A collaborative optimization operation method for chained microgrids in highway service areas considering source-load uncertainty
By building a renewable energy and electric vehicle charging load prediction model, and optimizing the chain microgrid collaborative operation strategy of highway service areas, the problem of poor adaptability of collaborative operation in multiple service areas is solved, and more efficient energy utilization and low-carbon operation are achieved .
Patent Information
- Application Number
- CN202411435778.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-15
- Publication Date
- 2024-12-13
- Estimated Expiration
- 2044-10-15
AI Technical Summary
The existing highway service area energy system has poor adaptability in the coordinated operation strategy of multi-service areas, resulting in low energy utilization and inability to fully utilize the advantages of distributed energy.
Adopt the highway service area chain microgrid collaborative optimization operation method that takes into account the uncertainty of source load. By constructing a typical renewable energy sunrise force model and an electric vehicle charging load prediction model, a supply and demand balance equation and interaction model are established to optimize a single service area. and multi-service area operation strategies to achieve optimization of system economy, low carbonity and self-consistent rate.
It improves the energy utilization efficiency and stability of highway service areas, enhances the ability to absorb new energy, and reduces carbon emissions and operating costs.
Smart Images
Figure CN118966468B_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of highway service area energy systems, and in particular to a highway service area chain microgrid collaborative optimization operation method taking into account source and load uncertainty. Background Art
[0002] With the rapid development of renewable energy and the gradual advancement of carbon neutrality goals, the access of highway service area energy systems to renewable energy has become a new way to integrate transportation and energy in the future. In long-distance highway networks, the energy systems of multiple service areas often operate independently of each other, resulting in low resource utilization efficiency, insufficient new energy consumption rate, and high carbon emissions. In response to these problems, chain microgrids have gradually attracted attention as a new energy management model. Chain microgrids organically combine multiple geographically dispersed service area microgrid systems to achieve coordinated and optimized operation in terms of energy supply and demand, energy storage management, and load regulation. With the changes in traffic flow and the increase in the demand for electric vehicle charging, the existing coordinated operation strategies may not be able to flexibly adapt to these changes, and the existing strategies often focus on local optimization within the microgrid, ignoring the global synergy of the entire chain microgrid system, resulting in low overall energy utilization efficiency and failure to give full play to the advantages of distributed energy. Therefore, it is an urgent problem to propose a coordinated operation strategy with good scalability and adaptability to meet the needs of future highway service area energy systems. Summary of the invention
[0003] In view of this, the present invention provides a highway service area chain microgrid collaborative optimization operation method considering source and load uncertainty, so as to at least solve the problems of poor adaptability and low energy utilization of the collaborative operation strategy of the multi-service area system in the prior art.
[0004] In order to achieve the above object, the present invention adopts the following technical solution:
[0005] The coordinated optimization operation method of the chained microgrid in highway service area considering the uncertainty of source and load includes the following steps:
[0006] Collection Based on the historical data of the microgrid in each service area, a typical daily power generation model of renewable energy and a charging load prediction model for electric vehicles are constructed. The typical daily power generation model of renewable energy includes Typical daily photovoltaic output power in each service area and wind power output , the electric vehicle charging load prediction model is Electric vehicle charging load power in service areas ;
[0007] Get the The relevant parameters of each sub-microgrid in the service area, and according to the relevant parameters of each sub-microgrid, , and Establish the The supply and demand balance equation of each sub-microgrid in a service area and the interaction model between adjacent service areas, where the supply and demand balance equation includes the supply and demand balance equation of the sub-microgrid system and the operation constraints of the sub-microgrid equipment, and the interaction model includes the Service area to The interactive power transmitted in each service area , where the relevant parameters of each sub-microgrid include new energy configuration, energy storage and load parameters;
[0008] Based on The coordinated energy supply and consumption of each microgrid in the service area is shown in the figure below. The optimization objectives are to minimize the operation cost of each service area and maximize the energy self-consistency rate of the microgrid system in each service area, and establish a single service area microgrid optimization model as the upper-level strategy.
[0009] Based on the interactive power between microgrids in the service area , taking the lowest daily cost of carbon emission management and the lowest interaction cost among multiple service areas as the optimization goals, a multi-service area self-consistent system collaborative optimization model is constructed as the lower-level strategy;
[0010] Combined with the supply and demand balance equations of each sub-microgrid and the interaction model between adjacent service areas, the upper and lower strategies are optimized to obtain the distributed energy output, energy storage charging and discharging power, and service area microgrid interaction power that optimize the system's economy, low carbon and self-consistency.
[0011] against Iterative optimization is carried out in each service area to form a collaborative operation plan for multiple self-consistent energy systems.
[0012] Preferably, the specific contents of constructing a typical daily power generation model of renewable energy and a charging load prediction model of electric vehicles include:
[0013] Typical daily power generation models for renewable energy include:
[0014] No. The sampling step of the historical data of the microgrid in each service area is 1 hour, and the step of the output per unit value interval is 0.1. Probability of each per unit value interval , and then calculate the average value of the sum of all outputs in the period Finally, the output power values are summed up according to the time period to get the Typical daily photovoltaic output power in each service area and wind power output ;
[0015] In the formula, or , and Represent photovoltaic and wind power respectively; and Respectively represent Service areas in The output of renewable energy in the period is within the standard value The number of days in the range and the power level.
[0016] Preferably, the electric vehicle charging load prediction model includes:
[0017] Statistical analysis of historical data on the time when electric vehicles start charging at the station and charging time Data, obtained and The probability distribution function of The simulated charging load :
[0018] In the formula, charging efficiency for electric vehicles; For the Rated charging power of charging piles in service areas; and are the starting charging time and charging duration of the randomly sampled electric vehicles;
[0019] After random sampling, the average value is calculated to be the charging load power of electric vehicles in the service area. :
[0020] .
[0021] Preferably, the specific contents of the supply and demand balance formula of the sub-microgrid system include:
[0022] Service station micro-network:
[0023] Tunnel sub-microgrid: Toll station microgrid: In the formula, and Respectively Conventional load and grid power supply within each service area microgrid; and Respectively Energy storage charging and discharging power in the microgrid of each service area; and For the The maximum limits of transmission power and receiving power of the service area sub-microgrid; the superscripts 1, 2 and 3 represent the service station, tunnel and toll station sub-microgrids respectively.
[0024] Preferably, the sub-microgrid equipment operation constraints specifically include:
[0025] Energy storage equipment constraints:
[0026] In the formula, and Respectively Energy storage charging and discharging power in the microgrid of each service area; It is an introduced 0-1 variable, indicating that the energy storage device cannot charge and discharge energy at the same time; and The maximum charging and discharging power of the energy storage device; and The maximum and minimum charge states of the energy storage device; and They represent the charging and discharging efficiency of the energy storage device respectively; is the rated capacity of energy storage;
[0027] Photovoltaic and wind power output constraints:
[0028] Charging power constraints in service areas: Power mutual assistance constraints of sub-microgrids within the service area: In the formula, , and Respectively The maximum output power of photovoltaic and wind power and the number of charging piles in each service area; is the rated charging power of the charging pile; Representative Any microgrid in the service area, and Respectively Service Areas Moment Micro Network The transmission power and receiving power, and Respectively Service Area Microgrid The maximum transmission power and receiving power limits; It is an introduced 0-1 variable, indicating that the sub-microgrid cannot receive or transmit power at the same time.
[0029] Preferably, the interaction model is:
[0030] In the formula, Service area to The interaction power transmitted in each service area is .
[0031] Preferably, the specific contents of establishing a single service area microgrid optimization model include:
[0032] No. The optimization goal of the service area operation cost is:
[0033] In the formula, For the The power supply of the grid in the microgrid of each service area; is the energy storage system power; and They are the total unit electricity purchase cost of the microgrid in the service area, photovoltaic operation and maintenance cost, wind turbine operation and maintenance cost, energy storage operation and maintenance cost, charging pile operation and maintenance cost, and electric vehicle charging income;
[0034] No. Optimization target of energy self-consistency rate of microgrid system in each service area:
[0035] In the formula, is the system energy self-consistency rate, Respectively Service Area Microgrid The total photovoltaic and wind power output of each sub-microgrid, the power supply power of the grid and the total load of the microgrid in the service area at all times.
[0036] Preferably, the specific contents of constructing a multi-service area self-consistent system collaborative optimization model include:
[0037] Optimization objectives of low-carbon operation of multiple service areas on expressways:
[0038] Introduce carbon emission penalty price and establish carbon emission optimization target as:
[0039] In the formula, is the daily cost of carbon emission management of the system, is the penalty amount per unit of carbon dioxide emissions, For the Service Areas Natural gas usage at each moment; and are the carbon emission factors of power purchase per unit of the power grid and natural gas combustion respectively; For the The power supply of the grid in the microgrid of each service area; and They are photovoltaic and wind power abandonment respectively;
[0040] Interaction cost optimization objectives of multiple service areas on highways:
[0041] In the formula, Indicates service areas and Unit power interaction cost between service areas; Indicates the number of service areas; Indicates the scheduling period; is the simulation step size.
[0042] Through the above technical solutions, it can be known that compared with the prior art, the present invention discloses a method for collaborative optimization operation of chained microgrids in highway service areas taking into account source-load uncertainty, which has the following beneficial effects: the present invention constructs a supply-demand matching model taking into account multiple uncertain factors, and based on the energy supply and consumption of each sub-microgrid in the service area microgrid, collaboratively optimizes each subgrid in the chained microgrid, and establishes a single service area microgrid optimization model as an upper-level strategy; considering the interactive power between service area microgrids, taking the low-carbon nature of the collaborative operation of multiple service area microgrids and the optimal new energy absorption rate as the optimization goals, a multi-service area self-consistent system collaborative optimization model is constructed as a lower-level strategy; the method fully considers the collaborative operation potential of highway chained service area microgrids, including the energy interaction between microgrids. The optimization operation strategy not only optimizes within each subgrid, but also coordinates and allocates resources within the entire chained microgrid system to achieve orderly operation of the chained microgrid, reduce the risk of poor flexibility of a single highway service area, and can achieve energy interaction between service areas, effectively improve the absorption capacity of renewable energy in the highway area, so as to improve the energy utilization efficiency and stability of the overall system. BRIEF DESCRIPTION OF THE DRAWINGS
[0043] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.
[0044] Figure 1 A flowchart of a method for collaborative optimization operation of chained microgrids in highway service areas taking into account source-load uncertainty provided by an embodiment of the present invention. DETAILED DESCRIPTION
[0045] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
[0046] The present invention provides a method for coordinated optimization operation of a chain microgrid in a highway service area taking into account source and load uncertainty. Figure 1 As shown, the following steps are included:
[0047] Collection Based on the historical data of the microgrid in each service area, a typical daily power generation model of renewable energy and a charging load prediction model for electric vehicles are constructed. The typical daily power generation model of renewable energy includes Typical daily photovoltaic output power in each service area and wind power output , the electric vehicle charging load prediction model is Electric vehicle charging load power in service areas ;
[0048] Get the The relevant parameters of each sub-microgrid in the service area, and according to the relevant parameters of each sub-microgrid, , and Establish the The supply and demand balance equation of each sub-microgrid in a service area and the interaction model between adjacent service areas, where the supply and demand balance equation includes the supply and demand balance equation of the sub-microgrid system and the operation constraints of the sub-microgrid equipment, and the interaction model includes the Service area to The interactive power transmitted in each service area , where the relevant parameters of each sub-microgrid include new energy configuration, energy storage and load parameters;
[0049] Based on The coordinated energy supply and consumption of each microgrid in the service area is shown in the figure below. The optimization objectives are to minimize the operation cost of each service area and maximize the energy self-consistency rate of the microgrid system in each service area, and establish a single service area microgrid optimization model as the upper-level strategy.
[0050] Based on the interactive power between microgrids in the service area , taking the lowest daily cost of carbon emission management and the lowest interaction cost among multiple service areas as the optimization goals, a multi-service area self-consistent system collaborative optimization model is constructed as the lower-level strategy;
[0051] Combined with the supply and demand balance equations of each sub-microgrid and the interaction model between adjacent service areas, the upper and lower strategies are optimized to obtain the distributed energy output, energy storage charging and discharging power, and service area microgrid interaction power that optimize the system's economy, low carbon and self-consistency.
[0052] against N Iterative optimization is carried out in each service area to form a collaborative operation plan for multiple self-consistent energy systems.
[0053] In order to further implement the above technical solutions, the specific contents of constructing a typical daily output model of renewable energy and a charging load prediction model for electric vehicles include:
[0054] Typical daily power generation models for renewable energy include:
[0055] No. The sampling step of the historical data of the microgrid in each service area is 1 hour, and the step of the output per unit value interval is 0.1. Probability of each per unit value interval , and then calculate the average value of the sum of all outputs in the period Finally, the output power values are summed up according to the time period to get the Typical daily photovoltaic output power in each service area and wind power output ;
[0056] In the formula, or , and Represent photovoltaic and wind power respectively; and Respectively represent Service areas in The output of renewable energy in the period is within the standard value The number of days in the range and the power level.
[0057] In order to further implement the above technical solutions, the electric vehicle charging load prediction model includes:
[0058] Statistical analysis of historical data on the time when electric vehicles start charging at the station and charging time Data, obtained and The probability distribution function of The simulated charging load :
[0059] In the formula, charging efficiency for electric vehicles; For the Rated charging power of charging piles in service areas; and are the starting charging time and charging duration of the randomly sampled electric vehicles;
[0060] After random sampling, the average value is calculated to be the charging load power of electric vehicles in the service area. :
[0061] .
[0062] In order to further implement the above technical solution, the specific contents of the supply and demand balance formula of the sub-microgrid system include:
[0063] Service station micro-network:
[0064] Tunnel sub-microgrid: Toll station microgrid: In the formula, and Respectively Conventional load and grid power supply within each service area microgrid; and Respectively Energy storage charging and discharging power in the microgrid of each service area; and For the The maximum limits of transmission power and receiving power of the service area sub-microgrid; the superscripts 1, 2 and 3 represent the service station, tunnel and toll station sub-microgrids respectively.
[0065] In order to further implement the above technical solution, the operation constraints of the sub-microgrid equipment specifically include:
[0066] Energy storage equipment constraints:
[0067] In the formula, and Respectively Energy storage charging and discharging power in the microgrid of each service area; It is an introduced 0-1 variable, indicating that the energy storage device cannot charge and discharge energy at the same time; and The maximum charging and discharging power of the energy storage device; and The maximum and minimum charge states of the energy storage device; and They represent the charging and discharging efficiency of the energy storage device respectively; is the rated capacity of energy storage;
[0068] Photovoltaic and wind power output constraints:
[0069] Charging power constraints in service areas: Power mutual assistance constraints of sub-microgrids within the service area: In the formula, , and Respectively The maximum output power of photovoltaic and wind power and the number of charging piles in each service area; is the rated charging power of the charging pile; Representative Any microgrid in the service area, and Respectively Service Area Moment Micro Network The transmission power and receiving power, and Respectively Service Area Microgrid The maximum transmission power and receiving power limits; It is an introduced 0-1 variable, indicating that the sub-microgrid cannot receive or transmit power at the same time.
[0070] In order to further implement the above technical solution, the interaction model is:
[0071] In the formula, Service area to The interaction power transmitted in each service area is .
[0072] In order to further implement the above technical solutions, the specific contents of establishing a single service area microgrid optimization model include:
[0073] No. The optimization goal of the service area operation cost is:
[0074] In the formula, For the The power supply of the grid in the microgrid of each service area; is the energy storage system power; and They are the total unit electricity purchase cost of the microgrid in the service area, photovoltaic operation and maintenance cost, wind turbine operation and maintenance cost, energy storage operation and maintenance cost, charging pile operation and maintenance cost, and electric vehicle charging income;
[0075] No. Optimization target of energy self-consistency rate of microgrid system in each service area:
[0076] In the formula, is the system energy self-consistency rate, Respectively Service Area Microgrid The total photovoltaic and wind power output of each sub-microgrid, the power supply power of the grid and the total load of the microgrid in the service area at all times.
[0077] In order to further implement the above technical solutions, the specific contents of constructing a multi-service area self-consistent system collaborative optimization model include:
[0078] Optimization objectives of low-carbon operation of multiple service areas on expressways:
[0079] Introduce carbon emission penalty price and establish carbon emission optimization target as:
[0080] In the formula, is the daily cost of carbon emission management of the system, is the penalty amount per unit of carbon dioxide emissions, For the Service Area Natural gas usage at each moment; and are the carbon emission factors of power purchase per unit of the power grid and natural gas combustion respectively; For the The power supply of the grid in the microgrid of each service area; and They are photovoltaic and wind power abandonment respectively;
[0081] Interaction cost optimization objectives of multiple service areas on highways:
[0082] In the formula, Indicates service areas and Unit power interaction cost between service areas; Indicates the number of service areas; Indicates the scheduling period; is the simulation step size.
[0083] The present invention proposes a method for the coordinated optimization operation of chain microgrids in highway service areas that takes into account the uncertainty of source and load. The method constructs a typical daily output model of renewable energy by introducing a probability distribution model, obtains the predicted charging load based on Monte Carlo random sampling, and constructs a supply and demand matching model that takes into account multiple uncertain factors. Secondly, based on the coordinated energy supply and consumption of the sub-microgrids, each subgrid in the chain microgrid is coordinated and optimized, and a single service area microgrid optimization model is established as the upper-level strategy; considering the interactive power between service area microgrids, the low-carbon nature of the coordinated operation of multi-service area microgrids and the optimal new energy consumption rate are taken as the optimization goals, and a multi-service area self-consistent system coordinated optimization model is constructed as the lower-level strategy. The proposed method fully considers the energy interaction between microgrids. The optimization operation strategy not only optimizes within each subgrid, but also coordinates the allocation of resources within the entire chain microgrid system to improve the energy utilization efficiency and stability of the overall system.
[0084] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.
Claims
1. A method for collaborative optimization of chained microgrids in highway service areas considering source-load uncertainty, characterized in that: The following steps are involved: Collection Based on the historical data of the microgrid in each service area, a typical daily power generation model of renewable energy and a charging load prediction model for electric vehicles are constructed. The typical daily power generation model of renewable energy includes Typical daily photovoltaic output power in each service area and wind power output , the electric vehicle charging load prediction model is Electric vehicle charging load power in service areas ; Get the The relevant parameters of each sub-microgrid in the service area, and according to the relevant parameters of each sub-microgrid, , and Establish the The supply and demand balance equation of each sub-microgrid in a service area and the interaction model between adjacent service areas, where the supply and demand balance equation includes the supply and demand balance equation of the sub-microgrid system and the operation constraints of the sub-microgrid equipment, and the interaction model includes the Service area to The interactive power transmitted in each service area , where the relevant parameters of each sub-microgrid include new energy configuration, energy storage and load parameters; Based on The coordinated energy supply and consumption of each microgrid in the service area is shown in the figure below. The optimization objectives are to minimize the operation cost of each service area and maximize the energy self-consistency rate of the microgrid system in each service area, and establish a single service area microgrid optimization model as the upper-level strategy. Based on the interactive power between microgrids in the service area , taking the lowest daily cost of carbon emission management and the lowest interaction cost among multiple service areas as the optimization goals, a multi-service area self-consistent system collaborative optimization model is constructed as the lower-level strategy; Combined with the supply and demand balance equations of each sub-microgrid and the interaction model between adjacent service areas, the upper and lower strategies are optimized to obtain the distributed energy output, energy storage charging and discharging power, and service area microgrid interaction power that optimize the system's economy, low carbon and self-consistency. against N Iterate and optimize each service area to form a collaborative operation plan for multiple self-consistent energy systems.
2. The method for coordinated optimization operation of chained microgrids in highway service areas considering source-load uncertainty according to claim 1 is characterized in that: The specific contents of constructing a typical daily power generation model for renewable energy and a charging load prediction model for electric vehicles include: Typical daily power generation models for renewable energy include: No. The sampling step of the historical data of the microgrid in each service area is 1 hour, and the step of the output per unit value interval is 0.
1. Probability of each per unit value interval , and then calculate the average value of the sum of all outputs in the period Finally, the output power values are summed up according to the time period to get the Typical daily photovoltaic output power in each service area and wind power output ; ， In the formula, or , and Represent photovoltaic and wind power respectively; and Respectively represent Service areas in The output of renewable energy in the period is within the standard value The number of days in the range and the power level.
3. The method for coordinated optimization operation of chained microgrids in highway service areas considering source-load uncertainty according to claim 1 is characterized in that: The electric vehicle charging load forecasting model includes: Statistical analysis of historical data on the time when electric vehicles start charging at the station and charging time Data, obtained and The probability distribution function of The simulated charging load : ， In the formula, charging efficiency for electric vehicles; For the Rated charging power of charging piles in service areas; and are the starting charging time and charging duration of the randomly sampled electric vehicles; After random sampling, the average value is calculated to be the charging load power of electric vehicles in the service area. : 。 4. The method for coordinated optimization operation of chained microgrids in highway service areas considering source-load uncertainty according to claim 1 is characterized in that: The specific contents of the supply and demand balance formula of the sub-microgrid system include: Service station micro-network: ， Tunnel sub-microgrid: , Toll station microgrid: , In the formula, and Respectively Conventional load and grid power supply within each service area microgrid; and Respectively Energy storage charging and discharging power in the microgrid of each service area; and For the The maximum limits of transmission power and receiving power of the service area sub-microgrid; the superscripts 1, 2 and 3 represent the service station, tunnel and toll station sub-microgrids respectively.
5. The method for coordinated optimization operation of chained microgrids in highway service areas considering source-load uncertainty according to claim 1 is characterized in that: The operation constraints of sub-microgrid equipment include: Energy storage equipment constraints: ， In the formula, and Respectively Energy storage charging and discharging power in the microgrid of each service area; It is an introduced 0-1 variable, indicating that the energy storage device cannot charge and discharge energy at the same time; and The maximum charging and discharging power of the energy storage device; and The maximum and minimum charge states of the energy storage device; and They represent the charging and discharging efficiency of the energy storage device respectively; is the rated capacity of energy storage; Photovoltaic and wind power output constraints: ， Charging power constraints in service areas: ， Power mutual assistance constraints of sub-microgrids within the service area: ， In the formula, , and Respectively The maximum output power of photovoltaic and wind power and the number of charging piles in each service area; is the rated charging power of the charging pile; Representative Any microgrid in the service area, and Respectively Service Areas Moment Micro Network The transmission power and receiving power, and Respectively Service Area Microgrid The maximum transmission power and receiving power limits; It is an introduced 0-1 variable, indicating that the sub-microgrid cannot receive or transmit power at the same time.
6. The method for coordinated optimization operation of chained microgrids in highway service areas considering source-load uncertainty according to claim 5 is characterized in that: The interaction model is: ， In the formula, Service area to The interaction power transmitted in each service area is .
7. The method for coordinated optimization operation of chained microgrids in highway service areas considering source-load uncertainty according to claim 1 is characterized in that: The specific contents of establishing a single service area microgrid optimization model include: No. The optimization goal of the service area operation cost is: ， In the formula, For the The power supply of the grid in the microgrid of each service area; is the energy storage system power; and They are the total unit electricity purchase cost of the microgrid in the service area, photovoltaic operation and maintenance cost, wind turbine operation and maintenance cost, energy storage operation and maintenance cost, charging pile operation and maintenance cost, and electric vehicle charging income; No. Optimization target of energy self-consistency rate of microgrid system in each service area: ， In the formula, is the system energy self-consistency rate, Respectively Service Area Microgrid The total photovoltaic and wind power output of each sub-microgrid, the power supply power of the grid and the total load of the microgrid in the service area at all times.
8. The method for coordinated optimization operation of chained microgrids in highway service areas considering source-load uncertainty according to claim 1 is characterized in that: The specific contents of constructing a multi-service area self-consistent system collaborative optimization model include: Optimization objectives of low-carbon operation of multiple service areas on expressways: Introduce carbon emission penalty price and establish carbon emission optimization target as: ， In the formula, is the daily cost of carbon emission management of the system, is the penalty amount per unit of carbon dioxide emissions, For the Service Areas Natural gas usage at each moment; and are the carbon emission factors of power grid unit electricity purchase and natural gas combustion respectively; For the The power supply of the grid in the microgrid of each service area; and They are photovoltaic and wind power curtailment respectively; Interaction cost optimization objectives of multiple service areas on highways: ， In the formula, Indicates service areas and Unit power interaction cost between service areas; Indicates the number of service areas; Indicates the scheduling period; is the simulation step size.