Integrated energy system double-layer optimization for electric vehicle coordinated shared energy storage

Abstract

The invention provides comprehensive energy system double-layer optimization for electric vehicle coordinated shared energy storage. The integrated energy system double-layer optimization of the electric vehicle coordinated shared energy storage comprises the steps that a, a multi-microgrid integrated energy system is established, in the microgrid integrated energy system, an equivalent shared energy storage model of an electric vehicle cluster is established, and an electric vehicle is regarded as a schedulable flexible load and energy storage device; by constructing an equivalent shared energy storage model of an electric vehicle cluster, the schedulable potential of an electric vehicle is fully excavated, so that the electric vehicle participates in micro-grid scheduling as a flexible load and energy storage device. The problem of randomness of electric vehicle charging time and electric quantity requirements is effectively solved, uncertainty in energy storage system dispatching is reduced, the Minkowski summation method is adopted, an electric vehicle cluster is modeled into generalized energy storage equipment, the dimension of the model is reduced, the calculation efficiency and the solving speed of the system are improved, and the method is suitable for popularization and application. And the stability and the accuracy under large-scale complex problems are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of integrated energy systems, and in particular to double-layer optimization of integrated energy systems in which electric vehicles coordinate shared energy storage. Background Art

[0002] The integrated energy system of electric vehicles coordinating shared energy storage integrates electric vehicles into the multi-microgrid system as a shared energy storage resource by scheduling the charging and discharging behavior of electric vehicles. In this system, due to the volatility of renewable energy generation and the imbalance of load demand between different microgrids, the use of shared energy storage equipment can achieve power regulation between multiple microgrids. Specifically, the system first establishes an equivalent shared energy storage model for electric vehicles, and fully taps the dispatchable potential of electric vehicles to enable them to participate in the operation and scheduling of multiple microgrids. Then, the system is planned through a two-layer optimization model. The upper model is responsible for optimizing the configuration and capacity planning of energy storage power stations to minimize the overall system operation cost; the lower model is responsible for optimizing the operation and scheduling of microgrids to ensure the energy balance within the microgrid and the efficient operation of equipment. The KKT method is used to transform the lower-level constraints into solvable problems in the upper layer in order to obtain the optimal energy configuration and operation strategy.

[0003] Although the system of electric vehicles coordinating shared energy storage can theoretically effectively reduce system operating costs and optimize energy utilization efficiency, it also has some limitations. The charging and discharging behavior of electric vehicles is subject to the actual usage habits of users. The randomness of charging time and power demand may lead to uncertainty in the scheduling of energy storage systems, affecting the optimization effect. The system depends on the participation of electric vehicles. If electric vehicle users cannot respond to scheduling requests in a timely manner or the charging and discharging efficiency of energy storage equipment is insufficient, the actual benefits of the system may be weakened. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] In view of the deficiencies in the prior art, the present invention provides a two-layer optimization of an integrated energy system in which electric vehicles coordinate shared energy storage, thereby solving the problem that the randomness of charging time and power demand may lead to uncertainty in the scheduling of the energy storage system; and the problem that the users of electric vehicles cannot respond to scheduling requests in a timely manner or the charging and discharging efficiency of the energy storage equipment is insufficient, which may weaken the actual benefits of the system.

[0006] (II) Technical solution

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: two-layer optimization of the integrated energy system of electric vehicles coordinating shared energy storage, including: a. Establishing a multi-microgrid integrated energy system, in which an equivalent shared energy storage model of electric vehicle clusters is established, and electric vehicles are regarded as a dispatchable flexible load and energy storage device. The model formula is:

[0008] 0≤P ch,j,t ≤P ch,max,j,t ,0≤P dis,j,t ≤P dis,max,j,t

[0009] Among them, P ch,j,t and P dis,j,t are the charging and discharging power of the electric vehicle in time period t, P ch,max,j,t and P dis,max,j,t The upper limit of charge and discharge power;

[0010] b. Through the user behavior prediction model based on historical data, the charging and discharging demand of electric vehicles and their potential dispatchability are evaluated, and the Minkowski summation method is used to aggregate the electric vehicle cluster into a generalized energy storage device. The formula is:

[0011] A⊕B={a+b|a∈A,b∈B}

[0012] This model effectively reduces the variable dimension and ensures the computational efficiency of the system;

[0013] c. Construct a two-layer optimization model for shared energy storage equipment, where the upper model is used to optimize the capacity configuration of shared energy storage equipment, and the lower model optimizes the internal power dispatch of the microgrid. The optimization goal of the upper model is to minimize the annual operating cost of the system. The formula is:

[0014]

[0015] Among them, C inv represents the investment cost of energy storage equipment, C op Indicates the operating cost of the system;

[0016] d. In the upper model, the Karush-Kuhn-Tucker condition is used to convert the lower problem into the upper constraint to ensure that the solution of the model meets the optimal conditions. The formula is:

[0017]

[0018] Among them, L(x,λ) is the Lagrangian function, λ is the dual variable, and g(x) is the operation constraint of the electric vehicle and the energy storage system;

[0019] e. In the lower model, electric vehicles and energy storage equipment are scheduled to charge and discharge reasonably in the microgrid to meet the balance of power supply and demand in the system. The formula is:

[0020] -P ch,j,t +P dis,j,t +P grid,j,t =P load,j,t

[0021] Among them, P grid,j,t Represents the power obtained from the grid, P load,j,t is the microgrid load demand;

[0022] f. Dynamically adjust the charging and discharging priority of electric vehicles, dynamically adjust the scheduling strategy according to the real-time market price signals of charging stations and user needs, and ensure the power demand of electric vehicle users and the stability of the system;

[0023] g. Use the big M method to linearize the nonlinear problem to ensure the solution efficiency and convergence of the upper and lower models. The formula is:

[0024] M(x)≥0,M>>1

[0025] This method can effectively handle complex nonlinear constraint problems and ensure the accuracy and convergence speed during the calculation process;

[0026] h. Considering the randomness and uncertainty of new energy, moderate power abandonment is allowed to improve the economy and flexibility of the energy storage system. The power abandonment rate is optimized and adjusted by analyzing the system benefits under different scenarios;

[0027] i. The system establishes a dynamic response mechanism based on the actual usage habits of electric vehicles, adjusts the priority and scheduling plan of electric vehicles participating in shared energy storage, and ensures the efficiency of system operation;

[0028] j. Optimize the charging behavior of electric vehicles, reduce the load on the power grid by shaving peaks and filling valleys, achieve orderly management of the energy storage system and optimize energy use, and improve the economy of the system;

[0029] k. Through the energy interaction between multi-microgrid integrated energy systems, make full use of the energy complementarity of shared energy storage equipment, optimize the transmission and exchange of electric energy, and improve the overall energy efficiency of the system;

[0030] I. The effectiveness of the two-level optimization model is verified by using simulation examples, which proves that the total operating cost of the system can be significantly reduced, the investment payback period can be shortened, and the economic benefits of energy storage equipment can be improved by rationally scheduling electric vehicles to participate in shared energy storage.

[0031] Preferably, the equivalent shared energy storage model is dynamically adjusted according to the arrival time, departure time and remaining battery power of the electric vehicle to adapt to changes in actual vehicle usage demand and charging behavior. The configuration of the shared energy storage device takes into account the energy complementarity and load complementarity between microgrids to reduce the energy storage investment cost of the overall system.

[0032] Preferably, the Minkowski summation method models the electric vehicle cluster as a generalized energy storage device, reduces the dimension and computational complexity of the scheduling model, and improves the model solution efficiency. The optimization goal of the upper model is to minimize the annualized operating cost of the shared energy storage device to solve the shared energy storage capacity configuration problem.

[0033] Preferably, the optimization goal of the lower model is to ensure the balance of power supply and demand in the microgrid and to improve the energy efficiency of the system by reasonably scheduling the charging and discharging behavior of electric vehicles.

[0034] Preferably, the integrated energy system monitors the usage status of electric vehicles in real time and dynamically adjusts the charging strategy to ensure efficient consumption of new energy. The scheduling priority of the electric vehicles is adjusted according to the actual usage needs of users to ensure that the user's electricity demand is met first during system operation.

[0035] Preferably, the energy flow relationship between the multi-microgrid integrated energy systems is optimized to maximize the energy complementarity between the multi-microgrid integrated energy systems and improve the overall energy utilization efficiency of the system.

[0036] Preferably, the Big M method linearizes nonlinear problems to ensure the computational efficiency and stability of the model's solution process under large-scale problems. The two-layer optimization model coordinates the scheduling of the shared energy storage system to shorten the investment payback period of the energy storage equipment and improve the economic benefits of the system.

[0037] Preferably, the charging scheduling of the electric vehicles takes precedence over the scheduling of new energy consumption, and efficient utilization of energy storage equipment is achieved by smoothing grid load fluctuations. The role of the energy storage equipment in the multi-microgrid integrated energy system is verified by simulation to optimize the system's operating costs and new energy consumption rate.

[0038] (III) Beneficial effects

[0039] The present invention provides a two-layer optimization of an integrated energy system for coordinated shared energy storage by electric vehicles. It has the following beneficial effects: by constructing an equivalent shared energy storage model for electric vehicle clusters, the dispatchable potential of electric vehicles is fully tapped, so that they can participate in microgrid scheduling as flexible loads and energy storage devices. This effectively addresses the randomness of electric vehicle charging time and power demand, reduces uncertainty in energy storage system scheduling, and uses the Minkowski summation method to model electric vehicle clusters as generalized energy storage devices, reducing the dimension of the model, improving the system's computational efficiency and solution speed, and ensuring stability and accuracy under large-scale complex problems.

[0040] This solution achieves efficient management of the energy storage system through a two-layer optimization model. The upper model is responsible for optimizing the capacity configuration of shared energy storage equipment to minimize the annual operating cost of the system; the lower model optimizes the supply and demand balance and improves the energy interaction efficiency of the multi-microgrid system by dynamically scheduling electric vehicles and power flows within the microgrid. This design not only copes with the volatility of new energy output, but also reduces the grid load by shaving peaks and filling valleys, achieving efficient utilization of energy storage equipment, and ultimately reducing the total operating cost of the system, shortening the investment payback period, and verifying the optimization effect through simulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a schematic diagram of the overall structure of the present invention. DETAILED DESCRIPTION

[0042] 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.

[0043] like Figure 1 As shown, the embodiment of the present invention provides a two-layer optimization of an integrated energy system in which electric vehicles coordinate shared energy storage, including: a. Establishing a multi-microgrid integrated energy system, in which an equivalent shared energy storage model of an electric vehicle cluster is established, and electric vehicles are regarded as a dispatchable flexible load and energy storage device. The model formula is:

[0044] 0≤P ch,j,t ≤P ch,max,j,t ,0≤P dis,j,t ≤P dis,max,j,t

[0045] Among them, P ch,j,t and P dis,j,t are the charging and discharging power of the electric vehicle in time period t, P ch,max,j,t and P dis,max,j,tTo set the upper limit of charging and discharging power, the equivalent shared energy storage model is dynamically adjusted according to the arrival time, departure time and remaining battery power of electric vehicles to adapt to changes in actual vehicle use needs and charging behavior. The integrated energy system monitors the use status of electric vehicles in real time and dynamically adjusts the charging strategy to ensure the efficient consumption of new energy. The scheduling priority of electric vehicles is adjusted according to the actual use needs of users to ensure that the user's power demand is met first in the operation of the system. The energy flow relationship between multi-microgrid integrated energy systems is optimized to maximize the energy complementarity between multi-microgrid integrated energy systems and improve the overall energy utilization efficiency of the system. The charging scheduling of electric vehicles takes precedence over the new energy consumption scheduling. The efficient utilization of energy storage equipment is achieved by smoothing the load fluctuations of the power grid. The role of energy storage equipment in the multi-microgrid integrated energy system is verified by simulation to optimize the operating cost of the system and the new energy consumption rate. The equivalent shared energy storage model is dynamically adjusted in real time according to the arrival time, departure time and remaining power of electric vehicles to ensure that the model can flexibly adapt to the actual charging and discharging behavior of electric vehicles. The dynamic adjustment mechanism not only improves the system's responsiveness to random demands, but also optimizes the utilization rate of energy storage equipment to ensure that the system operates more efficiently and economically.

[0046] b. Through the user behavior prediction model based on historical data and the long-term accumulation of historical data, it is possible to make a relatively accurate prediction of the charging behavior, time and demand of electric vehicle users, evaluate the charging and discharging demand of electric vehicles and their potential dispatchability, and use the Minkowski summation method to aggregate electric vehicle clusters into generalized energy storage devices. The formula is:

[0047] A⊕B={a+b|a∈A,b∈B}

[0048] This model effectively reduces the variable dimension and ensures the computational efficiency of the system. The Minkowski summation method models the electric vehicle cluster as a generalized energy storage device, reducing the dimension and computational complexity of the scheduling model and improving the model's solution efficiency.

[0049] c. Construct a two-layer optimization model for shared energy storage equipment, where the upper model is used to optimize the capacity configuration of shared energy storage equipment. The two-layer optimization model coordinates the scheduling of energy storage equipment and power supply in the microgrid by introducing the upper and lower models. The lower model optimizes the internal power scheduling of the microgrid, and the optimization goal of the upper model is to minimize the annual operating cost of the system. The formula is:

[0050]

[0051] Among them, C inv represents the investment cost of energy storage equipment, C opIt represents the operating cost of the system. The configuration of shared energy storage equipment takes into account the energy complementarity and load complementarity between microgrids to reduce the energy storage investment cost of the overall system. The optimization goal of the upper model is to minimize the annual operating cost of the shared energy storage equipment to solve the shared energy storage capacity configuration problem. The two-layer optimization model coordinates the scheduling of the shared energy storage system to shorten the investment payback period of the energy storage equipment and improve the economic benefits of the system.

[0052] d. In the upper model, the Karush-Kuhn-Tucker condition is used to convert the lower problem into the upper constraint to ensure that the solution of the model meets the optimal conditions. When the KKT condition is used to convert the lower power dispatch problem into the upper constraint, the dynamic characteristics of the energy storage equipment and the uncertainty demand of electric vehicles are considered. The formula is:

[0053]

[0054] Among them, L(x,λ) is the Lagrangian function, λ is the dual variable, and g(x) is the operating constraint of the electric vehicle and the energy storage system.

[0055] e. In the lower model, electric vehicles and energy storage equipment are scheduled to charge and discharge reasonably in the microgrid to meet the balance of power supply and demand in the system. The formula is:

[0056] -P ch,j,t +P dis,j,t +P grid,j,t =P load,j,t

[0057] Among them, P grid,j,t Represents the power obtained from the grid, P load,j,t For the microgrid load demand, the optimization goal of the lower model is to ensure the balance of power supply and demand within the microgrid and improve the energy efficiency of the system by reasonably scheduling the charging and discharging behavior of electric vehicles.

[0058] f. Dynamically adjust the charging and discharging priority of electric vehicles, and dynamically adjust the scheduling strategy according to the real-time market price signals of charging stations and user needs to ensure the power demand of electric vehicle users and the stability of the system.

[0059] g. Use the big M method to linearize the nonlinear problem to ensure the solution efficiency and convergence of the upper and lower models. The formula is:

[0060] M(x)≥0,M>>1

[0061] This method can effectively handle complex nonlinear constraint problems and ensure the accuracy and convergence speed during the calculation process. The big M method linearizes nonlinear problems to ensure the computational efficiency and stability of the model solution process under large-scale problems.

[0062] h. Considering the randomness and uncertainty of new energy, moderate power abandonment is allowed to improve the economy and flexibility of the energy storage system. The power abandonment rate is optimized and adjusted by analyzing the system benefits under different scenarios.

[0063] i. The system is based on the actual usage habits of electric vehicles. By establishing an intelligent scheduling mechanism, the system can make flexible scheduling decisions based on the actual usage habits of users, establish a dynamic response mechanism, adjust the priority and scheduling plan of electric vehicles participating in shared energy storage, and ensure the efficiency of system operation.

[0064] j. Optimize the charging behavior of electric vehicles, reduce the load on the power grid by shaving peak loads and filling valleys, achieve orderly management of the energy storage system and optimize energy use, and improve the economy of the system.

[0065] k. Through the energy interaction between multi-microgrid integrated energy systems, make full use of the energy complementarity of shared energy storage equipment, optimize the transmission and exchange of electric energy, and improve the overall energy efficiency of the system.

[0066] I. The effectiveness of the two-level optimization model is verified by using simulation examples, which proves that the total operating cost of the system can be significantly reduced, the investment payback period can be shortened, and the economic benefits of energy storage equipment can be improved by rationally scheduling electric vehicles to participate in shared energy storage.

[0067] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. Two-layer optimization of integrated energy system with electric vehicles coordinating shared energy storage, characterized by: The following steps are involved: a. Establish a multi-microgrid integrated energy system. In the microgrid integrated energy system, establish an equivalent shared energy storage model for electric vehicle clusters, and regard electric vehicles as a dispatchable flexible load and energy storage device. The model formula is: 0≤P ch，j，t ≤P ch，max,j,t ,0≤P dis,j，t ≤P dis,max,j，t Among them, P ch，j,t and P dis,j，t are the charging and discharging power of the electric vehicle in time period t, P ch，max,j，t and P dis，max,j，t The upper limit of charge and discharge power; b. Through the user behavior prediction model based on historical data, the charging and discharging demand of electric vehicles and their potential dispatchability are evaluated, and the Minkowski summation method is used to aggregate the electric vehicle cluster into a generalized energy storage device. The formula is: This model effectively reduces the variable dimension and ensures the computational efficiency of the system; c. Construct a two-layer optimization model for shared energy storage equipment, where the upper model is used to optimize the capacity configuration of shared energy storage equipment, and the lower model optimizes the internal power dispatch of the microgrid. The optimization goal of the upper model is to minimize the annual operating cost of the system. The formula is: Among them, C inv represents the investment cost of energy storage equipment, C op Indicates the operating cost of the system; d. In the upper model, the Karush-Kuhn-Tucker condition is used to convert the lower problem into the upper constraint to ensure that the solution of the model meets the optimal conditions. The formula is: Among them, L(x, λ) is the Lagrangian function, λ is the dual variable, and g(x) is the operation constraint of the electric vehicle and the energy storage system; e. In the lower model, electric vehicles and energy storage equipment are scheduled to charge and discharge reasonably in the microgrid to meet the balance of power supply and demand in the system. The formula is: -P ch，j，t +P dis，j，t +P grid，j，t =P load，j，t Among them, P grid，j，t Represents the power obtained from the grid, P load，j，t is the microgrid load demand; f. Dynamically adjust the charging and discharging priority of electric vehicles, dynamically adjust the scheduling strategy according to the real-time market price signals of charging stations and user needs, and ensure the power demand of electric vehicle users and the stability of the system; g. Use the big M method to linearize the nonlinear problem to ensure the solution efficiency and convergence of the upper and lower models. The formula is: M(x)≥0, M>>1 This method can effectively handle complex nonlinear constraint problems and ensure the accuracy and convergence speed during the calculation process; h. Considering the randomness and uncertainty of new energy, moderate power abandonment is allowed to improve the economy and flexibility of the energy storage system. The power abandonment rate is optimized and adjusted by analyzing the system benefits under different scenarios; i. The system establishes a dynamic response mechanism based on the actual usage habits of electric vehicles, adjusts the priority and scheduling plan of electric vehicles participating in shared energy storage, and ensures the efficiency of system operation; j. Optimize the charging behavior of electric vehicles, reduce the load on the power grid by shaving peaks and filling valleys, achieve orderly management of the energy storage system and optimize energy use, and improve the economy of the system; k. Through the energy interaction between multi-microgrid integrated energy systems, make full use of the energy complementarity of shared energy storage equipment, optimize the transmission and exchange of electric energy, and improve the overall energy efficiency of the system; I. The effectiveness of the two-level optimization model is verified by using simulation examples, which proves that the total operating cost of the system can be significantly reduced, the investment payback period can be shortened, and the economic benefits of energy storage equipment can be improved by rationally scheduling electric vehicles to participate in shared energy storage.

2. The two-layer optimization of the integrated energy system for coordinated shared energy storage of electric vehicles according to claim 1 is characterized by: The equivalent shared energy storage model is dynamically adjusted according to the arrival time, departure time and remaining battery power of the electric vehicle to adapt to changes in actual vehicle demand and charging behavior. The configuration of the shared energy storage device takes into account the energy complementarity and load complementarity between microgrids to reduce the energy storage investment cost of the overall system.

3. The dual-layer optimization of the integrated energy system for coordinated shared energy storage of electric vehicles according to claim 1 is characterized by: The Minkowski summation method models the electric vehicle cluster as a generalized energy storage device, reduces the dimension and computational complexity of the scheduling model, and improves the model solution efficiency. The optimization goal of the upper model is to minimize the annualized operating cost of the shared energy storage device to solve the shared energy storage capacity configuration problem.

4. The dual-layer optimization of the integrated energy system for coordinated shared energy storage of electric vehicles according to claim 1 is characterized by: The optimization goal of the lower model is to ensure the balance of power supply and demand in the microgrid and improve the energy efficiency of the system by reasonably scheduling the charging and discharging behavior of electric vehicles.

5. The two-layer optimization of the integrated energy system of electric vehicles coordinating shared energy storage according to claim 1 is characterized by: The integrated energy system monitors the usage status of electric vehicles in real time and dynamically adjusts the charging strategy to ensure efficient consumption of new energy. The scheduling priority of the electric vehicles is adjusted according to the actual usage needs of users to ensure that the user's electricity demand is met first during system operation.

6. The dual-layer optimization of the integrated energy system for coordinated shared energy storage of electric vehicles according to claim 1 is characterized by: The energy flow relationship between the multi-microgrid integrated energy systems is optimized, the energy complementarity between the multi-microgrid integrated energy systems is maximized, and the overall energy utilization efficiency of the system is improved.

7. The dual-layer optimization of the integrated energy system for coordinated shared energy storage of electric vehicles according to claim 1 is characterized by: The big M method linearizes nonlinear problems to ensure the computational efficiency and stability of the model's solution process under large-scale problems. The two-layer optimization model coordinates the scheduling of the shared energy storage system to shorten the investment payback period of the energy storage equipment and improve the economic benefits of the system.

8. The dual-layer optimization of the integrated energy system for coordinated shared energy storage of electric vehicles according to claim 1 is characterized by: The charging scheduling of the electric vehicles takes precedence over the scheduling of new energy consumption, and efficient utilization of energy storage equipment is achieved by smoothing grid load fluctuations. The role of the energy storage equipment in the multi-microgrid integrated energy system is verified by simulation to optimize the system's operating costs and new energy consumption rate.

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