability density function It can be expressed by the following relationship:

[0103] in, , They are The mean and standard deviation of

[0104] according to The charging time of electric vehicles can be calculated for:

[0105] in, is the rated charging power of the charging pile; charging efficiency for electric vehicles;

[0106] Charging end time for: Therefore, the charging power requirement of each electric vehicle is:

[0107]

[0108] The Monte Carlo method is used for simulation. After multiple simulations, the average value is obtained. That is the charging load characteristics of electric vehicles on highways:

[0109] in, is the number of simulations; is the number of electric vehicles; is the total number of simulations; is the total number of electric vehicles, i.e., the traffic volume.

[0110] In a preferred embodiment of the present invention, on a highway, there is uncertainty in the charging time, the starting charging time, etc. of each electric vehicle. The EV charging load simulation based on the Monte Carlo method can analyze electric vehicles as flexibility resources, thereby establishing a model to simulate the charging load demand of electric vehicles.

[0111] S3. According to the topological structure and the first characteristic set, the sub-microgrids in the multi-microgrid system are pre-divided to obtain the service area sub-microgrid and the tunnel sub-microgrid; the loads in the service area sub-microgrid and the tunnel sub-microgrid are divided according to the importance. Specifically including:

[0112] In the preferred embodiment of the present invention, an exemplary topology diagram of a multi-microgrid system is shown in Figure 2 .

[0113] In a preferred embodiment of the present invention, each sub-microgrid in the multi-microgrid system is classified into service area sub-microgrid and tunnel sub-microgrid. The service area sub-microgrid and tunnel sub-microgrid are generated by wind and solar renewable energy on the source side, and are stored in batteries on the storage side. The load side is divided according to the characteristics and level of the actual load; the actual load includes electric vehicle charging load, highway traffic system infrastructure and service facilities; the sub-microgrids can transmit electric power;

[0114] Each sub-microgrid has an energy trading channel with the electricity market. The electricity market is used to provide a preset limit of power deficiency compensation for each sub-microgrid in the multi-microgrid system under the constraints of voltage level transmission distance and capacity.

[0115] In a preferred embodiment of the present invention, the loads in the service area sub-microgrid and the tunnel sub-microgrid are divided into importance levels according to preset conditions, including:

[0116] According to preset conditions, the loads in the service area sub-microgrid and the tunnel sub-microgrid are divided into three categories: important load, transferable load and interruptible load. Among them, the important load is the first in importance, the transferable load is the second in importance, and the interruptible load is the third in importance.

[0117] In the preferred embodiment of the present invention, the division results are shown in Table 1:

[0118]

[0119] In the preferred embodiment of the present invention, the sub-microgrid is divided into a service area sub-microgrid and a tunnel sub-microgrid, and their load types and importance are analyzed in detail, which provides a basis for the study of the operation control strategy of multiple microgrids.

[0120] S4, obtaining a control strategy for the multi-microgrid system according to the topological structure, the first characteristic set, the service area sub-microgrid and the tunnel sub-microgrid; constructing a first model by considering the control strategy according to the preset objective function and the preset constraint conditions. Specifically including:

[0121] In the preferred embodiment of the present invention, see Figure 3 , the control strategies include:

[0122] A1. Determine whether the photovoltaic and wind power outputs in the multi-microgrid system are greater than the load in the multi-microgrid system;

[0123] If the output of photovoltaic and wind power is greater than the load in the multi-microgrid system, each sub-microgrid determines the state of energy storage capacity and whether there is any remaining power. If so, it sells electricity to the power market; if not, it proceeds to A6;

[0124] If the output of photovoltaic and wind power is greater than the load in the multi-microgrid system, the process goes to A2;

[0125] A2: Determine whether power exchange is possible between sub-microgrids within the multi-microgrid system; if so, proceed to A3; if not, each sub-microgrid determines the energy storage capacity status and performs energy dispatch according to the load importance, and proceed to A5;

[0126] A3, determine whether the remaining power of each sub-microgrid meets the important load of the multi-microgrid system; if so, proceed to A6, if not, proceed to A4;

[0127] A4. Each sub-microgrid determines the energy storage capacity status and enters A5;

[0128] A5: Determine whether power balance is achieved between each microgrid. If so, proceed to A6; if not, proceed to A7;

[0129] A6. Determine whether to purchase and sell electricity based on the time-of-use electricity price, and purchase and sell electricity adaptively;

[0130] A7. Purchase electricity from the electricity market.

[0131] In a preferred embodiment of the present invention, the preset objective function includes:

[0132] The optimization goal is to minimize the total cost of the multi-microgrid system. The preset objective function can be expressed by the following relationship:

[0133] in, For the Daily investment cost of energy storage system of each microgrid; is the trading session in hours; is the total number of trading hours per day, which is 24 hours in the preferred embodiment of the present invention; is the number of microgrids in the system; for The interaction cost between multiple microgrids in a time period, in yuan; is the transaction cost of each microgrid power market, in yuan; For the The operating cost of each microgrid and micro-power source, in yuan; and Respectively The discomfort cost of the transferable load and the compensation cost of the interruptible load in each microgrid, in yuan;

[0134] The daily investment cost of energy storage system includes:

[0135] in, For the The planned capacity of the battery, The investment cost of the battery per unit rated power, in yuan; is the battery life, in days; is the fund discount rate, which is 0.04 in the preferred embodiment of the present invention;

[0136] The interaction costs between multiple microgrids include:

[0137] in, For micro-grid i The interactive transmission cost between microgrids is in yuan; For Microgrid and Microgrid Unit power transmission cost coefficient, unit is yuan\kW; for Time Microgrid and Microgrid The amount of electricity traded, in kW;

[0138] Electricity market transaction costs include:

[0139] in, and They are The price of electricity purchase and sale in the electricity market during the period, in yuan; and They are Time Microgrid The amount of electricity purchased and sold from the electricity market, in kW;

[0140] Micro power supply operating costs include:

[0141] in, for Time period The wind power output of each microgrid, in kW; for Time period The photovoltaic output of each microgrid, in kW; for Time period The battery storage output of each microgrid, in kW; is the unit power operation and maintenance cost of the micro power source, in Yuan\kW;

[0142] The discomfort costs of shiftable loads and the compensation costs of interruptible loads include:

[0143] in, and is the user discomfort coefficient; For the Indoor temperature of users in each microgrid; For the The most comfortable temperature for users in each microgrid; For the Interruptible load value in each microgrid; For the The load benchmark value within each microgrid.

[0144] In a preferred embodiment of the present invention, the preset constraints include:

[0145] (1) Interaction constraints among multiple microgrids

[0146] in, express Moment by Ziwei Network Flow to sub-microgrid of electrical energy; express Moment by Ziwei Network Flow to sub-microgrid of electrical energy;

[0147] (2) Micro power output constraints

[0148] in, , and They are During the period, the sub-micro network The actual output power of photovoltaic, wind power and battery energy storage in the house, in kW; , and are the maximum output power of photovoltaic, wind power and battery energy storage in the sub-microgrid, in kW;

[0149] (3) Power balance constraints

[0150] in, express Time period Individual microgrid infrastructure loads; express Time period The transferable load value within each microgrid;

[0151] (4) EV charging station constraints

[0152] in, The maximum power of EV charging load; is the charging power margin; The maximum permitted construction capacity for EV charging stations; The load power for EV charging.

[0153] In the preferred embodiment of the present invention, the interconnection and mutual assistance of electric energy between each sub-microgrid in the multi-microgrid system is taken into consideration. Compared with the traditional single microgrid planning, it can effectively improve the energy interconnection between multiple microgrids and improve the energy utilization efficiency; at the same time, it strengthens the deep integration between the highway traffic system and the energy system. By considering the minimum total cost as the objective function, the overall operating cost of the self-consistent multi-microgrid system of the highway is effectively reduced.

[0154] S5. Solve the first model, obtain the coordinated optimization configuration plan of the multi-microgrid system, and complete the optimization configuration.

[0155] In summary, the method for collaborative optimization configuration of a self-consistent multi-microgrid system for highways taking into account the charging load of electric vehicles of the present invention adopts a microgrid power supply mode, and collaboratively plans distributed photovoltaic, wind power, electric vehicle charging loads, energy storage devices and controllable loads within a certain range, fully considering the power supply side and demand side resources, to form a power network that can operate in parallel with the large power grid and can operate independently of the large power grid or in an isolated island, thus realizing the collaborative optimization configuration of a self-consistent multi-microgrid system for highways, improving the low-carbon and clean level of highway transportation systems, and having important theoretical and practical significance for promoting energy integration and coordinated development of highway transportation.

[0156] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for collaborative optimization configuration of a highway self-consistent multi-microgrid system taking into account electric vehicle charging load, characterized in that: The following steps are involved: S1. Analyze the highway self-consistent multi-microgrid system to obtain the topological structure of the multi-microgrid system; S2. Analyze the power characteristics of the multi-microgrid system according to the topological structure to obtain a first characteristic set; the first characteristic set includes photovoltaic power characteristics, wind power characteristics, energy storage charging and discharging characteristics, and electric vehicle charging load characteristics; S3, pre-dividing the sub-microgrids in the multi-microgrid system according to the topological structure and the first characteristic set to obtain a service area sub-microgrid and a tunnel sub-microgrid; The loads in the service area sub-microgrid and the tunnel sub-microgrid are divided according to their importance; The S3 includes: The sub-microgrids in the multi-microgrid system are classified into service area sub-microgrids and tunnel sub-microgrids. The service area sub-microgrid and tunnel sub-microgrid are generated by wind and solar renewable energy on the source side, and energy is stored in batteries on the storage side. The load side is divided according to the characteristics and levels of the actual load; the actual load includes electric vehicle charging load, highway traffic system infrastructure and service facilities; the sub-microgrids can transmit electric power; Each sub-microgrid has an energy trading channel with the power market, and the power market is used to provide a preset limit of power deficiency compensation for each sub-microgrid in the multi-microgrid system under the constraints of voltage level transmission distance and capacity; S4, obtaining a control strategy of the multi-microgrid system according to the topological structure, the first characteristic set, the service area sub-microgrid and the tunnel sub-microgrid; constructing a first model according to a preset objective function and preset constraints while considering the control strategy; The control strategy includes: A1. Determine whether the photovoltaic and wind power outputs in the multi-microgrid system are both greater than the load in the multi-microgrid system; If the photovoltaic and wind power outputs are both greater than the load in the multi-microgrid system, each sub-microgrid determines the energy storage capacity status and whether there is any remaining power. If so, electricity is sold to the power market; if not, enter A6; If the photovoltaic and wind power outputs are both greater than the load in the multi-microgrid system, the process proceeds to A2; A2, determine whether power interaction is possible between the sub-microgrids within the multi-microgrid system; if so, proceed to A3; if not, each sub-microgrid determines the energy storage capacity status and performs energy dispatch according to the load importance, and proceed to A5; A3, determine whether the remaining power of each sub-microgrid meets the important load in the multi-microgrid system; if so, proceed to A6, if not, proceed to A4; A4. Each sub-microgrid determines the energy storage capacity status and enters A5; A5: Determine whether power balance is achieved between each microgrid. If so, proceed to A6; if not, proceed to A7; A6. Determine whether to purchase and sell electricity based on the time-of-use electricity price, and purchase and sell electricity adaptively; A7. Purchase electricity from the electricity market; S5. Solve the first model to obtain a collaborative optimization configuration solution for the multi-microgrid system, and complete the optimization configuration.

2. The method for collaborative optimization configuration of a highway self-consistent multi-microgrid system taking into account electric vehicle charging load according to claim 1 is characterized in that: The S3 further includes: The loads in the service area sub-microgrid and the tunnel sub-microgrid are divided into importance levels according to preset conditions, including: According to preset conditions, the loads in the service area sub-microgrid and the tunnel sub-microgrid are divided into three categories: important load, transferable load and interruptible load, among which the important load has the first importance, the transferable load has the second importance, and the interruptible load has the third importance.

3. The method for collaborative optimization configuration of a highway self-consistent multi-microgrid system taking into account electric vehicle charging load according to claim 2 is characterized in that: The photovoltaic power characteristics include: The photovoltaic power generation model can be expressed by the following relationship: ， in, Output power for photovoltaic power generation; is the power derating factor of the photovoltaic power generation system; is the rated power of the photovoltaic power generation system; is the actual light intensity; is the light intensity under standard test conditions; is the power temperature coefficient; is the actual surface temperature; The temperature is the standard test condition.

4. The method for collaborative optimization configuration of a highway self-consistent multi-microgrid system taking into account electric vehicle charging load according to claim 3 is characterized in that: The wind power characteristics include: The wind power generation model can be expressed by the following relationship: ， ， in, and are the shape and size parameters of the fan respectively; is the rated output power of the wind turbine; The real-time wind speed when the fan is working; is the rated wind speed; is the cut-in wind speed; To cut out the wind speed; Specific parameters for the job; is the rated power of wind power.

5. The method for collaborative optimization configuration of a highway self-consistent multi-microgrid system taking into account electric vehicle charging load according to claim 4 is characterized in that: The energy storage charge and discharge characteristics include: The battery energy storage model can be expressed by the following relationship: ， in, The maximum allowable charging power of the battery; is the maximum allowable discharge power of the battery; The maximum allowable charging power of the battery at the initial moment; It is the maximum allowable charging power under the constraint of the maximum charging current of the battery; The maximum allowable charging power under the maximum charging rate constraint of the battery; The maximum allowable discharge power of the battery at the initial moment; The battery charging efficiency; is the battery discharge efficiency.

6. The method for collaborative optimization configuration of a highway self-consistent multi-microgrid system taking into account electric vehicle charging load according to claim 5 is characterized in that: The electric vehicle charging load characteristics include: Monte Carlo method is used to simulate the charging load of electric vehicles: Daily mileage of electric vehicles from highway entrance to charging station in service area L Obey the lognormal distribution, probability density function It can be expressed by the following relationship: ， in, They are The mean and standard deviation of Initial charge state of electric vehicles entering highways Obey uniform distribution, probability density function It can be expressed by the following relationship: ， in, They are The lower and upper limits of The state of charge of an electric vehicle when it arrives at a charging station It can be expressed by the following relationship: ， in, is the battery capacity of the electric vehicle; The power consumption of electric vehicles per 100 kilometers; The time when electric vehicles start charging at the station Obeys normal distribution, probability density function It can be expressed by the following relationship: ， in, They are The mean and standard deviation of according to The charging time of electric vehicles can be calculated for: ， in, is the rated charging power of the charging pile; charging efficiency for electric vehicles; Charging end time for: , Therefore, the charging power requirement of each electric vehicle is: ， The Monte Carlo method is used for simulation. After multiple simulations, the average value is obtained. That is the charging load characteristics of electric vehicles on highways: ， in, is the number of simulations; is the number of electric vehicles; is the total number of simulations; is the total number of electric vehicles, i.e., the traffic volume.

7. The method for collaborative optimization configuration of a highway self-consistent multi-microgrid system taking into account electric vehicle charging load according to claim 6 is characterized in that: The preset objective function includes: The optimization goal is to minimize the total cost of the multi-microgrid system. The preset objective function can be expressed by the following relationship: ， in, For the Daily investment cost of energy storage system of each microgrid; is the trading session in hours; is the total number of trading sessions per day; is the number of microgrids in the system; for The interaction cost between multiple microgrids in a time period, in yuan; is the transaction cost of each microgrid power market, in yuan; For the The operating cost of each microgrid and micro-power source, in yuan; and Respectively The discomfort cost of the transferable load and the compensation cost of the interruptible load in each microgrid, in yuan; The daily investment cost of the energy storage system includes: ， in, For the The planned capacity of the battery is The investment cost of the battery per unit rated power, in yuan; is the battery life, in days; is the funding discount rate; The interaction costs between the multi-microgrids include: ， in, For micro-grid The interactive transmission cost between microgrids is in yuan; For Microgrid and Microgrid Unit power transmission cost coefficient, unit is yuan\kW; for Time Microgrid and Microgrid The amount of electricity traded, in kW; The electricity market transaction costs include: ， in, and They are The price of electricity purchase and sale in the electricity market during the period, in yuan; and They are Time Microgrid The amount of electricity purchased and sold from the electricity market, in kW; The micro power supply operating costs include: ， in, for Time period The wind power output of each microgrid, in kW; for Time period The photovoltaic output of each microgrid, in kW; for Time period The battery storage output of each microgrid, in kW; It is the unit power operation and maintenance cost of the micro power source, in Yuan\kW; The discomfort costs of the transferable loads and the compensation costs of the interruptible loads include: ， ， in, and is the user discomfort coefficient; For the Indoor temperature of users in each microgrid; For the The most comfortable temperature for users in each microgrid; For the Interruptible load value in each microgrid; For the The load benchmark value within each microgrid.

8. The method for collaborative optimization configuration of a highway self-consistent multi-microgrid system taking into account electric vehicle charging load according to claim 7 is characterized in that: The preset constraints include: (1) Interaction constraints among multiple microgrids ， in, express Moment by Ziwei Network Flow to sub-microgrid of electrical energy; express Moment by Ziwei Network Flow to sub-microgrid of electrical energy; (2) Micro power output constraints ， ， ， in, and They are During the period, the sub-micro network The actual output power of photovoltaic, wind power and battery energy storage in the house, in kW; and are the maximum output power of photovoltaic, wind power and battery energy storage in the sub-microgrid, in kW; (3) Power balance constraints ， in, express Time period Individual microgrid infrastructure loads; express Time period The transferable load value within each microgrid; (4) EV charging station constraints ， in, The maximum power of EV charging load; is the charging power margin; The maximum permitted construction capacity for EV charging stations; The load power for EV charging.

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