Off-grid microgrid and electric vehicle collaborative optimization method and system

Abstract

The invention relates to an off-grid micro-grid and electric vehicle collaborative optimization method and system, and the method comprises the steps: S1, obtaining fixed parameters in an off-grid micro-grid and electric vehicle, and the fixed parameters comprise the basic characteristics of wind power, photovoltaic, electric vehicle and gas turbine equipment; s2, according to the fixed parameters, an objective function is constructed, constraint conditions are preset, a mathematical model is established according to the objective function and the constraint conditions, and the objective function is cost minimization within a preset time period; s3, basic parameters are obtained, the mathematical model is solved according to the basic parameters, a solving result is obtained, if the solving result meets the requirement, an optimal configuration scheme for collaborative optimization of the off-grid microgrid and the electric vehicle is output, if the solving result does not meet the requirement, the step S2 is executed, and the solving result comprises the electric power balance condition, the energy output fluctuation and the V2G application condition. The method solves the problem that the participation degrees of the electric vehicles are not matched under different operation conditions in a traditional method.

Description

Technical Field

[0001] The present invention relates to the technical field of off-grid microgrid energy management and optimization, and particularly to a collaborative optimization method and system for an off-grid microgrid and an electric vehicle. Background Art

[0002] There are many deficiencies in the current microgrid system in terms of capacity configuration and scheduling scheme. First of all, traditional microgrid optimization models usually focus on single-type energy management and fail to fully consider the coordinated operation of multiple energy sources such as wind power, photovoltaic power, and gas turbines with electric vehicles. As a distributed energy storage device with V2G (Vehicle-to-Grid) function, an electric vehicle can supply power to the microgrid through reverse charging and has the characteristics of mobility and flexibility. If the V2G function of the electric vehicle can be effectively integrated into the energy management of the microgrid, it will help improve the overall efficiency and reliability of the system. However, existing optimization models often ignore the participation of electric vehicles, resulting in the microgrid being unable to fully utilize the reverse power supply capacity of electric vehicles during peak loads, and the power supply may be insufficient; while during low loads, it is unable to effectively store the excess power in electric vehicles, causing energy waste.

[0003] In addition, although existing technologies have made certain progress in dealing with the phenomena of wind and light abandonment and load abandonment, there are still some areas that need improvement. Due to the volatility and uncontrollability of wind power and photovoltaic power generation, there are sometimes situations where the generated electricity exceeds the system load demand, resulting in some renewable energy not being fully utilized, thus increasing the operating cost of the system. Traditional models also have deficiencies in coping with power demand fluctuations in different time periods. The power load characteristics on weekdays and holidays are significantly different, and traditional models fail to make dynamic adjustments according to the load characteristics of different time periods and cannot fully utilize the V2G regulation ability of electric vehicles, resulting in the microgrid being unable to meet the load demand in some time periods, while there is over-configuration in other time periods.

[0004] In summary, the existing technologies have the following main disadvantages in the capacity optimization configuration of the microgrid system: 1) Lack of coordinated optimization of multiple energy sources and the V2G function of electric vehicles, and failure to fully consider the comprehensive utilization and synergy effect of wind power, photovoltaic power, gas turbines and the V2G of electric vehicles, which limits the overall efficiency of the system. 2) There are deficiencies in dealing with the phenomena of wind and light abandonment and load abandonment, and it is unable to maximize the utilization of renewable energy and the bidirectional charging ability of electric vehicles, resulting in energy waste and increased operating costs. 3) Failure to perform dynamic optimization according to the changes in power load characteristics: Failure to flexibly adjust the charging and discharging strategies of electric vehicles according to different load characteristics such as weekdays and holidays, resulting in sub-optimal configuration and scheduling of the microgrid and electric vehicles in different time periods.

[0005] These drawbacks limit the overall performance of off-grid microgrid systems. There is an urgent need for a new optimization model to solve the above problems. By introducing the V2G collaborative optimization of electric vehicles, the present invention proposes a collaborative optimization method and system for off-grid microgrids and electric vehicles, aiming to improve the economy and environmental friendliness of the system and achieve efficient utilization of energy. Summary of the Invention

[0006] The object of the present invention is to provide a collaborative optimization method and system for off-grid microgrids and electric vehicles, which can effectively improve the utilization rate of electric vehicles, reduce the phenomenon of wind and photovoltaic curtailment, achieve cost minimization, thereby improving the overall performance and economy of the system, and enhancing the reliability and environmental friendliness of the system.

[0007] To achieve the above object, the present invention provides the following solutions:

[0008] In a first aspect, the present invention provides a collaborative optimization method for off-grid microgrids and electric vehicles, including:

[0009] S1. Obtain the fixed parameters in the off-grid microgrid and electric vehicles, where the fixed parameters include the basic characteristics of wind power, photovoltaic power, electric vehicles, and gas turbines.

[0010] S2. According to the fixed parameters, construct an objective function and preset constraint conditions, and establish a mathematical model based on the objective function and the constraint conditions, where the objective function is to minimize the cost within a preset time period.

[0011] S3. Obtain the basic parameters, solve the mathematical model according to the basic parameters, and obtain the solution result. If the solution result meets the requirements, output the optimal configuration plan for the collaborative optimization of the off-grid microgrid and electric vehicles. If the solution result does not meet the requirements, return to S2, where the solution result includes the power balance situation, energy output fluctuation, and V2G application situation.

[0012] Optionally, the basic characteristics include installed capacity, output curve, charge and discharge efficiency, output limit, and ramp rate.

[0013] Optionally, the objective function is:

[0014]

[0015] where Z is the cost, C ins(t) 、C om(t) are the installation cost and operation and maintenance cost at time t, respectively; is the start-up cost of the gas turbine at time t; C pl(t) 、C ll(t) represent the penalty cost for wind and photovoltaic curtailment and the penalty cost for load rejection at time t, respectively.

[0016] Optionally, the installation cost is:

[0017]

[0018] In the formula, C ins，i(t) is the cost of the i-th type of generator set at time t, and C ins，j(t) is the cost of the electric vehicle cluster device j at time t. I i and I j represent the maximum power of the i-th type of generator set and the electric vehicle cluster j respectively. Ng is the total number of generator sets;

[0019] The operation and maintenance cost is:

[0020]

[0021] In the formula, C om，i(t) and C om，j(t) represent the operation and maintenance costs of the i-th type of generator set and the electric vehicle cluster device j at time point t respectively;

[0022] The start-up cost is:

[0023]

[0024] In the formula, represents the start-up cost of the gas turbine at the time point, represents the start-up state of the gas turbine at the time point;

[0025] The penalty cost for curtailment of wind and solar power is:

[0026]

[0027] In the formula, C pl refers to the penalty cost per unit of curtailment of wind and solar power, is the curtailment of wind and solar power at the time point;

[0028] The penalty cost for load shedding is:

[0029] C ll(t) = C LL × LL (t)

[0030] In the formula, C LL is the penalty cost per unit of load shedding, and LL(t) is the load shedding at time point t.

[0031] Optionally, the constraint conditions include real-time power balance constraint, wind and solar power output constraint, thermal power output constraint, electric vehicle state constraint, load shedding and curtailment of wind and solar power constraint, gas turbine start-stop constraint, and gas turbine unit ramp rate constraint.

[0032] Optionally, the basic parameters include initial investment cost, startup cost, operation and maintenance cost, discount rate, and operation period.

[0033] Optionally, solving the mathematical model according to the basic parameters includes: using the Gurobi mathematical optimization solver to solve the mathematical model.

[0034] In a second aspect, the present invention provides an off-grid microgrid and electric vehicle collaborative optimization system, including: a parameter setting module, a model construction module, and a model solving and analysis module;

[0035] The parameter setting module is used to obtain the fixed parameters in the off-grid microgrid and electric vehicles, where the fixed parameters include the basic characteristics of wind power, photovoltaic, electric vehicles, and gas turbine equipment;

[0036] The model construction module is used to construct an objective function and preset constraint conditions according to the fixed parameters, and establish a mathematical model according to the objective function and the constraint conditions, where the objective function is to minimize the cost within a preset time period;

[0037] The model solving and analysis module is used to obtain the basic parameters, solve the mathematical model according to the basic parameters, and obtain the solution result. If the solution result meets the requirements, it outputs the optimal configuration plan for the collaborative optimization of the off-grid microgrid and electric vehicles. If the solution result does not meet the requirements, it returns to S2, where the solution result includes the power balance situation, energy output fluctuation, and V2G application situation.

[0038] In a third aspect, the present invention provides a computer device, including a memory and a processor, where the memory stores a computer program, and is characterized in that when the processor executes the computer program, it implements the steps of an off-grid microgrid and electric vehicle collaborative optimization method.

[0039] In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of an off-grid microgrid and electric vehicle collaborative optimization method.

[0040] The beneficial effects of the present invention are as follows: Through the collaborative optimization of electric vehicles, the present invention solves the problem of mismatched participation of electric vehicles under different operating conditions in traditional methods. Specifically, when wind and light curtailment and load shedding are not considered, the high installed capacity of the system on weekdays indicates a strong ability to respond to electricity demand, while the significant increase in the power supply demand of electric vehicles on holidays reflects the characteristics of load fluctuations. After introducing wind and light curtailment and load shedding, the installed capacity on weekdays decreases, and the utilization rate of electric vehicles increases. This not only reflects the effective response of the system to the instability of wind and light resources but also optimizes the economic benefits. In addition, by increasing the participation of electric vehicles, the present invention effectively reduces the economic losses caused by resource waste and load loss, ensures the reliability and stability of power supply, and thus improves the overall performance and environmental benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0042] Figure 1 It is a flowchart of a method for collaborative optimization of an off-grid microgrid and an electric vehicle according to an embodiment of the present invention;

[0043] Figure 2 It is a curve graph of wind and light output and load of a method for collaborative optimization of an off-grid microgrid and an electric vehicle according to an embodiment of the present invention, where (a) is the wind power output curve, (b) is the photovoltaic power output curve, (c) is the weekday load, and (d) is the holiday load;

[0044] Figure 3 It is the power optimization output results of various situations of a method for collaborative optimization of an off-grid microgrid and an electric vehicle according to an embodiment of the present invention. Among them, (a) is the power optimization output result of the weekday load situation, (b) is the power optimization output result of the holiday load situation, (c) is the power optimization output result of the weekday load considering wind and light curtailment and load shedding, and (d) is the power optimization output result of the holiday considering wind and light curtailment and load shedding;

[0045] Figure 4 It is the wind and light curtailment and load shedding results of various situations of a method for collaborative optimization of an off-grid microgrid and an electric vehicle according to an embodiment of the present invention. Among them, (a) is the wind and light curtailment and load shedding curve on weekdays, and (b) is the wind and light curtailment and load shedding curve considering holidays. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0046] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0047] To make the above objects, features, and advantages of the present invention more obvious and understandable, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0048] Embodiment 1:

[0049] This embodiment provides a collaborative optimization method for an off-grid microgrid and electric vehicles, including:

[0050] S1. Obtain the fixed parameters in the off-grid microgrid and electric vehicles, where the fixed parameters include the basic characteristics of wind power, photovoltaic, electric vehicles, and gas turbine equipment;

[0051] S2. According to the fixed parameters, construct an objective function and preset constraint conditions, and establish a mathematical model based on the objective function and constraint conditions, where the objective function is to minimize the cost within a preset time period;

[0052] S3. Obtain the basic parameters, solve the mathematical model according to the basic parameters, obtain the solution result. If the solution result meets the requirements, output the optimal configuration plan for the collaborative optimization of the off-grid microgrid and electric vehicles. If the solution result does not meet the requirements, return to S2, where the solution result includes the power balance situation, energy output fluctuation, and V2G application situation.

[0053] Further, the basic characteristics of the gas turbine equipment include installed capacity, power generation efficiency, output limit, and ramp-up ability.

[0054] Further, the objective function is:

[0055]

[0056] Where Z is the cost, C ins(t) 、C om(t) are the installation cost and operation and maintenance cost at time point t respectively; is the start-up cost of the gas turbine at time point t; C pl(t) 、C ll(t) represent the penalty cost for abandoning wind and light and the penalty cost for abandoning load at time point t respectively.

[0057] Specifically, the specific items in the objective function are as follows:

[0058] The installation cost C ins(t) :

[0059] The installation cost at time t is the sum of a series of costs involved in the equipment purchase, construction and installation, design, commissioning, etc. required for various equipment to reach the available condition. The expression is as follows:

[0060]

[0061] In the formula, C ins，i(t) is the cost (yuan / kW) of the i-th type of generator set (wind power, photovoltaic, diesel generator) at time t, and C ins，j (t) is the cost (yuan / kW) of the electric vehicle cluster equipment j at time t. I i , I j respectively represent the maximum power (kW) of the i-th type of generator set and the electric vehicle cluster j. C inins，i , C inins，j are the initial investment costs (yuan / kW) of the i-th type of generator set and the electric vehicle cluster equipment j, r i , r j are the discount rates of the i-th type of generator set and the electric vehicle cluster equipment j, n i , n j are the operation years of the i-th type of generator set and the electric vehicle cluster j.

[0062] The operation and maintenance cost C om(t) :

[0063] The operation and maintenance cost refers to the repair costs dynamically invested to ensure the normal operation of the generator and the V2G system during their service life. As follows:

[0064]

[0065] C om，i(t) = C yrom，i ÷8760 (6)

[0066] C om，j(t) = C yrom，j ÷8760 (7)

[0067] C om，i(t) , C om，j(t) , C yrom，i , C yrom，j respectively represent the operation and maintenance cost (yuan / kw.hour) and the annual operation and maintenance cost (yuan / kW.year) of the i-th type of generator set and the electric vehicle cluster equipment j at time t. I i , I j represent the maximum power (kW) of the i-th type of generator set and the electric vehicle cluster j.

[0068] Startup cost

[0069] The start-up cost refers to all the costs incurred when starting up a micro gas turbine from a shutdown state under different start-up conditions, as shown in the expression:

[0070]

[0071] In the formula, represents the start-up cost of the gas turbine at time point t (yuan / time), represents the start-up state of the gas turbine at time point t. is a 0,1 variable. Its value is 1 when starting up and 0 when shutting down.

[0072] The penalty cost for wind and light curtailment C pl(t) :

[0073] The penalty cost for wind and light curtailment refers to the penalty cost incurred due to the fact that the power generation from wind and light exceeds the maximum capacity that the system can accommodate, resulting in the curtailment of some wind and light power generation.

[0074]

[0075] C pl refers to the penalty cost per unit of wind and light curtailment (yuan / kW), is the amount of wind and light curtailment at time point t (kW), i is the index variable of the generating unit, that is, the i-th generating unit, and Ng is the total number of generating units (wind power and photovoltaic power stations).

[0076] The penalty cost for load curtailment C ll(t) :

[0077] In a power system with renewable energy, due to prediction errors and the volatility of renewable energy itself, there may be a phenomenon of forced load curtailment in the system at some extreme moments, and the cost caused by this phenomenon is the penalty cost for load curtailment.

[0078] C ll(t) = C LL × LL (t) (10)

[0079] C LL is the penalty cost per unit of load curtailment (yuan / kW); LL (t) is the amount of load curtailment at time point t (kW).

[0080] Furthermore, the constraint conditions include real-time power balance constraints, wind and light output constraints, thermal power output constraints, electric vehicle state constraints, load curtailment and wind and light curtailment amount constraints, gas turbine start-stop constraints, and gas turbine unit ramp rate constraints.

[0081] Specifically, according to the variables involved in step 2), the following detailed constraint conditions are set:

[0082] Real-time power balance constraint:

[0083] To ensure the safe and stable operation of the system, it is required that the system generation be balanced with the load during the operation dispatching time. Considering the losses of curtailed wind and curtailed light, the energy balance constraint at time t is expressed as:

[0084]

[0085] Wherein, respectively represent the output powers (kW) of various types of power generation equipment (photovoltaic, wind power, micro gas turbine) at time t, respectively represent the discharging and charging powers (kW) of electric vehicle cluster j at time t, P de(t) represents the real-time load demand (kW) at time t.

[0086] Wind and solar power output constraint:

[0087] The output constraint is usually formulated by the power dispatching agency according to the operation conditions and safety requirements of the system, restricting the output power or speed of equipment such as generators and motors to ensure the stable operation and safety of the system, avoid equipment overload or underload, and prevent fluctuations in system frequency and voltage.

[0088]

[0089] In the formula, are respectively the rated powers (kW) of photovoltaic and wind power. This constraint limits the fluctuation range of wind and solar power output to ensure the safe and stable operation of the power system.

[0090] Thermal power output constraint:

[0091]

[0092] In the formula, respectively represent the minimum and maximum output limits of thermal power at time t.

[0093] Electric vehicle state constraint:

[0094] State of charge SOC j(t) describes the ratio of the remaining battery charge of an electric vehicle to its rated capacity at time t, reflecting its remaining energy and available capacity. The state of charge constraint is used to ensure that excessive or improper power consumption does not damage the battery, and is used to guide the charging and discharging strategies of electric vehicle clusters to maximize system utilization and extend service life. The state of charge of an electric vehicle is restricted between the minimum value and the maximum value to prevent overcharging and over-discharging and extend battery life.

[0095]

[0096] wherein, u ch , u dch are the charging and discharging efficiencies (%) of the electric vehicle respectively, and E j is the rated capacity (kWh) of the electric vehicle cluster.

[0097]

[0098] The above formula limits the maximum and minimum values of the charging and discharging power of the electric vehicle cluster.

[0099]

[0100] The above formula ensures that the electric vehicle does not charge and discharge simultaneously.

[0101] Load shedding and abandoned wind and solar power constraints:

[0102]

[0103] 0 ≤ LL (t) ≤ P de(t) (23)

[0104]

[0105] On the one hand, the above formula limits the maximum and minimum values of the abandoned wind and solar power and load shedding power. On the other hand, it ensures that load shedding and abandoned wind and solar power do not occur simultaneously.

[0106] Gas turbine start-stop constraints:

[0107]

[0108] is the start-up cost of the gas turbine. This constraint means that when and only when , the gas turbine starts at time t, and there is a start-up cost at time t In other combined cases the value of is 0.

[0109] Gas turbine unit ramp rate constraint:

[0110] When the thermal power unit adjusts its power generation, it is restricted by mechanical, thermal and other aspects and cannot immediately reach the set target value. It needs a gradual adjustment process, that is, the ramp rate constraint of the thermal power system.

[0111]

[0112] They are the up and down ramping capabilities of the unit respectively. The formula ensures that the gas turbine is restricted by the up and down ramping capabilities during continuous startup. When the unit starts and stops, it is not affected by the ramping capabilities and operates at the minimum thermal power output Start and stop.

[0113] Furthermore, the basic parameters include initial investment cost, startup cost, operation and maintenance cost, discount rate, and operation period.

[0114] Furthermore, solving the mathematical model according to the basic parameters includes: using the Gurobi mathematical optimization solver to solve the mathematical model.

[0115] The method of this embodiment will be further described below in conjunction with the accompanying drawings:

[0116] As Figure 1 shown, a method for collaborative optimization of an off-grid microgrid and electric vehicles provided by the present invention includes the following steps:

[0117] 1) Set the fixed parameters required for the model. These parameters include the basic characteristics of wind power, photovoltaic, electric vehicle, and gas turbine equipment, such as installed capacity, output curve, charge and discharge efficiency, output limit, and ramping ability;

[0118] 2) According to the fixed parameters in step 1), establish a mathematical model and define the objective function. The purpose of the objective function is to minimize the cost of the system within a time period. Multiple factors such as installation cost, operation and maintenance cost, startup cost, penalty cost for curtailment of wind and solar power, and penalty cost for load shedding need to be comprehensively considered in the model;

[0119] The objective function involved in step 2) is the minimum cost within the time period T. According to the existing capacity and cost parameters of wind power, photovoltaic, and gas turbine, optimize the charge and discharge strategy of electric vehicles and their scheduling plan. The specific model construction is shown in formula (1). Each component item in the objective function is shown in formulas (2)-(10).

[0120] 3) Define the constraint conditions, which include the installed capacity of the off-grid microgrid and electric vehicle collaborative optimization system, the output of the gas turbine, start and stop, ramping, wind and solar output, charge and discharge status of electric vehicles, and the amount of load shedding and curtailment of wind and solar power. Combining with the objective function established in step 2), ensure the feasibility and effectiveness of the model during operation, and that the system can meet the requirements of power balance, output limit, and electric vehicle status in actual operation;

[0121] According to the variables involved in step 2), the set constraint conditions are shown in formulas (11)-(27).

[0122] 4) Input the basic parameters required for the operation of the input model. These parameters include initial investment cost, startup cost, operation and maintenance cost, discount rate, and operation period.

[0123] 5) According to the model in steps 2) and 3) and the basic parameters in step 4), run the model for solution and analyze the solution results. These results include power balance, energy output fluctuation, and V2G application. Compare the solution results with the actual data to check whether the power balance output by the model is reasonable, whether the energy output conforms to the expected fluctuation range, and whether the V2G application effectively meets the demand.

[0124] 6) Conduct a comparison between the simulation test and the actual operation data to check the performance of the solution results under different scenarios and whether they can stably meet the system requirements. If the solution results do not meet the expectations, it is necessary to adjust the model parameters and constraint conditions according to steps 2) and 3) and re-run the solution. After passing the verification, finally output the optimal configuration plan for the coordinated optimization of the off-grid microgrid and electric vehicles.

[0125] The wind and light output curves and load curves are as Figure 2 (a)-(d) shown and the specific data is shown in Table 1. According to the optimization results of the method of this embodiment, the scheduling of electric vehicles shows obvious differences under different conditions, indicating that the system has high adaptability and optimization ability in coping with actual operation challenges.

[0126] Table 1

[0127]

[0128]

[0129] The final results are shown in Table 2. Considering the curtailment of wind and light and the curtailment of load, the optimal available power of electric vehicles in the system on weekdays is 42.61 kW, and the electricity available for microgrid scheduling is 360.63 kWh, while on holidays they are 58.52 kW and 557.23 kWh respectively. These data indicate that in the absence of additional losses, the system selects a higher participation rate of electric vehicle reverse power supply in order to meet the power demand and improve the regulation ability. During holidays, the demand for the V2G function of electric vehicles increases significantly, reflecting the higher power demand and greater volatility during holidays, and the system responds to these demands by increasing the participation of electric vehicles.

[0130] After considering the curtailed wind power and curtailed load in the optimization model, the optimal available power of electric vehicles on weekdays drops to 27.75 kW, and the electricity available for microgrid dispatching increases to 429.58 kWh, while on holidays they are 39.88 kW and 557.72 kWh respectively. The lower available power and higher electricity indicate that in actual operation, the system needs to optimize the V2G strategy of electric vehicles to cope with the instability of wind and solar resources and the fluctuations of load. This adjustment reflects the system's response ability to the fluctuations of wind and solar resources and the changes of load in practical applications. By increasing the electricity supplied by electric vehicles to the microgrid in the reverse direction, the system can better handle the curtailed wind power and curtailed load, thus optimizing the energy utilization efficiency.

[0131] Table 2

[0132]

[0133] As Figure 3 (a)-(d) shows that in all scenarios, electric vehicles have demonstrated their significant energy storage and regulation functions. During the peak solar power generation period in the daytime (10 - 15 hours), the electric vehicle cluster absorbs the surplus solar power generation through charging, playing a role in smoothing the power generation curve. During the peak load period in the evening (18 - 20 hours), the electric vehicle cluster supports the system to meet the load demand through discharging, thus reducing the system's dependence on traditional energy. Without considering the curtailed wind and solar power, the charging and discharging power of electric vehicles increases significantly, indicating that their energy storage function is further exerted, effectively improving the utilization rate of renewable energy. At the same time, the gas turbine mainly operates as a backup power source to make up for the peak load or the shortage of renewable energy.

[0134] On weekdays, the load demand is relatively high, and the charging and discharging capabilities of electric vehicles during the day and at night are fully utilized, cooperating with the gas turbine to meet the load demand. On holidays, the load demand decreases significantly, and the excess solar power generation is more used to charge electric vehicles, while the discharging demand relatively decreases, showing the system's flexible adaptability to the load demand. Considering and not considering the scenario of curtailed wind and solar power also have a greater impact on the system operation efficiency. When considering the curtailed wind and solar power, part of the photovoltaic and wind power generation is curtailed, resulting in lower charging and discharging power of electric vehicles; while in the case where curtailed wind and solar power are not allowed, the excess wind and solar power generation is fully utilized, and the charging and discharging capabilities of electric vehicles are developed to the maximum extent, further improving the utilization rate of renewable energy.

[0135] Figure 4 (a)-(b) shows that the curtailment of photovoltaic power mainly concentrates in the peak solar power generation period (10 - 11 hours), which is more serious on holidays, while the curtailment of wind power is less and the utilization rate is higher. The load loss mainly occurs in the early morning low-load period, and the loss on weekdays is slightly higher.

[0136] These results indicate that, after considering the curtailment of wind and solar power and load shedding, the system can schedule electric vehicles more flexibly to cope with the challenges in actual operation. The optimized strategy addresses the fluctuations in wind and solar resources and the uncertainty of load by increasing the V2G power supply of electric vehicles, reducing the economic losses caused by resource waste and load shedding. The lower available power and higher power supply also indicate that the system can utilize the reverse power supply capacity of electric vehicles more effectively, reducing the demand for additional power generation capacity during peak loads while improving the utilization efficiency of renewable energy. Ultimately, these adjustments and optimizations enhance the overall economic and environmental benefits of the system, ensuring the stability and reliability of power supply.

[0137] Embodiment 2:

[0138] An off-grid microgrid and electric vehicle collaborative optimization system, comprising: a parameter setting module, a model construction module, and a model solution analysis module;

[0139] The parameter setting module is used to obtain the fixed parameters in the off-grid microgrid and electric vehicles, where the fixed parameters include the basic characteristics of wind power, photovoltaic power, electric vehicles, and gas turbine equipment;

[0140] Specifically, the parameter setting module ensures the consistency and stability of model operation, improves system reliability, and provides standardized input for model construction and solution.

[0141] The model construction module is used to construct an objective function and preset constraint conditions according to the fixed parameters, and establish a mathematical model based on the objective function and constraint conditions, where the objective function is the minimization of cost within a preset time period;

[0142] Specifically, the model construction module abstracts the actual problem into a mathematical model, improving the accuracy and efficiency of problem-solving and providing a theoretical basis for objective function optimization; the definition of constraint conditions ensures that the solution results meet the actual requirements such as power balance and output limits, avoiding invalid and infeasible solutions, and providing boundary conditions for model solution. The digital input realizes effective data transfer, improves system interactivity, and supports model solution.

[0143] The model solution analysis module is used to obtain the basic parameters, solve the mathematical model according to the basic parameters, and obtain the solution results. If the solution results meet the requirements, it outputs the optimal configuration plan for the collaborative optimization of the off-grid microgrid and electric vehicles. If the solution results do not meet the requirements, it returns to S2, where the solution results include the power balance situation, energy output fluctuations, and V2G application situation.

[0144] The specific model solution analysis module obtains the optimized objective function value to provide a basis for decision-making, and indicates the model optimization direction by analyzing the solution process. It also verifies and compares the results to check the accuracy and reliability of the solution results, evaluates the adaptability and robustness of the model under different scenarios, and provides a reference for practical engineering applications. The collaborative effect of these modules significantly improves the operating efficiency and resource utilization rate of the system.

[0145] Embodiment 3:

[0146] A computer device includes a memory and a processor. The memory stores a computer program. It is characterized in that when the processor executes the computer program, the steps of an off-grid microgrid and electric vehicle collaborative optimization method are implemented.

[0147] Embodiment 4:

[0148] A computer-readable storage medium stores a computer program. It is characterized in that when the computer program is executed by a processor, the steps of an off-grid microgrid and electric vehicle collaborative optimization method are implemented.

[0149] If the described functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs, etc., which can store program codes.

[0150] The logic and / or steps represented in the flowchart or described in other ways herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in combination with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device.

[0151] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection (electronic device) having one or more wirings, a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or, if necessary, other suitable processing, and then stored in a computer memory.

[0152] The embodiments described above are only descriptions of the preferred embodiments of the present invention and do not limit the scope of the present invention. Without departing from the spirit of the design of the present invention, various modifications and improvements made by those of ordinary skill in the art to the technical solutions of the present invention shall fall within the protection scope determined by the claims of the present invention.

Claims

1. An off-grid microgrid and electric vehicle collaborative optimization method, characterized in that, Including: S1. Obtain the fixed parameters in the off-grid microgrid and electric vehicles, where the fixed parameters include the basic characteristics of wind power, photovoltaic power, electric vehicles, and gas turbine equipment; S2. According to the fixed parameters, construct an objective function and preset constraint conditions, and establish a mathematical model based on the objective function and the constraint conditions, where the objective function is to minimize the cost within a preset time period; S3. Obtain the basic parameters, solve the mathematical model according to the basic parameters, and obtain the solution result. If the solution result meets the requirements, output the optimal configuration plan for the coordinated optimization of the off-grid microgrid and electric vehicles. If the solution result does not meet the requirements, return to S2, where the solution result includes the power balance situation, energy output fluctuation, and V2G application situation.

2. The off-grid microgrid and electric vehicle collaborative optimization method according to claim 1, wherein The basic characteristics include installed capacity, output curve, charge-discharge efficiency, output limit, and ramp-up capacity.

3. The off-grid microgrid and electric vehicle collaborative optimization method according to claim 1, wherein The objective function is: Among them, Z is the cost, C ins(t) , C om(t) are the installation cost and operation and maintenance cost at time point t respectively; is the start-up cost of the gas turbine at time point t; C pl(t) , C ll(t) represent the penalty cost for wind and light curtailment and the penalty cost for load shedding at time point t respectively.

4. The off-grid microgrid and electric vehicle collaborative optimization method according to claim 3, characterized in that, The installation cost is: where, C ins,i(t) is the cost of the i-th type of power generation unit at time t, and C ins,j(t) is the cost of the electric vehicle cluster device j at time t. I i , I j represent the maximum power of the i-th type of power generation unit and the electric vehicle cluster j respectively. Ng is the total number of power generation units; The operation and maintenance cost is: Where, C om，i(t) and C om，j(t) respectively represent the operation and maintenance costs of the i-th type of generating unit and the electric vehicle cluster device j at time point t; The start-up cost is: In the formula, represents the start-up cost of the gas turbine at a time point, represents the start-up state of the gas turbine at a time point; The penalty cost for wind and light abandonment is: Where C pl represents the penalty cost for unit curtailment of wind and solar power, and is the amount of curtailed wind and solar power at a time point; The penalty cost for load abandonment is: C ll(t) = C LL × LL (t) where C LL is the penalty cost for unit load shedding, and LL (t) is the amount of load shedding at time point t.

5. The off-grid microgrid and electric vehicle collaborative optimization method according to claim 1, characterized in that The constraint conditions include real-time power balance constraint, wind and light output constraint, thermal power output constraint, electric vehicle state constraint, load abandonment and wind and light abandonment quantity constraint, gas turbine start-stop constraint, and gas turbine unit ramp-up constraint.

6. The off-grid microgrid and electric vehicle collaborative optimization method according to claim 1, wherein The basic parameters include initial investment cost, start-up cost, operation and maintenance cost, discount rate, and operation life.

7. The off-grid microgrid and electric vehicle collaborative optimization method according to claim 1, characterized in that Solving the mathematical model according to the basic parameters includes: using the Gurobi mathematical optimization solver to solve the mathematical model.

8. The system of the off-grid microgrid and electric vehicle collaborative optimization method according to any one of claims 1-7, comprising: Parameter setting module, model construction module, model solution and analysis module; The parameter setting module is used to obtain the fixed parameters in the off-grid microgrid and electric vehicles, where the fixed parameters include the basic characteristics of wind power, photovoltaic power, electric vehicles, and gas turbine equipment; The model construction module is used to construct an objective function and preset constraint conditions according to the fixed parameters, and establish a mathematical model based on the objective function and the constraint conditions, where the objective function is to minimize the cost within a preset time period; The model solution and analysis module is used to obtain the basic parameters, solve the mathematical model according to the basic parameters, and obtain the solution result. If the solution result meets the requirements, output the optimal configuration plan for the coordinated optimization of the off-grid microgrid and electric vehicles. If the solution result does not meet the requirements, return to S2, where the solution result includes the power balance situation, energy output fluctuation, and V2G application situation.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method according to any one of claims 1 to 7.

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