Electric vehicle and energy storage multi-layer joint scheduling method and device based on source network load storage cooperation

Abstract

The invention relates to the technical field of power system dispatching, and particularly provides an electric vehicle and energy storage multi-layer joint dispatching method and device based on source network load storage collaboration, and the method comprises the steps: solving a pre-constructed upper-layer optimization model, and obtaining a load curve; substituting the load curve into a pre-constructed middle-layer optimization model and solving to obtain a source storage scheduling scheme and a power flow result; substituting the source storage scheduling scheme and the power flow result into a pre-constructed lower-layer optimization model and solving to obtain power flow distribution of the power distribution network; and obtaining a dispatching scheme of the power distribution network based on the power flow distribution of the power distribution network. According to the technical scheme provided by the invention, consumption of renewable energy sources such as wind power and photovoltaic energy can be promoted, and energy resource allocation is optimized; the power distribution network loss is reduced; and the ability of the power grid to cope with complex scenes is enhanced.

Description

Technical Field

[0001] This invention relates to the field of power system dispatching technology, specifically to a multi-layer joint dispatching method and device for electric vehicles and energy storage based on source-grid-load-storage coordination. Background Technology Against the backdrop of the power system's accelerated transformation towards low-carbon and flexible power, the installed capacity of renewable energy sources such as wind power and photovoltaics has increased significantly. However, the strong volatility of renewable energy output and the randomness of load demand have led to phenomena such as a surge in grid transmission losses during peak hours and a rise in wind and solar curtailment rates during off-peak hours. The traditional model relying on power source-side regulation is no longer suitable for the operational needs of the new power system due to its slow response and insufficient economic efficiency.

[0002] Electric vehicles (EVs), as flexible resources combining load and energy storage attributes, offer a new path for distribution network optimization through large-scale integration. However, disorderly charging can increase distribution network losses by 15%-25%, and existing research on orderly scheduling has limitations: First, it analyzes demand response (DR), EV scheduling, or energy storage system (ESS) control in isolation, lacking a coordinated optimization framework of "price-compensation-ESS"; second, the battery loss compensation mechanism for EV discharge is crudely designed, and there is insufficient incentive for users to participate in deep peak shaving; third, it does not coordinate a multi-objective collaborative mechanism that integrates load-side fluctuation suppression, source-storage-side economics, and network-side loss optimization. Furthermore, demand response mechanisms often rely on a single electricity price signal, failing to fully consider the nonlinear response characteristics of users to electricity prices, and the complementary scheduling potential of ESS and EVs has not been fully explored, making it difficult to achieve economical, low-carbon, and efficient operation of the distribution network. Summary of the Invention

[0003] To overcome the above-mentioned shortcomings, this invention proposes a multi-layer joint scheduling method and device for electric vehicles and energy storage based on source-grid-load-storage coordination.

[0004] Firstly, a multi-layer joint scheduling method for electric vehicles and energy storage based on source-grid-load-storage coordination is provided, wherein the multi-layer joint scheduling method for electric vehicles and energy storage based on source-grid-load-storage coordination includes: Solve the pre-built upper-level optimization model to obtain the load curve; Substitute the load curve into the pre-built mid-level optimization model and solve it to obtain the source-storage scheduling scheme and power flow results; Substitute the source-storage scheduling scheme and power flow results into the pre-constructed lower-level optimization model and solve it to obtain the power flow distribution of the distribution network; Based on the power flow distribution of the distribution network, a dispatching scheme for the distribution network is obtained.

[0005] Preferably, the pre-constructed upper-level optimization model includes: a first objective function and a first constraint condition with the goal of minimizing net load fluctuation.

[0006] Furthermore, the first objective function is as follows:

[0007] In the above formula, For net load fluctuations, Let be the electrical load at time t. To provide power to the wind turbine at time t, For the photovoltaic output at time t, To optimize the cycle.

[0008] Furthermore, the first constraint condition is as follows:

[0009]

[0010]

[0011]

[0012] In the above formula, α represents the upper limit of load response change at each time point, and β represents the upper limit of electricity price change at each time point. Let be the response power at time t. Let be the response electricity price at time t. Let be the initial electricity price at time t. Let t be the electricity price fluctuation at time t.

[0013] Furthermore, the pre-constructed mid-level optimization model includes a second objective function and a second constraint condition with the goal of minimizing the total cost of unit combination.

[0014] Furthermore, the second objective function is as follows:

[0015] In the above formula, This represents the total cost of unit combination. The cost of charging and discharging electric vehicles, For the maintenance costs of wind and light, To account for the cost of wind and solar power curtailment For electricity purchase costs, The charging and discharging costs of energy storage systems, To account for charging and discharging losses and costs, The basic cost of charging and discharging electric vehicles, For electric vehicle discharge compensation, , The operating and maintenance costs per unit power output for wind power and solar power, respectively. , These represent the actual power generation of wind power and photovoltaic power at time t, respectively. , These are the wind curtailment penalty costs per unit of electricity generated by wind power and solar power, respectively. , These are the projected power generation from wind power and solar power, respectively. , These represent the electricity price and power purchased at time t, respectively. , , , , Let $t$ be the electricity price at time $t$, the absolute value of the charging power, the absolute value of the discharging power, the charging efficiency, and the discharging efficiency. , These are the charging and discharging power of the energy storage system and the unit charging and discharging energy cost coefficient of the energy storage system, respectively. For time intervals, To optimize the cycle time.

[0016] Furthermore, the basic costs of charging and discharging the electric vehicle are as follows:

[0017] The electric vehicle discharge compensation is as follows:

[0018] In the above formula, , These represent the charging and discharging loads of electric vehicles at time t. The discharge battery loss coefficient for electric vehicles. The charging price for an electric vehicle at time t. , Let t be the total number of electric vehicles being charged and discharged. , Let be the average charging power and discharging power of the electric vehicle at time t, respectively. Let be the discharge price of the electric vehicle at time t. Let be the maximum discharge power of the electric vehicle, k be the discharge compensation price of the electric vehicle, and J be the discharge compensation growth rate of the electric vehicle.

[0019] Furthermore, the second constraint is as follows:

[0020]

[0021]

[0022]

[0023] In the above formula, , These are the maximum rated power for wind power and solar power, respectively. , Let be the maximum number of electric vehicles that can be charged and discharged at time t. Let t be the state of charge of the energy storage system. The state of charge of the energy storage system at time t-1 is... , These are the lower and upper limits of the state of charge of the energy storage system, respectively. For charging efficiency, For discharge efficiency, Let be the charging power at time t. Let be the discharge power at time t. Let be the response power at time t. , These are the upper limits for charging power and discharging power, respectively.

[0024] Furthermore, the pre-constructed mid-level optimization model includes a third objective function and a third constraint condition aimed at minimizing the distribution network loss.

[0025] Furthermore, the third objective function is as follows:

[0026] In the above formula, Let E be the active power loss of the distribution network, and E be the set of branches of the distribution network. Let be the current in branch ij at time t. Let be the resistance of branch ij. To optimize the cycle time.

[0027] Furthermore, the third constraint condition is as follows:

[0028]

[0029]

[0030] In the above formula, , , , , , For time t at node i, the active power purchased, the number of discharging vehicles, the charging and discharging power of electric vehicles, the active load, the number of charging vehicles, and the active power transmitted are given. , These are the average charging power and discharging power of electric vehicles, respectively. , , , For time t at node i, the reactive power purchased, reactive power discharge load, transmitted reactive power, and reactive power regulation power of electric vehicles are... , Let be the minimum and maximum voltage values ​​at node i. , Let be the minimum and maximum current values ​​of branch ij. , Let be the minimum and maximum values ​​of the transmission power of branch ij. The maximum number of vehicles that can charge or discharge at node i. Let be the current in branch ij at time t. Let be the current in branch ij at time t.

[0031] Secondly, a multi-layered joint dispatching device for electric vehicles and energy storage based on source-grid-load-storage coordination is provided, the device comprising: The first analysis module is used to solve the pre-built upper-level optimization model to obtain the load curve; The second analysis module is used to substitute the load curve into the pre-built mid-level optimization model and solve it to obtain the source-storage scheduling scheme and power flow results. The third analysis module is used to substitute the source-storage scheduling scheme and power flow results into the pre-built lower-level optimization model and solve it to obtain the power flow distribution of the distribution network. The scheduling module is used to obtain a scheduling scheme for the distribution network based on the power flow distribution of the distribution network.

[0032] Preferably, the pre-constructed upper-level optimization model includes: a first objective function and a first constraint condition with the goal of minimizing net load fluctuation.

[0033] Furthermore, the first objective function is as follows:

[0034] In the above formula, For net load fluctuations, Let be the electrical load at time t. To provide power to the wind turbine at time t, For the photovoltaic output at time t, To optimize the cycle.

[0035] Furthermore, the first constraint condition is as follows:

[0036]

[0037]

[0038]

[0039] In the above formula, α represents the upper limit of load response change at each time point, and β represents the upper limit of electricity price change at each time point. Let be the response power at time t. Let be the response electricity price at time t. Let be the initial electricity price at time t. Let t be the electricity price fluctuation at time t.

[0040] Furthermore, the pre-constructed mid-level optimization model includes a second objective function and a second constraint condition with the goal of minimizing the total cost of unit combination.

[0041] Furthermore, the second objective function is as follows:

[0042] In the above formula, This represents the total cost of unit combination. The cost of charging and discharging electric vehicles, For the maintenance costs of wind and light, To account for the cost of wind and solar power curtailment For electricity purchase costs, The charging and discharging costs of energy storage systems, To account for charging and discharging losses and costs, The basic cost of charging and discharging electric vehicles, For electric vehicle discharge compensation, , The operating and maintenance costs per unit power output for wind power and solar power, respectively. , These represent the actual power generation of wind power and photovoltaic power at time t, respectively. , These are the wind curtailment penalty costs per unit of electricity generated by wind power and solar power, respectively. , These are the projected power generation from wind power and solar power, respectively. , These represent the electricity price and power purchased at time t, respectively. , , , , Let $t$ be the electricity price at time $t$, the absolute value of the charging power, the absolute value of the discharging power, the charging efficiency, and the discharging efficiency. , These are the charging and discharging power of the energy storage system and the unit charging and discharging energy cost coefficient of the energy storage system, respectively. For time intervals, To optimize the cycle time.

[0043] Furthermore, the basic costs of charging and discharging the electric vehicle are as follows:

[0044] The electric vehicle discharge compensation is as follows:

[0045] In the above formula, , These represent the charging and discharging loads of electric vehicles at time t. The discharge battery loss coefficient for electric vehicles. The charging price for an electric vehicle at time t. , Let t be the total number of electric vehicles being charged and discharged. , Let be the average charging power and discharging power of the electric vehicle at time t, respectively. Let be the discharge price of the electric vehicle at time t. Let be the maximum discharge power of the electric vehicle, k be the discharge compensation price of the electric vehicle, and J be the discharge compensation growth rate of the electric vehicle.

[0046] Furthermore, the second constraint is as follows:

[0047]

[0048]

[0049]

[0050] In the above formula, , These are the maximum rated power for wind power and solar power, respectively. , Let be the maximum number of electric vehicles that can be charged and discharged at time t. Let t be the state of charge of the energy storage system. The state of charge of the energy storage system at time t-1 is... , These are the lower and upper limits of the state of charge of the energy storage system, respectively. For charging efficiency, For discharge efficiency, Let be the charging power at time t. Let be the discharge power at time t. Let be the response power at time t. , These are the upper limits for charging power and discharging power, respectively.

[0051] Furthermore, the pre-constructed mid-level optimization model includes a third objective function and a third constraint condition aimed at minimizing the distribution network loss.

[0052] Furthermore, the third objective function is as follows:

[0053] In the above formula, Let E be the active power loss of the distribution network, and E be the set of branches of the distribution network. Let be the current in branch ij at time t. Let be the resistance of branch ij. To optimize the cycle time.

[0054] Furthermore, the third constraint condition is as follows:

[0055]

[0056]

[0057] In the above formula, , , , , , For time t at node i, the active power purchased, the number of discharging vehicles, the charging and discharging power of electric vehicles, the active load, the number of charging vehicles, and the active power transmitted are given. , These are the average charging power and discharging power of electric vehicles, respectively. , , , For time t at node i, the reactive power purchased, reactive power discharge load, transmitted reactive power, and reactive power regulation power of electric vehicles are... , Let be the minimum and maximum voltage values ​​at node i. , Let be the minimum and maximum current values ​​of branch ij. , Let be the minimum and maximum values ​​of the transmission power of branch ij. The maximum number of vehicles that can charge or discharge at node i. Let be the current in branch ij at time t. Let be the current in branch ij at time t.

[0058] Thirdly, a computer device is provided, comprising: one or more processors; The processor is used to execute one or more programs; When the one or more programs are executed by the one or more processors, the multi-layer joint scheduling method for electric vehicles and energy storage based on source-grid-load-storage coordination is implemented.

[0059] Fourthly, a computer-readable storage medium is provided, on which a computer program is stored, wherein when the computer program is executed, the multi-layer joint scheduling method for electric vehicles and energy storage based on source-grid-load-storage coordination is implemented.

[0060] The above-described technical solutions of the present invention have at least one or more of the following beneficial effects: This invention provides a multi-layer joint scheduling method and device for electric vehicles and energy storage based on source-grid-load-storage coordination, comprising: solving a pre-constructed upper-layer optimization model to obtain a load curve; substituting the load curve into a pre-constructed middle-layer optimization model and solving it to obtain a source-storage scheduling scheme and power flow results; substituting the source-storage scheduling scheme and power flow results into a pre-constructed lower-layer optimization model and solving it to obtain the power flow distribution of the distribution network; and obtaining a scheduling scheme for the distribution network based on the power flow distribution of the distribution network. The technical solution provided by this invention can improve grid stability, reduce distribution network losses, and enhance the grid's ability to cope with complex scenarios. Specifically: This invention improves stability, reduces losses, and enhances resilience at the power grid operation level. After the complete process is implemented, the upper-level optimization of net load fluctuations can smooth the load curve and reduce peak-to-valley differences, significantly improving the stability and reliability of power grid operation; the lower-level optimization of power flow distribution can reduce power losses in the distribution network and save on power grid operating costs; at the same time, the hierarchical constraints and coordination mechanisms enable the power grid to have stronger regulation and response capabilities when facing complex scenarios such as extreme weather and renewable energy fluctuations, ensuring the safe operation of the power grid.

[0061] This invention promotes the absorption of renewable energy and optimizes resource allocation at the energy utilization level. By leveraging the orderly charging and discharging and demand response mechanism of electric vehicles, it can effectively absorb renewable energy sources such as wind power and photovoltaics, alleviate their intermittency and volatility issues, and promote the transformation of the energy structure towards a low-carbon model. Through global scheduling of "source-grid-load-storage," it achieves the rational allocation and efficient utilization of power sources, grids, loads, and energy storage resources in the power system, reducing energy waste and improving overall energy efficiency. Attached Figure Description

[0062] Figure 1 This is a schematic diagram of the main steps of the multi-layer joint scheduling method for electric vehicles and energy storage based on source-grid-load-storage coordination according to an embodiment of the present invention. Detailed Implementation

[0063] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0064] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0065] Example 1 See appendix Figure 1 , Figure 1 This is a schematic flowchart illustrating the main steps of a multi-layered joint scheduling method for electric vehicles and energy storage based on source-grid-load-storage coordination, according to an embodiment of the present invention. Figure 1 As shown, the multi-layer joint scheduling method for electric vehicles and energy storage based on source-grid-load-storage coordination in this embodiment of the invention mainly includes the following steps: Step S101: Solve the pre-built upper-level optimization model to obtain the load curve; Step S102: Substitute the load curve into the pre-constructed mid-level optimization model and solve it to obtain the source-storage scheduling scheme and power flow results; Step S103: Substitute the source-storage scheduling scheme and power flow results into the pre-built lower-level optimization model and solve it to obtain the power flow distribution of the distribution network; Step S104: Based on the power flow distribution of the distribution network, obtain the dispatching scheme of the distribution network.

[0066] In this embodiment, the pre-built upper-level optimization model includes a first objective function and a first constraint condition with the goal of minimizing net load fluctuation.

[0067] In one implementation, the first objective function is as follows:

[0068] In the above formula, For net load fluctuations, Let be the electrical load at time t. To provide power to the wind turbine at time t, For the photovoltaic output at time t, To optimize the cycle.

[0069] In one implementation, the first constraint condition is as follows:

[0070]

[0071]

[0072]

[0073] In the above formula, α represents the upper limit of load response change at each time point, and β represents the upper limit of electricity price change at each time point. Let be the response power at time t. Let be the response electricity price at time t. Let be the initial electricity price at time t. Let t be the electricity price fluctuation at time t.

[0074] In one implementation, the pre-built mid-level optimization model includes a second objective function and a second constraint condition aimed at minimizing the total cost of unit combination.

[0075] In one implementation, the second objective function is as follows:

[0076] In the above formula, This represents the total cost of unit combination. The cost of charging and discharging electric vehicles, For the maintenance costs of wind and light, To account for the cost of wind and solar power curtailment For electricity purchase costs, The charging and discharging costs of energy storage systems, To account for charging and discharging losses and costs, The basic cost of charging and discharging electric vehicles, For electric vehicle discharge compensation, , The operating and maintenance costs per unit power output for wind power and solar power, respectively. , These represent the actual power generation of wind power and photovoltaic power at time t, respectively. , These are the wind curtailment penalty costs per unit of electricity generated by wind power and solar power, respectively. , These are the projected power generation from wind power and solar power, respectively. , These represent the electricity price and power purchased at time t, respectively. , , , , Let $t$ be the electricity price at time $t$, the absolute value of the charging power, the absolute value of the discharging power, the charging efficiency, and the discharging efficiency. , These are the charging and discharging power of the energy storage system and the unit charging and discharging energy cost coefficient of the energy storage system, respectively. For time intervals, To optimize the cycle time.

[0077] In one implementation, the basic cost of charging and discharging the electric vehicle is as follows:

[0078] The electric vehicle discharge compensation is as follows:

[0079] In the above formula, , These represent the charging and discharging loads of electric vehicles at time t. The discharge battery loss coefficient for electric vehicles. The charging price for an electric vehicle at time t. , Let t be the total number of electric vehicles being charged and discharged. , Let be the average charging power and discharging power of the electric vehicle at time t, respectively. Let be the discharge price of the electric vehicle at time t. Let be the maximum discharge power of the electric vehicle, k be the discharge compensation price of the electric vehicle, and J be the discharge compensation growth rate of the electric vehicle.

[0080] In one implementation, the second constraint is as follows:

[0081]

[0082]

[0083]

[0084] In the above formula, , These are the maximum rated power for wind power and solar power, respectively. , Let be the maximum number of electric vehicles that can be charged and discharged at time t. Let t be the state of charge of the energy storage system. The state of charge of the energy storage system at time t-1 is... , These are the lower and upper limits of the state of charge of the energy storage system, respectively. For charging efficiency, For discharge efficiency, Let be the charging power at time t. Let be the discharge power at time t. Let be the response power at time t. , These are the upper limits for charging power and discharging power, respectively.

[0085] In one implementation, the pre-built mid-level optimization model includes a third objective function and a third constraint condition aimed at minimizing the distribution network loss.

[0086] In one implementation, the third objective function is as follows:

[0087] In the above formula, Let E be the active power loss of the distribution network, and E be the set of branches of the distribution network. Let be the current in branch ij at time t. Let be the resistance of branch ij. To optimize the cycle time.

[0088] In one implementation, the third constraint is as follows:

[0089]

[0090]

[0091] In the above formula, , , , , , For time t at node i, the active power purchased, the number of discharging vehicles, the charging and discharging power of electric vehicles, the active load, the number of charging vehicles, and the active power transmitted are given. , These are the average charging power and discharging power of electric vehicles, respectively. , , , For time t at node i, the reactive power purchased, reactive power discharge load, transmitted reactive power, and reactive power regulation power of electric vehicles are... , Let be the minimum and maximum voltage values ​​at node i. , Let be the minimum and maximum current values ​​of branch ij. , Let be the minimum and maximum values ​​of the transmission power of branch ij. The maximum number of vehicles that can charge or discharge at node i. Let be the current in branch ij at time t. Let be the current in branch ij at time t.

[0092] In one specific implementation, the present invention provides a model solving method, as follows: Step 1: Mathematical Expression and Preprocessing of the Model Using the Yalmip toolbox, a three-stage optimization operation model considering the low-carbon characteristics of electric vehicles (EVs) is mathematically modeled, clearly describing the decision variables, constraints, and objective functions at each level (the upper level optimizes the load curve with the goal of minimizing net load fluctuation, the middle level optimizes source-storage side operation by integrating multiple types of costs, and the lower level optimizes power flow distribution with the goal of minimizing distribution network losses). At the same time, the nonlinear constraints in the model are linearized and approximated to adapt to the solution requirements of the Cplex solver.

[0093] Step 2: Solver Parameter Settings and Strategy Selection Using the Cplex solver, parameters such as solution time and optimal gap are set; strategies such as layered solution and warm start are selected to improve computational efficiency; for non-convex constraints, a second-order cone relaxation method is adopted to ensure that the solution results converge to the global optimum.

[0094] Step 3: Solve layer by layer sequentially Solve the problem sequentially in the order of "top layer → middle layer → bottom layer": Upper-level solution: With the goal of minimizing net load fluctuation, optimize the load curve and pass the optimized load curve to the middle level.

[0095] Mid-level solution: Based on the optimized load curve transmitted from the upper layer, and combined with various cost factors on the source and storage sides, the solution is obtained to obtain the source and storage scheduling scheme and power flow results, and then transmitted to the lower layer.

[0096] Lower-level solution: Based on the source-storage scheduling scheme and power flow results transmitted from the middle layer, the power flow distribution of the distribution network is optimized with the goal of minimizing distribution network losses. Ultimately, a three-layer coordinated optimization of "source-grid-load-storage" is achieved.

[0097] Step 4: Iterative optimization After completing the hierarchical solution from "upper layer → middle layer → lower layer", the optimization results of each layer and the whole are verified. If indicators such as upper layer net load fluctuation, middle layer comprehensive cost, and lower layer distribution network loss are found to be below expectations, or if there are deviations between actual operating data and model assumptions, the parameters in the model are adjusted. Then, the process returns to step 1, and the adjusted model is mathematically expressed and preprocessed using Yalmip. It is then solved again using Cplex according to the hierarchical solution strategy. This process is iterated until a globally optimal solution that meets the requirements of coordinated optimization of "source-grid-load-storage" is obtained.

[0098] Example 2 Based on the same inventive concept, this invention also provides a multi-layered joint dispatching device for electric vehicles and energy storage based on source-grid-load-storage coordination, the multi-layered joint dispatching device for electric vehicles and energy storage based on source-grid-load-storage coordination includes: The first analysis module is used to solve the pre-built upper-level optimization model to obtain the load curve; The second analysis module is used to substitute the load curve into the pre-built mid-level optimization model and solve it to obtain the source-storage scheduling scheme and power flow results. The third analysis module is used to substitute the source-storage scheduling scheme and power flow results into the pre-built lower-level optimization model and solve it to obtain the power flow distribution of the distribution network. The scheduling module is used to obtain a scheduling scheme for the distribution network based on the power flow distribution of the distribution network.

[0099] Preferably, the pre-constructed upper-level optimization model includes: a first objective function and a first constraint condition with the goal of minimizing net load fluctuation.

[0100] Furthermore, the first objective function is as follows:

[0101] In the above formula, For net load fluctuations, Let be the electrical load at time t. To provide power to the wind turbine at time t, For the photovoltaic output at time t, To optimize the cycle.

[0102] Furthermore, the first constraint condition is as follows: ,

[0103] ,

[0104] In the above formula, α represents the upper limit of load response change at each time point, and β represents the upper limit of electricity price change at each time point. Let be the response power at time t. Let be the response electricity price at time t. Let be the initial electricity price at time t. Let t be the electricity price fluctuation at time t.

[0105] Furthermore, the pre-constructed mid-level optimization model includes a second objective function and a second constraint condition with the goal of minimizing the total cost of unit combination.

[0106] Furthermore, the second objective function is as follows:

[0107] In the above formula, This represents the total cost of unit combination. The cost of charging and discharging electric vehicles, For the maintenance costs of wind and light, To account for the cost of wind and solar power curtailment For electricity purchase costs, The charging and discharging costs of energy storage systems, To account for charging and discharging losses and costs, The basic cost of charging and discharging electric vehicles, For electric vehicle discharge compensation, , The operating and maintenance costs per unit power output for wind power and solar power, respectively. , These represent the actual power generation of wind power and photovoltaic power at time t, respectively. , These are the wind curtailment penalty costs per unit of electricity generated by wind power and solar power, respectively. , These are the projected power generation from wind power and solar power, respectively. , These represent the electricity price and power purchased at time t, respectively. , , , , Let $t$ be the electricity price at time $t$, the absolute value of the charging power, the absolute value of the discharging power, the charging efficiency, and the discharging efficiency. , These are the charging and discharging power of the energy storage system and the unit charging and discharging energy cost coefficient of the energy storage system, respectively. For time intervals, To optimize the cycle time.

[0108] Furthermore, the basic costs of charging and discharging the electric vehicle are as follows:

[0109] The electric vehicle discharge compensation is as follows:

[0110] In the above formula, , These represent the charging and discharging loads of electric vehicles at time t. The discharge battery loss coefficient for electric vehicles. The charging price for an electric vehicle at time t. , Let t be the total number of electric vehicles being charged and discharged. , Let be the average charging power and discharging power of the electric vehicle at time t, respectively. Let be the discharge price of the electric vehicle at time t. Let be the maximum discharge power of the electric vehicle, k be the discharge compensation price of the electric vehicle, and J be the discharge compensation growth rate of the electric vehicle.

[0111] Furthermore, the second constraint is as follows:

[0112] ,

[0113]

[0114] In the above formula, , These are the maximum rated power for wind power and solar power, respectively. , Let be the maximum number of electric vehicles that can be charged and discharged at time t. Let t be the state of charge of the energy storage system. The state of charge of the energy storage system at time t-1 is... , These are the lower and upper limits of the state of charge of the energy storage system, respectively. For charging efficiency, For discharge efficiency, Let be the charging power at time t. Let be the discharge power at time t. Let be the response power at time t. , These are the upper limits for charging power and discharging power, respectively.

[0115] Furthermore, the pre-constructed mid-level optimization model includes a third objective function and a third constraint condition aimed at minimizing the distribution network loss.

[0116] Furthermore, the third objective function is as follows:

[0117] In the above formula, Let E be the active power loss of the distribution network, and E be the set of branches of the distribution network. Let be the current in branch ij at time t. Let be the resistance of branch ij. To optimize the cycle time.

[0118] Furthermore, the third constraint condition is as follows: , ,

[0119] In the above formula, , , , , , For time t at node i, the active power purchased, the number of discharging vehicles, the charging and discharging power of electric vehicles, the active load, the number of charging vehicles, and the active power transmitted are given. , These are the average charging power and discharging power of electric vehicles, respectively. , , , For time t at node i, the reactive power purchased, reactive power discharge load, transmitted reactive power, and reactive power regulation power of electric vehicles are... , Let be the minimum and maximum voltage values ​​at node i. , Let be the minimum and maximum current values ​​of branch ij. , Let be the minimum and maximum values ​​of the transmission power of branch ij. The maximum number of vehicles that can charge or discharge at node i. Let be the current in branch ij at time t. Let be the current in branch ij at time t.

[0120] Example 3 Based on the same inventive concept, this invention also provides a computer device, which includes a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the computer storage medium to achieve corresponding method flows or corresponding functions, thereby implementing the steps of the multi-layer joint scheduling method for electric vehicles and energy storage based on source-grid-load-storage coordination in the above embodiments.

[0121] Example 4 Based on the same inventive concept, this invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the steps of the multi-layer joint scheduling method for electric vehicles and energy storage based on source-grid-load-storage coordination in the above embodiments.

[0122] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0123] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0124] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0125] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0126] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A multi-layer joint scheduling method for electric vehicles and energy storage based on source-grid-load-storage coordination, characterized in that, The method includes: Solve the pre-built upper-level optimization model to obtain the load curve; Substitute the load curve into the pre-built mid-level optimization model and solve it to obtain the source-storage scheduling scheme and power flow results; Substitute the source-storage scheduling scheme and power flow results into the pre-constructed lower-level optimization model and solve it to obtain the power flow distribution of the distribution network; Based on the power flow distribution of the distribution network, a dispatching scheme for the distribution network is obtained.

2. The method as described in claim 1, characterized in that, The pre-built upper-level optimization model includes a first objective function and a first constraint condition with the goal of minimizing net load fluctuation.

3. The method as described in claim 2, characterized in that, The first objective function is as follows: In the above formula, For net load fluctuations, Let be the electrical load at time t. To provide power to the wind turbine at time t, For the photovoltaic output at time t, To optimize the cycle.

4. The method as described in claim 3, characterized in that, The first constraint is as follows: In the above formula, α represents the upper limit of load response change at each time point, and β represents the upper limit of electricity price change at each time point. Let be the response power at time t. Let be the response electricity price at time t. Let be the initial electricity price at time t. Let t be the electricity price fluctuation at time t.

5. The method as described in claim 2, characterized in that, The pre-constructed mid-level optimization model includes a second objective function and a second constraint condition, with the goal of minimizing the total cost of unit combination.

6. The method as described in claim 5, characterized in that, The second objective function is as follows: In the above formula, This represents the total cost of unit combination. The cost of charging and discharging electric vehicles, For the maintenance costs of wind and light, To account for the cost of wind and solar power curtailment For electricity purchase costs, The charging and discharging costs of energy storage systems, To account for charging and discharging losses and costs, The basic cost of charging and discharging electric vehicles, For electric vehicle discharge compensation, , The operating and maintenance costs per unit power output for wind power and solar power, respectively. , These represent the actual power generation of wind power and photovoltaic power at time t, respectively. , These are the wind curtailment penalty costs per unit of electricity generated by wind power and solar power, respectively. , These are the projected power generation from wind power and solar power, respectively. , These represent the electricity price and power purchased at time t, respectively. , , , , Let $t$ be the electricity price at time $t$, the absolute value of the charging power, the absolute value of the discharging power, the charging efficiency, and the discharging efficiency. , These are the charging and discharging power of the energy storage system and the unit charging and discharging energy cost coefficient of the energy storage system, respectively. For time intervals, To optimize the cycle time.

7. The method as described in claim 6, characterized in that, The basic costs of charging and discharging electric vehicles are as follows: The electric vehicle discharge compensation is as follows: In the above formula, , These represent the charging and discharging loads of electric vehicles at time t. The discharge battery loss coefficient for electric vehicles. The charging price for an electric vehicle at time t. , Let t be the total number of electric vehicles being charged and discharged. , Let be the average charging power and discharging power of the electric vehicle at time t, respectively. Let be the discharge price of the electric vehicle at time t. Let be the maximum discharge power of the electric vehicle, k be the discharge compensation price of the electric vehicle, and J be the discharge compensation growth rate of the electric vehicle.

8. The method as described in claim 7, characterized in that, The second constraint is as follows: In the above formula, , These are the maximum rated power for wind power and solar power, respectively. , Let be the maximum number of electric vehicles that can be charged and discharged at time t. Let t be the state of charge of the energy storage system. The state of charge of the energy storage system at time t-1 is... , These are the lower and upper limits of the state of charge of the energy storage system, respectively. For charging efficiency, For discharge efficiency, Let be the charging power at time t. Let be the discharge power at time t. Let be the response power at time t. , These are the upper limits for charging power and discharging power, respectively.

9. The method as described in claim 2, characterized in that, The pre-constructed mid-level optimization model includes a third objective function and a third constraint condition aimed at minimizing the distribution network loss.

10. The method as described in claim 9, characterized in that, The third objective function is as follows: In the above formula, Let E be the active power loss of the distribution network, and E be the set of branches of the distribution network. Let be the current in branch ij at time t. Let be the resistance of branch ij. To optimize the cycle time.

11. The method as described in claim 10, characterized in that, The third constraint is as follows: ， In the above formula, , , , , , For time t at node i, the active power purchased, the number of discharging vehicles, the charging and discharging power of electric vehicles, the active load, the number of charging vehicles, and the active power transmitted are given. , These are the average charging power and discharging power of electric vehicles, respectively. , , , For time t at node i, the reactive power purchased, reactive power discharge load, transmitted reactive power, and reactive power regulation power of electric vehicles are... , Let be the minimum and maximum voltage values ​​at node i. , Let be the minimum and maximum current values ​​of branch ij. , Let be the minimum and maximum values ​​of the transmission power of branch ij. The maximum number of vehicles that can charge or discharge at node i. Let be the current in branch ij at time t. Let be the current in branch ij at time t.

12. A multi-layer joint dispatching device for electric vehicles and energy storage based on source-grid-load-storage coordination, characterized in that, The device includes: The first analysis module is used to solve the pre-built upper-level optimization model to obtain the load curve; The second analysis module is used to substitute the load curve into the pre-built mid-level optimization model and solve it to obtain the source-storage scheduling scheme and power flow results. The third analysis module is used to substitute the source-storage scheduling scheme and power flow results into the pre-built lower-level optimization model and solve it to obtain the power flow distribution of the distribution network. The scheduling module is used to obtain a scheduling scheme for the distribution network based on the power flow distribution of the distribution network.

13. The apparatus as claimed in claim 12, characterized in that, The pre-built upper-level optimization model includes a first objective function and a first constraint condition with the goal of minimizing net load fluctuation.

14. The apparatus as claimed in claim 13, characterized in that, The first objective function is as follows: In the above formula, For net load fluctuations, Let be the electrical load at time t. To provide power to the wind turbine at time t, For the photovoltaic output at time t, To optimize the cycle.

15. The apparatus as claimed in claim 14, characterized in that, The first constraint is as follows: ， In the above formula, α represents the upper limit of load response change at each time point, and β represents the upper limit of electricity price change at each time point. Let be the response power at time t. Let be the response electricity price at time t. Let be the initial electricity price at time t. Let t be the electricity price fluctuation at time t.

16. The apparatus as claimed in claim 13, characterized in that, The pre-constructed mid-level optimization model includes a second objective function and a second constraint condition, with the goal of minimizing the total cost of unit combination.

17. The apparatus as claimed in claim 16, characterized in that, The second objective function is as follows: In the above formula, This represents the total cost of unit combination. The cost of charging and discharging electric vehicles, For the maintenance costs of wind and light, To account for the cost of wind and solar power curtailment For electricity purchase costs, The charging and discharging costs of energy storage systems, To account for charging and discharging losses and costs, The basic cost of charging and discharging electric vehicles, For electric vehicle discharge compensation, , The operating and maintenance costs per unit power output for wind power and solar power, respectively. , These represent the actual power generation of wind power and photovoltaic power at time t, respectively. , These are the wind curtailment penalty costs per unit of electricity generated by wind power and solar power, respectively. , These are the projected power generation from wind power and solar power, respectively. , These represent the electricity price and power purchased at time t, respectively. , , , , Let $t$ be the electricity price at time $t$, the absolute value of the charging power, the absolute value of the discharging power, the charging efficiency, and the discharging efficiency. , These are the charging and discharging power of the energy storage system and the unit charging and discharging energy cost coefficient of the energy storage system, respectively. For time intervals, To optimize the cycle time.

18. The apparatus as claimed in claim 17, characterized in that, The basic costs of charging and discharging electric vehicles are as follows: The electric vehicle discharge compensation is as follows: In the above formula, , These represent the charging and discharging loads of electric vehicles at time t. The discharge battery loss coefficient for electric vehicles. The charging price for an electric vehicle at time t. , Let t be the total number of electric vehicles being charged and discharged. , Let be the average charging power and discharging power of the electric vehicle at time t, respectively. Let be the discharge price of the electric vehicle at time t. Let be the maximum discharge power of the electric vehicle, k be the discharge compensation price of the electric vehicle, and J be the discharge compensation growth rate of the electric vehicle.

19. The apparatus as claimed in claim 18, characterized in that, The second constraint is as follows: ， In the above formula, , These are the maximum rated power for wind power and solar power, respectively. , Let be the maximum number of electric vehicles that can be charged and discharged at time t. Let t be the state of charge of the energy storage system. The state of charge of the energy storage system at time t-1 is... , These are the lower and upper limits of the state of charge of the energy storage system, respectively. For charging efficiency, For discharge efficiency, Let be the charging power at time t. Let be the discharge power at time t. Let be the response power at time t. , These are the upper limits for charging power and discharging power, respectively.

20. The apparatus as claimed in claim 13, characterized in that, The pre-constructed mid-level optimization model includes a third objective function and a third constraint condition aimed at minimizing the distribution network loss.

21. The apparatus as claimed in claim 20, characterized in that, The third objective function is as follows: In the above formula, Let E be the active power loss of the distribution network, and E be the set of branches of the distribution network. Let be the current in branch ij at time t. Let be the resistance of branch ij. To optimize the cycle time.

22. The apparatus as claimed in claim 21, characterized in that, The third constraint is as follows: ， In the above formula, , , , , , For time t at node i, the active power purchased, the number of discharging vehicles, the charging and discharging power of electric vehicles, the active load, the number of charging vehicles, and the active power transmitted are given. , These are the average charging power and discharging power of electric vehicles, respectively. , , , For time t at node i, the reactive power purchased, reactive power discharge load, transmitted reactive power, and reactive power regulation power of electric vehicles are... , Let be the minimum and maximum voltage values ​​at node i. , Let be the minimum and maximum current values ​​of branch ij. , Let be the minimum and maximum values ​​of the transmission power of branch ij. The maximum number of vehicles that can charge or discharge at node i. Let be the current in branch ij at time t. Let be the current in branch ij at time t.

23. A computer device, characterized in that, include: One or more processors; The processor is used to execute one or more programs; When the one or more programs are executed by the one or more processors, the multi-layer joint scheduling method for electric vehicles and energy storage based on source-grid-load-storage coordination as described in any one of claims 1 to 11 is implemented.

24. A computer-readable storage medium, characterized in that, It contains a computer program, which, when executed, implements the multi-layer joint scheduling method for electric vehicles and energy storage based on source-grid-load-storage coordination as described in any one of claims 1 to 11.

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