Energy scheduling method, platform and program product

Abstract

The invention discloses an energy scheduling method, platform and program product, and relates to the technical field of energy management, and the comprehensive energy scheduling method comprises the steps that an upper layer scheduling module determines a first scheduling scheme based on the energy information of each region and a preset global optimization target, and sends the first scheduling scheme to a lower layer execution module, the lower-layer execution module optimizes the charging and discharging plans of the electric vehicles in all the areas based on the received first scheduling scheme to obtain an optimization result and sends the optimization result to the upper-layer scheduling module, and the upper-layer scheduling module adjusts the first scheduling scheme based on the optimization result to obtain a second scheduling scheme. By means of upper and lower layer information transmission and iterative optimization, the method can combine the characteristics of the energy system of each region and the dynamic behavior of the electric vehicle to realize the optimal scheduling of the multi-region comprehensive energy system, thereby improving the utilization efficiency and economic benefit of the energy.

Description

Technical Field

[0001] The present application relates to the field of energy management technology, and in particular to energy scheduling methods, platforms and program products. Background Art

[0002] At present, the multi-regional integrated energy system improves the efficiency of energy utilization through the coordinated operation of power, heat and cooling systems. However, with the large-scale access of electric vehicles, the dynamic charging and discharging behavior of electric vehicles increases the complexity and uncertainty of energy system scheduling, making it difficult for the integrated energy scheduling system to optimize energy scheduling due to the charging and discharging behavior of electric vehicles, resulting in low energy utilization efficiency and economic benefits. Summary of the invention

[0003] The main purpose of this application is to provide an energy scheduling method, platform and program product, aiming to solve the technical problem that the comprehensive energy scheduling system is difficult to optimize energy scheduling due to the charging and discharging behavior of electric vehicles, resulting in low energy utilization efficiency and economic benefits.

[0004] To achieve the above objectives, the present application proposes a comprehensive energy scheduling method, which is applied to an upper scheduling module, and the method includes:

[0005] Determine a first scheduling scheme based on energy information of each region and a preset global optimization target, and send the first scheduling scheme to a lower-level execution module, so that the lower-level execution module optimizes the charging and discharging plans of electric vehicles in each region based on the first scheduling scheme, obtains optimization results, and sends the optimization results to a local location;

[0006] The first scheduling scheme is adjusted based on the optimization result to obtain a second scheduling scheme.

[0007] In one embodiment, the step of determining the first scheduling scheme based on the energy information of each region and a preset global optimization target includes:

[0008] Based on the energy information of each area, a first scheduling scheme is determined with the goal of minimizing the operating cost of each area, and the energy information of each area is adjusted based on the first scheduling scheme to obtain adjusted energy information;

[0009] The adjusted energy information is interacted based on an algorithm, and the first scheduling plan is adjusted based on the adjusted energy information and a preset equilibrium condition to obtain the first scheduling plan, wherein the first scheduling plan needs to balance the energy supply and energy demand of each region.

[0010] In one embodiment, the step of adjusting the first scheduling scheme based on the optimization result to obtain a second scheduling scheme includes:

[0011] When it is detected that the energy information of each region changes, or the optimization result is received, or the charging and discharging related information of the electric vehicle after the change is received, it is determined whether the third scheduling plan can meet the preset global optimization goal, and the third scheduling plan includes the first scheduling plan or the adjusted scheduling plan;

[0012] If not, the third scheduling scheme is adjusted to obtain an adjusted scheduling scheme, and the adjusted scheduling scheme is sent to the lower-layer execution module, so that the lower-layer execution module optimizes the charging and discharging plans of electric vehicles in each area based on the adjusted scheduling scheme, obtains optimization results, and sends the optimization results to the local area;

[0013] Otherwise, the third scheduling scheme is not adjusted, and the third scheduling scheme is used as the second scheduling scheme.

[0014] In one embodiment, after the step of adjusting the first scheduling scheme based on the optimization result to obtain the second scheduling scheme, the method further includes:

[0015] When it is detected that the difference between energy supply and energy demand reaches a preset threshold, if the energy supply of each area can meet the energy demand, the second scheduling plan is adjusted to change the user's energy use behavior so that the energy supply and energy demand of each area are balanced.

[0016] To achieve the above objectives, the present application also proposes a comprehensive energy scheduling method, which is applied to the lower-level execution module, and the method includes:

[0017] Receiving a first scheduling scheme sent by an upper scheduling module, wherein the first scheduling scheme is determined based on energy information of each region and a preset global optimization target;

[0018] Based on the first scheduling scheme, the charging and discharging plans of electric vehicles in each area are optimized respectively to obtain optimization results, and the optimization results are sent to the upper scheduling module so that the upper scheduling module can adjust the first scheduling scheme based on the optimization results to obtain a second scheduling scheme.

[0019] In one embodiment, the step of optimizing the charging and discharging plan of the electric vehicle based on the first scheduling scheme to obtain the optimization result includes:

[0020] Acquiring driving data of the electric vehicle, and determining a trip chain of the electric vehicle based on the driving data;

[0021] Based on the first scheduling scheme and the trip chain, the charging and discharging plans of the electric vehicles in each area are optimized respectively with the goal of minimizing the charging and discharging costs to obtain optimization results.

[0022] In one embodiment, the step of obtaining the driving data of the electric vehicle and determining the trip chain of the electric vehicle based on the driving data includes:

[0023] Determine the location, status, travel demand and driving condition of the electric vehicle based on the driving data;

[0024] Based on the position and the state, determining a driving path of the electric vehicle;

[0025] Determining a probability of the electric vehicle transitioning between different states based on the states;

[0026] determining a transition probability matrix based on the probabilities;

[0027] A trip chain of the electric vehicle is determined based on the driving path, the transition probability matrix, the travel demand, the driving condition and the preset charging and discharging power of the electric vehicle.

[0028] In one embodiment, the step of optimizing the charging and discharging plans of electric vehicles in each region based on the first scheduling scheme to obtain the optimization results includes:

[0029] When a change in the charging and discharging related information of the electric vehicle is detected, or the second scheduling scheme is received, judging whether it is necessary to optimize the charging and discharging plan of the electric vehicle in each area based on the received energy supply and energy demand of each area, wherein the second scheduling scheme includes the first scheduling scheme and the adjusted scheduling scheme sent by the upper scheduling module;

[0030] If necessary, the charging and discharging plan of the electric vehicles in each area is optimized to obtain the optimization result, and the optimization result is sent to the upper scheduling module;

[0031] Otherwise, the changed electric vehicle charging and discharging related information is sent to the upper scheduling module.

[0032] In addition, to achieve the above purpose, the present application also proposes an integrated energy dispatching platform, characterized in that the integrated energy dispatching platform includes:

[0033] The upper scheduling module is used to determine a first scheduling scheme based on the acquired energy information of each area and a preset global optimization target, and send the first scheduling scheme to the lower execution module, so that the lower execution module can optimize the charging and discharging plans of electric vehicles in each area based on the first scheduling scheme, obtain optimization results, and send the optimization results to the local, adjust the first scheduling scheme based on the optimization results, and obtain a second scheduling scheme;

[0034] The lower-level execution module is used to receive a first scheduling scheme sent by an upper-level scheduling module, wherein the first scheduling scheme is determined based on the energy information of each region and a preset global optimization target, and based on the first scheduling scheme, the charging and discharging plans of electric vehicles in each region are optimized respectively to obtain optimization results, and the optimization results are sent to the upper-level scheduling module, so that the upper-level scheduling module can adjust the first scheduling scheme based on the optimization results to obtain a second scheduling scheme.

[0035] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the steps of the comprehensive energy scheduling method described above.

[0036] One or more technical solutions proposed in this application have at least the following technical effects:

[0037] When multi-regional energy integrated energy optimization scheduling is required, the upper-level scheduling module obtains a first scheduling scheme based on the energy information of each region and the preset global optimization target and sends it to the lower-level execution module; the lower-level execution module optimizes the charging and discharging plans of electric vehicles in each region based on the first scheduling scheme, obtains optimization results, and sends the optimization results to the upper-level scheduling module; the upper-level scheduling module adjusts the optimization scheme based on the received optimization results. Therefore, the present application uses the upper and lower layers to transmit information and optimize the whole and the part respectively, so that the integrated energy scheduling method proposed in the present application can realize the optimized scheduling of the multi-regional integrated energy system based on the characteristics of the energy system in each region and the dynamic behavior of electric vehicles through the above-mentioned two-layer structure, thereby improving the energy utilization efficiency and economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0039] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0040] Figure 1 A flowchart of the first embodiment of the comprehensive energy scheduling method of the present application is provided;

[0041] Figure 2 This is a schematic diagram of the flow chart of the electric / heat / cold integrated energy system model for this application;

[0042] Figure 3 A flow chart of the second embodiment of the comprehensive energy scheduling method of the present application;

[0043] Figure 4 A schematic diagram of the coordinated optimization dispatch process for a multi-region integrated energy system including electric vehicles;

[0044] Figure 5 A brief flowchart of the comprehensive energy scheduling method provided in Example 3 of the present application;

[0045] Figure 6 A brief flowchart of the comprehensive energy scheduling method provided in Example 4 of the present application.

[0046] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0047] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0048] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0049] Based on this, the embodiment of the present application provides a comprehensive energy scheduling method, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the comprehensive energy scheduling method of the present application.

[0050] In this embodiment, the comprehensive energy scheduling method includes steps S10 to S20:

[0051] Step S10, determining a first scheduling scheme based on energy information of each region and a preset global optimization target, and sending the first scheduling scheme to a lower-level execution module, so that the lower-level execution module optimizes the charging and discharging plans of electric vehicles in each region based on the first scheduling scheme, obtains an optimization result, and sends the optimization result to a local location;

[0052] It should be noted that the execution subject is the upper scheduling module, and the comprehensive energy scheduling method is applied to the upper scheduling module. The main application scenario is the scheduling of multi-regional comprehensive energy systems. The multi-regional comprehensive energy system is an energy network that improves the overall energy utilization efficiency by cooperating with different types of energy systems. Different types of energy systems include power, heat and cold systems. The power system includes power generation equipment (such as natural gas turbines, wind power, photovoltaics), energy storage equipment (such as battery energy storage) and distribution networks. The thermal system includes cogeneration units, gas boilers, heat pumps, heating networks and thermal energy storage devices. The cooling system includes electric chillers, absorption chillers, cold networks and cold energy storage devices.

[0053] The regional integrated energy systems are connected through power and heat networks, referring to Figure 2 , Figure 2 It is a comprehensive energy system model of electricity / heat / cold. For example, the gas turbine is connected to the waste heat boiler through the heat energy flow, and the gas boiler is connected to the absorption chiller through the cold power flow. These systems realize energy exchange and sharing through the power network and the thermal network, so as to improve energy utilization efficiency.

[0054] The integrated energy dispatching system is a system used to dispatch energy in various regions. The dispatching includes receiving energy information from various regions, determining and adjusting energy dispatching plans, and executing energy dispatching plans. The integrated energy dispatching system includes an upper-level dispatching module and a lower-level execution module. These two modules coordinate and execute operations in a unified manner through the integrated energy dispatching system.

[0055] The upper scheduling module is used to obtain energy information of each region, and determine the energy scheduling plan based on the energy information and the preset global optimization target. It also receives the optimization results sent by the lower execution module, and adjusts the energy scheduling plan based on the energy information, the preset global optimization target and the optimization results. Energy information mainly includes information such as energy demand, energy price, and environmental constraints of energy system operation in each region. In this embodiment, the electricity, heat and cooling demand of each region can be predicted through historical data and real-time monitoring, so as to estimate the energy demand information.

[0056] The energy dispatching scheme includes energy dispatching parameters and energy dispatching instructions. Energy dispatching parameters include parameters such as energy price signals and energy demand. Energy dispatching parameters can be used to set the energy price signals and energy demand of each region to provide support for energy dispatching operations. Energy dispatching instructions include information such as the type of energy to be dispatched, the region and system used to provide energy, and the region and system used to receive energy. Energy dispatching parameters and energy dispatching instructions can be used to reasonably dispatch energy in multiple regions.

[0057] In a feasible real-time manner, after the step of adjusting the first scheduling solution based on the optimization result to obtain the second scheduling solution, the following may also be performed:

[0058] When it is detected that the difference between energy supply and energy demand reaches a preset threshold, if the energy supply of each area can meet the energy demand, the second scheduling plan is adjusted to change the user's energy use behavior so that the energy supply and energy demand of each area are balanced.

[0059] It should be noted that the upper scheduling module adjusts the second scheduling scheme in the form of electricity price, electricity usage strategy and other methods.

[0060] It is understandable that when there is a sudden increase in energy demand or a sudden decrease in energy supply in each area, so that the difference between energy supply and energy demand reaches a preset threshold, the upper-level scheduling module needs to balance the energy supply and demand of the area in a short time. However, the usual iterative optimization method needs to transfer information between the upper and lower layers, and requires multiple iterations until convergence, so it is difficult to adjust the supply and demand of each area in a short time. The upper-level scheduling module directly changes the electricity price and electricity strategy in the second scheduling plan. Since changes to the electricity price and electricity strategy will affect the user's energy consumption behavior, the load on the demand side can be reduced, thereby helping to balance the supply and demand of energy.

[0061] Step S20: adjusting the first scheduling scheme based on the optimization result to obtain a second scheduling scheme.

[0062] It is understandable that after the lower-level execution module optimizes the charging and discharging plan of the electric vehicle, the actual charging and discharging plan of the electric vehicle changes, and the energy demand and energy supply of each region also change accordingly. The upper layer adjusts the first scheduling plan by receiving the optimization results and the latest energy information of each region so that it can meet the preset global optimization goal. The final adjusted scheduling plan is the second scheduling plan.

[0063] Specifically, there are three regions A, B, and C. The integrated energy systems of the three regions are operated by the power, heat, and cooling systems, and the systems are connected through the power network and the heat network. The upper scheduling module determines the first scheduling plan based on the power prices a, b, c, energy demands x, y, z, and the environment in which the energy system operates, and sends it to the lower execution module. The power price parameters in the first scheduling plan are a1, b1, c1, and the energy demands are x1, y1, z1.

[0064] The lower-level execution module receives and executes the first scheduling scheme sent by the upper layer to perform energy scheduling and change energy parameters. At this time, the power price parameters become a1, b1, c1, and the energy demand becomes x1, y1, z1. The lower-level execution module obtains information such as power price and energy demand from the first scheduling scheme, and also obtains real-time information such as the electric vehicle's itinerary, charging demand, and charging station capacity, and adjusts the electric vehicle's charging and discharging plan based on this information. The lower-level execution module feeds back the adjusted electric vehicle charging and discharging plan, state changes, and energy usage to the upper-level scheduling module. The upper-level scheduling module adjusts the first scheduling scheme based on the received information, such as adjusting the scheduling instructions, and making the power price parameters become a2, b2, c2, and the energy demand become x2, y2, z2. Finally, a scheduling scheme that meets the preset global optimization conditions is obtained, which is used as the second scheduling scheme.

[0065] In a feasible implementation manner, the step of adjusting the first scheduling scheme based on the optimization result to obtain the second scheduling scheme may also be:

[0066] When it is detected that the energy information of each region has changed, or the optimization result is received, or the charging and discharging related information of the electric vehicle after the change is received, it is determined whether the third scheduling plan can meet the preset global optimization goal. The third scheduling plan includes the first scheduling plan or the adjusted scheduling plan. If not, the third scheduling plan is adjusted to obtain the adjusted scheduling plan, and the adjusted scheduling plan is sent to the lower-level execution module, so that the lower-level execution module can optimize the charging and discharging plans of the electric vehicles in each region based on the adjusted scheduling plan, obtain the optimization result, and send the optimization result to the local. Otherwise, the third scheduling plan is not adjusted, and the third scheduling plan is used as the second scheduling plan.

[0067] It should be noted that the energy information of each region obtained by the upper-level scheduling module is the energy demand, transaction price, environmental constraints and other information related to the energy scheduling of each region.

[0068] It is understandable that there are three types of information received by the upper scheduling module, namely, the energy information of each region obtained by the upper scheduling module, the optimization results fed back by the lower execution module, and the electric vehicle charging and discharging related information fed back by the lower execution module. Since the above three types of information indicate that some factors in the integrated energy system of each region have changed, the plan that was originally able to achieve the global optimization goal of maximizing the global comprehensive profit may no longer be able to maximize the global profit. Therefore, the upper scheduling module needs to judge whether the current plan can achieve the preset global optimization goal based on the changed factors.

[0069] If the current solution can still achieve the global optimization goal, the solution will not be adjusted and will be used as the second scheduling solution. If the current solution cannot achieve the global optimization goal, the solution will be re-adjusted and the optimized solution will be sent to the lower-level execution module, which will receive the optimization results and judge again whether the current solution can achieve the global optimization goal. The upper-level scheduling module will repeat the judgment and adjustment steps until the upper-level scheduling module determines that the current solution does not need to be adjusted, and finally obtains the second scheduling solution.

[0070] Specifically, in the three areas A, B, and C, the concentrated charging behavior of electric vehicles in area B causes a sudden increase in the energy demand in area B. At this time, the energy supply in area B cannot meet the energy demand. At this time, the lower-level execution module does not optimize the charging and discharging plan of the electric vehicle, but feeds back the changed energy demand information to the upper-level scheduling module. Based on the received energy demand information, the upper-level scheduling module determines that the current scheduling plan needs to be adjusted, and sends the adjusted scheduling plan to the lower-level execution module. The lower-level execution module receives the adjusted scheduling plan and adjusts the charging and discharging plan of the electric vehicle according to the received plan, and feeds back the optimization result to the upper-level scheduling module. Based on the received optimization result, the upper-level scheduling module determines that the third scheduling plan does not need to be adjusted. At this time, the iteration ends, and the current scheduling plan is used as the second scheduling plan.

[0071] In summary, when it is necessary to perform multi-regional energy comprehensive energy optimization scheduling, the upper scheduling module obtains the first scheduling scheme based on the energy demand, transaction price, environmental conditions and other factors of each region and the preset global optimization goal and sends it to the lower execution module, so that the lower execution module can obtain the optimization results including the electric vehicle charging and discharging plan and energy usage based on the first scheduling scheme and send it to the upper scheduling module. The upper scheduling module adjusts the first scheduling scheme based on the changing energy information, electric vehicle charging and discharging information or the received optimization results. After repeated iterative optimization, the second scheduling scheme is finally obtained, and the energy supply and demand of each region is helped to balance by responding to user needs. Therefore, this embodiment uses this method of upper and lower layer information interaction and iterative optimization, combined with the user demand response method, so that this embodiment can realize the optimal scheduling of the multi-regional comprehensive energy system based on the characteristics of the energy system of each region and the dynamic behavior of the electric vehicle, thereby improving the energy utilization efficiency and economic benefits.

[0072] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can refer to the above introduction, and will not be repeated later. Figure 3, step S10, determining a first scheduling scheme based on the energy information of each region and a preset global optimization target, including steps S11 to S12:

[0073] Step S11, based on the energy information of each area, determining a first scheduling scheme with the goal of minimizing the operating cost of each area, and adjusting the energy information of each area based on the first scheduling scheme to obtain adjusted energy information;

[0074] It should be noted that the preset global optimization goal is to maximize the comprehensive profit of each region. To achieve this global optimization goal, it is necessary to minimize the operating cost of the comprehensive energy system of each region and maximize the comprehensive benefits of each region. The first scheduling plan is a plan that minimizes the operating cost of the comprehensive energy system of each region. The minimization of the operating cost of each region is to minimize the sum of the operating cost of the energy equipment in each region, the energy transaction cost, the charging and discharging cost of electric vehicles, and the carbon emission cost. The specific expression is:

[0075]

[0076] Cost information includes the operating cost of each regional energy equipment C OM 、Energy transaction costs between regions C ex 、Charging and discharging cost of electric vehicles C EV and carbon emission costs The formula expresses that the sum of the above costs of the 1st to Mth energy devices is the smallest. The operating costs of energy equipment include energy consumption costs (electricity costs of power systems, fuel costs of thermal systems), maintenance and repair costs, labor costs and other costs. The energy transaction costs between regions include energy transmission costs, transaction costs and other costs. The charging and discharging costs of electric vehicles include energy loss costs during the charging and discharging process and other costs. The carbon emission costs include the costs generated in the process of reducing carbon emissions.

[0077] Step S12, interacting with the adjusted energy information based on an algorithm, and adjusting the first scheduling plan based on the adjusted energy information and a preset equilibrium condition to obtain the first scheduling plan, wherein the first scheduling plan needs to balance the energy supply and energy demand of each region.

[0078] It should be noted that the first scheduling plan includes energy sharing, interaction and trading plans among regions. The energy information shared by each region is energy price information, energy demand information and other information. The energy information of each region can be shared and interacted through the alternating direction multiplier method. The alternating direction multiplier method is a method suitable for solving large-scale optimization problems. It decomposes a complex optimization problem into several smaller sub-problems and solves these sub-problems by alternating and iterating the variables. In this embodiment, the sub-problem is to maximize the comprehensive profit of the integrated energy system of each region, and the variables that need to be updated alternately are energy demand, price signals and other variables.

[0079] The supply and demand balance means that the supply, demand and loss of energy are the same. The specific expression is:

[0080]

[0081] Among them, P i,t is the power generation of the ith device at time t, D j,t is the demand of the jth load at time t, P loss,t is the energy loss at time t, and the formula expresses that the power generation of the 1st to Nth devices at time t is equal to the demand of the 1st to Mth loads at time t plus the energy loss at time t.

[0082] The preset equilibrium condition is an equilibrium condition such as Nash equilibrium. Nash equilibrium means that when multiple parties are playing games, each party's plan is the best plan for other parties. Therefore, no party will change its plan to achieve equilibrium. Figure 4 , Figure 4 The invention provides a collaborative optimization scheduling for a multi-region integrated energy system including electric vehicles, which includes a Nash negotiation energy sharing method. In this embodiment, since there is information sharing among the multi-regional energy integrated systems, the profit distribution of the electricity price information of each region can be carried out based on the Nash equilibrium, and a specific scheduling plan can be obtained. The obtained plan is the best for the respective interests of each region, thereby achieving the best distribution of the comprehensive interests of each region. Since the first scheduling plan has minimized the operating cost of the integrated energy system of each region, the distribution based on the Nash equilibrium maximizes the distribution of the comprehensive interests of each region, thereby ultimately maximizing the comprehensive profit of each region.

[0083] It can be understood that the first scheduling scheme has minimized the operating cost of the integrated energy system in each region, and on this basis, each region shares information such as energy prices and energy demand through the upper scheduling module. The upper scheduling module adjusts the first scheduling scheme based on the Nash equilibrium on the basis of sharing the energy information of each region, and obtains the first scheduling scheme that satisfies the supply and demand balance and maximizes the comprehensive profit of each region.

[0084] In this embodiment, after obtaining the first scheduling scheme, the optimal operating state of each device is determined by optimizing the energy supply using a mixed integer linear programming model. The mixed integer linear programming model is a mathematical programming model that can be used to better handle multi-dimensional and complex optimization problems. The optimal operating state of each device refers to the optimal operating state of each device in the power system, thermal system, and cooling system in the multi-regional integrated energy system, so that the overall system reaches the optimal operating state.

[0085] Specifically, there are three regions A, B, and C. The upper-level scheduling module minimizes the sum of the operating costs of the energy equipment, energy transaction costs, electric vehicle charging and discharging costs, and carbon emission costs of each region, and determines that the plan at this time is the first scheduling plan. The upper-level scheduling module adjusts the energy information of the three regions A, B, and C based on the first scheduling plan to obtain the adjusted energy information. After that, the energy information of the three regions A, B, and C is interacted through the alternating direction multiplier method to maximize the profits of the three regions A, B, and C. At this time, the electricity prices of the three regions A, B, and C are a, b, and c, respectively. After that, the upper-level scheduling module allocates the electricity prices of A, B, and C based on Nash equilibrium, so that the electricity prices of the three regions A, B, and C are a1, b1, and c1. The scheduling plan at this time is the first scheduling plan, which maximizes the comprehensive profits of multiple regions.

[0086] In summary, when it is necessary to optimize the scheduling of a multi-region integrated energy system, the upper scheduling module determines the first scheduling scheme based on the information such as the energy price and energy demand of each region, with the goal of minimizing the operating cost of each region, and adjusts the energy information in each region based on the first scheduling scheme to obtain the adjusted energy information; after that, the upper scheduling module interacts with the energy information such as the adjusted energy price and energy demand of each region through the alternating direction multiplier method, and adjusts the first scheduling scheme based on the adjusted energy information and the preset equilibrium condition to obtain the first scheduling scheme, which includes parameters such as energy price and energy demand, as well as energy scheduling instructions, energy trading instructions, etc., which can maximize the overall profit and balance the energy supply and demand of each region. Therefore, this embodiment can realize the coordinated optimization among multiple regional integrated energy systems, improve the overall energy utilization efficiency and economic benefits of the system, and optimize energy scheduling and trading strategies through energy sharing and exchange between regions.

[0087] Based on this, the third embodiment of the present application also provides a comprehensive energy scheduling method, referring to Figure 5 , Figure 5This is a flow chart of the third embodiment of the comprehensive energy scheduling method of the present application; the same or similar contents as those of the above-mentioned embodiments 1 and 2 can be referred to the above introduction and will not be repeated later.

[0088] The comprehensive energy scheduling method is applied to the lower-level execution module, and the method includes steps H10 to H20:

[0089] Step H10, receiving a first scheduling scheme sent by an upper scheduling module, wherein the first scheduling scheme is determined based on energy information of each region and a preset global optimization target;

[0090] It should be noted that the execution subject is the lower-level execution module in the integrated energy system. The lower-level execution module is used to receive and execute the energy scheduling plan sent by the upper-level scheduling module, and send the optimization results after execution to the upper-level scheduling module.

[0091] It is understandable that the first scheduling scheme received by the lower execution module includes energy scheduling parameters and energy scheduling instructions, and the energy scheduling parameters include parameters such as energy price signals and energy demand. By receiving the energy scheduling scheme sent by the upper scheduling module, further processing can be performed based on the relevant energy information in the received scheme.

[0092] Step H20, based on the first scheduling scheme, respectively optimize the charging and discharging plans of electric vehicles in each area to obtain optimization results, and send the optimization results to the upper scheduling module, so that the upper scheduling module can adjust the first scheduling scheme based on the optimization results to obtain a second scheduling scheme.

[0093] It should be noted that electric vehicles are powered by electricity and have rechargeable batteries inside. Electric vehicles are usually charged at charging piles in the area. Charging piles and electric vehicle clusters can be used as mobile energy storage units. The charging and discharging plan of electric vehicles is information such as the time, location, and amount of charging and discharging of electric vehicles. By determining the charging and discharging plan of electric vehicles, the charging and discharging behavior of electric vehicles can be determined.

[0094] It can be understood that the lower-layer execution module is used to receive and execute the scheduling plan sent by the upper-layer scheduling module. Therefore, the lower-layer execution module can obtain the energy information of each region after executing the plan. The lower-layer execution module can optimize the charging and discharging behavior of electric vehicles based on the energy information of each region obtained. For example, after obtaining the energy price information of each region, it can plan to charge the electric vehicle in the area with lower energy price.

[0095] The lower-level execution module can also obtain real-time data information of electric vehicles, including the mileage of electric vehicles, the demand for charging volume, and the capacity of charging stations. Electric vehicles can optimize the charging and discharging plan of electric vehicles based on this real-time information. After the optimization is completed, the lower-level execution module sends the obtained optimization results to the upper-level scheduling module. The optimization results include information such as the actual charging and discharging plan, status, and energy usage of the electric vehicle.

[0096] In summary, in this embodiment, the lower-level execution module receives the first scheduling scheme determined by the upper-level scheduling module based on the energy information of each region and the preset global optimization target, and optimizes the charging and discharging plan of the electric vehicle based on the first scheduling scheme, and feeds back the obtained optimization result to the upper-level scheduling module; the lower-level execution module obtains the real-time data of the electric vehicle, and optimizes the charging and discharging plan of the electric vehicle based on the obtained real-time data, and feeds back the obtained optimization result to the upper-level scheduling module, so that the upper-level scheduling module can optimize the scheduling scheme. Therefore, this embodiment optimizes the charging and discharging plan of the electric vehicle and feeds back the optimization result, so that the upper-level scheduling module can combine the charging and discharging related information of the electric vehicle when adjusting the energy scheduling scheme, so that this embodiment can optimize the scheduling of multi-regional comprehensive energy based on the behavior of the electric vehicle, thereby improving the energy utilization efficiency and economic benefits.

[0097] Based on the first to third embodiments of the present application, in the fourth embodiment of the present application, refer to Figure 6 , Figure 6 This is a flowchart of the fourth embodiment of the comprehensive energy scheduling method of this application. For the same or similar contents as the above embodiments 1 to 3, please refer to the above introduction and will not be repeated later. On this basis, step H20, the charging and discharging plan of the electric vehicle is optimized based on the first scheduling scheme to obtain the optimization result, including steps H21 to H22:

[0098] Step H21, obtaining driving data of the electric vehicle, and determining a trip chain of the electric vehicle based on the driving data;

[0099] It should be noted that the driving data includes the time, location, power change and other data of the electric vehicle. The collected data needs to be cleaned and preprocessed. Data cleaning is to remove noise and outliers in the data to ensure the accuracy of the data. For example, remove the data of illogical time periods.

[0100] Data preprocessing operations include data filling, formatting, feature extraction, time period division, etc. Data filling is to process missing data and fill in the gaps caused by signal interruption or equipment problems. For example, interpolation or other techniques are used to estimate missing power or location data. Formatting is to convert the collected data into a unified format. Feature extraction is to extract useful feature values ​​from the original data, such as the energy consumption, dwell time, and driving distance of electric vehicles in each time period. Time period division is to divide a day into several time periods, and classify the data in each time period according to the status of the vehicle (driving, parking, charging, discharging).

[0101] It is understandable that the trip chain of an electric vehicle is defined as the entire trip from the starting point to the destination, including all driving and parking states. Including the time, location, power change and other data of the electric vehicle, therefore, the vehicle trip chain can be obtained through the driving data of the vehicle, thereby obtaining the location, driving and parking status and other information of the electric vehicle at each time, so as to obtain the power consumption and charging demand of the electric vehicle.

[0102] Step H22, based on the first scheduling scheme and the trip chain, with the goal of minimizing the charging and discharging cost, optimize the charging and discharging plans of the electric vehicles in each area to obtain optimization results.

[0103] It should be noted that the steps of the lower-level execution module executing the first scheduling plan include scheduling and trading energy according to the energy scheduling instructions and energy trading instructions of the first scheduling plan, and setting relevant parameters according to parameters such as energy prices and energy demands in the first scheduling plan.

[0104] The objective function for optimizing the minimization of charging and discharging cost is:

[0105]

[0106] Among them, C dis,k,t is the charging cost of the kth electric vehicle at time t, C cha,k,t is the discharge cost of the kth electric vehicle at time t. The formula is expressed as the sum of the charging and discharging costs of the 1st to Mth electric vehicles in the time period from 1 to T.

[0107] The optimization of charging and discharging costs can be performed by a meta-heuristic algorithm, which is a type of search algorithm that can avoid falling into a local optimum by searching the entire solution space. Meta-heuristic algorithms include genetic algorithms, simulated annealing algorithms, particle swarm algorithms, and the like. This embodiment uses a genetic algorithm for optimization.

[0108] The genetic algorithm searches for the global optimal solution by simulating the process of natural selection and genetic variation. The specific steps for optimizing the problem in this embodiment include:

[0109] 1. Initialize the population: randomly generate a set of feasible all-electric vehicle charging and discharging plans.

[0110] 2. Fitness evaluation: For each feasible all-electric vehicle charging and discharging plan, calculate the total charging and discharging cost of the all-electric vehicle during the entire time period and evaluate the total cost. The lower the total cost, the higher the evaluation of the electric vehicle plan.

[0111] 3. Selection: Select the evaluated and compared individuals for reproduction to generate the next generation of individuals. In this embodiment, the individuals are the charging and discharging plans of electric vehicles. The selection method can be roulette selection, tournament selection, elite selection, random selection, etc.

[0112] 4. Crossover and mutation: Select a part of the selected electric vehicle charging and discharging plan for crossover and mutation. The selected part can be the charging and discharging position, charging and discharging amount and other factors in the electric vehicle charging and discharging plan. Crossover can be to exchange the charging and discharging position, charging and discharging amount and other factors of the two plans or to inherit them by the new plan. Mutation can be to make corresponding changes to the charging and discharging position, charging and discharging amount and other factors in the electric vehicle charging and discharging plan.

[0113] 5. Termination condition: When the number of new electric vehicle charging and discharging plans generated reaches the preset generation, or the total cost of the electric vehicle charging and discharging plan no longer decreases, stop the iteration and output the electric vehicle charging and discharging plan with the lowest total cost.

[0114] Since the charging and discharging behavior of electric vehicles affects the state of the electric vehicles themselves, it also affects the energy supply and demand of each region. Therefore, the optimization results obtained after optimizing the electric vehicle charging and discharging plan include not only the actual electric vehicle charging and discharging plan, but also the state changes of the electric vehicles, the energy usage of each region and other information.

[0115] In a feasible embodiment, the specific implementation of obtaining the driving data of the electric vehicle and determining the trip chain of the electric vehicle based on the driving data may be:

[0116] Determine the location, state, travel demand and driving condition of the electric vehicle based on the driving data; determine the driving path of the electric vehicle based on the location and the state; determine the probability of the electric vehicle transferring between different states based on the state; determine the transition probability matrix based on the probability; determine the trip chain of the electric vehicle based on the driving path, the transition probability matrix, the travel demand, the driving condition and the preset charging and discharging power of the electric vehicle.

[0117] It should be noted that the states of electric vehicles include driving state, parking state, charging state and discharging state, and the state transition of electric vehicles in different time periods is described by the Markov chain model. The Markov chain model is a model used to indicate that the future state of an object depends only on the current state, not on the past historical state.

[0118] The Markov chain model has three basic concepts: state space, state transition, and transition probability. The state space is a set of all possible concepts in the Markov chain model, and in this embodiment, it represents a set of electric vehicle states. State transition represents the transition from one state to another, and the transition probability represents the probability of transitioning from one state to another, which can be represented by the state transition equation. The specific expression is:

[0119] P(X t+1 =s j X t =s i )=p ij

[0120] Among them, X t is the state at time t, s i and j are the states in the state space, p ij Indicates that from state s i to j The transition probability of an electric vehicle between different states can be estimated by estimating the transition probability of the electric vehicle through the collected historical data of the electric vehicle, and a transition probability matrix can be formed. The transition probability matrix is ​​used to describe the probability of an electric vehicle transferring from one state to another. In the transition probability matrix, each row represents the current state and each column represents the next state.

[0121] Travel demand can be expressed by the travel probability density of electric vehicles. The travel probability density is used to represent the distribution of travel activities and can be described by the normal distribution function using the collected electric vehicle data. The specific formula is:

[0122]

[0123] Among them, μ is the average travel time and σ is the standard deviation of the travel time. The travel time is the total travel time of the electric vehicle from the starting point to the destination. By modeling the travel behavior of electric vehicles and combining it with the modeling of the driving path of electric vehicles, the state of the electric vehicle at each time point can be determined more accurately.

[0124] Driving conditions refer to the operating status of a vehicle under different road environments and traffic conditions. These conditions include but are not limited to changes in vehicle speed, acceleration and deceleration, driving distance, road condition type, etc. The driving conditions of electric vehicles can be obtained or inferred through collected data. For example, the vehicle driving distance can be obtained through GPS data or calculated from vehicle location data. The vehicle speed can be calculated from the known driving distance and the corresponding time period, or directly obtained through high-precision sensor data. Road conditions are estimated by combining external data (such as road traffic systems, map data, meteorological data, etc.).

[0125] The power of an electric vehicle can be expressed by a state variable equation. The specific expression is:

[0126]

[0127] Among them, SOC EV,k,t is the state of the kth electric car at time t, P cha,k,t and P dis,k,t is the charge and discharge power, η cha and η dis It is the charging and discharging efficiency. Since electric vehicles will generate energy loss when charging and discharging, the energy used for charging and discharging electric vehicles is less than the actual energy required.

[0128] It is understandable that modeling the driving path of an electric vehicle based on the collected electric vehicle data is to determine the state of the electric vehicle at various time points during its driving process, and modeling the travel behavior of the electric vehicle is to determine the travel demand of the electric vehicle. Since the driving path modeling of the electric vehicle includes the state of the electric vehicle, and the state of the electric vehicle includes the charging and discharging state of the electric vehicle, the charging and discharging behavior of the electric vehicle can be determined by modeling the travel and driving path, and combined with the driving conditions of the electric vehicle, the electric power of the electric vehicle can be determined by the electric power change formula of the electric vehicle, and finally the power consumption and charging demand of the electric vehicle are obtained.

[0129] Specifically, the lower-level execution module obtains the driving data such as time, location, and power change of each electric vehicle in the area. Through the obtained driving data, the driving path and travel behavior of the electric vehicle are modeled, and the charging and discharging behavior of each electric vehicle is finally determined. Combined with the charging and discharging formula and driving conditions, the power consumption and charging demand of the electric vehicle in each time period are estimated. Since the power consumption and charging demand of the electric vehicle in each time period can be estimated, the charging and discharging behavior of the electric vehicle can be optimized on this basis. According to the genetic algorithm, multiple feasible charging and discharging plans are first created, and their charging and discharging costs are evaluated. The charging and discharging plan with low charging and discharging cost is selected with a higher probability. The factors such as the charging and discharging position and charging and discharging amount in the selected charging and discharging plan are crossed and mutated, and the steps of charging and discharging cost evaluation, charging and discharging plan selection, crossing and mutation are iterated multiple times. Finally, the number of iterations reaches the preset upper limit, and the charging and discharging plan with the lowest charging and discharging cost is finally selected as the charging and discharging plan, and the actual charging and discharging plan, electric vehicle status, energy usage and other information are obtained.

[0130] In summary, this embodiment collects driving data of electric vehicles, models the driving routes and travel behaviors of electric vehicles, and estimates the charging and discharging behaviors of electric vehicles, and estimates the power consumption and charging requirements of electric vehicles in each time period by combining driving conditions and power state equations. Afterwards, based on the estimated power consumption and charging requirements, the charging and discharging plan of electric vehicles is optimized through a meta-heuristic algorithm, and finally an optimization result including information such as the actual charging and discharging plan, electric vehicle status, and energy usage is obtained. Therefore, this embodiment ensures energy balance and cost minimization in each area by optimizing the charging and discharging plan of each electric vehicle.

[0131] In a feasible implementation manner, step H20, adjusting the first scheduling scheme based on the optimization result to obtain a second scheduling scheme, may also be:

[0132] When a change in the charging and discharging related information of the electric vehicle is detected, or the second scheduling plan is received, based on the received energy supply and energy demand of each area, it is determined whether it is necessary to optimize the charging and discharging plan of the electric vehicles in each area, wherein the second scheduling plan includes the first scheduling plan and the adjusted scheduling plan sent by the upper scheduling module; if necessary, the charging and discharging plan of the electric vehicles in each area is optimized to obtain the optimization result, and the optimization result is sent to the upper scheduling module; otherwise, the changed charging and discharging related information of the electric vehicle is sent to the upper scheduling module.

[0133] It should be noted that the information related to electric vehicle charging and discharging is real-time information related to the charging and discharging behavior of electric vehicles, such as the electric vehicle's mileage, charging requirements, and charging station capacity. The supply and demand of energy refers to the supply and demand of energy in each region. The optimized charging and discharging plan for electric vehicles needs to balance the supply and demand of energy in each region.

[0134] It is understandable that the lower-level execution module receives two types of information, one is the scheduling scheme sent by the upper-level scheduling module, and the other is the information related to the charging and discharging of electric vehicles received by the lower-level execution module. When the information related to the charging and discharging of electric vehicles received by the lower-level execution module changes, or when the scheduling scheme sent by the upper-level scheduling module is received, it means that the relevant factors affecting the charging and discharging of electric vehicles have changed, and therefore, the charging and discharging plan of electric vehicles needs to be changed. At this time, the lower-level execution module will generally optimize the charging and discharging plan of electric vehicles based on the changed information or the scheduling scheme sent by the upper-level scheduling module. However, in some special cases, such as a large number of electric vehicles charging in one area, resulting in an imbalance in the energy supply and demand in the area, the lower-level execution module does not optimize the charging and discharging plan of electric vehicles in the area at this time, but directly feeds back the changed information to the upper-level scheduling module, and the upper-level scheduling module adjusts the overall scheduling scheme.

[0135] In summary, in this embodiment, the lower-level execution module will optimize the charging and discharging plan of the electric vehicle based on the changing information related to the charging and discharging of the electric vehicle or feed back the changing charging and discharging information of the electric vehicle to the upper-level scheduling module, so that the upper-level scheduling module can adjust the comprehensive energy scheduling plan based on the received charging and discharging information of the electric vehicle or the optimization result, and finally obtain the second scheduling plan that meets the global optimization goal. Therefore, this embodiment dynamically adjusts the charging and discharging plan of the electric vehicle based on the information of the electric vehicle and feeds back information, so that the comprehensive energy scheduling system can adjust the comprehensive energy scheduling plan in real time based on the changing charging and discharging information of the electric vehicle, improve the flexibility of the comprehensive energy scheduling system, and improve the energy utilization efficiency and economic benefits by optimizing the comprehensive energy scheduling plan in real time.

[0136] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the comprehensive energy scheduling method of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0137] The present application proposes an integrated energy dispatching platform, characterized in that the integrated energy dispatching platform comprises:

[0138] The upper scheduling module is used to determine a first scheduling scheme based on the acquired energy information of each area and a preset global optimization target, and send the first scheduling scheme to the lower execution module, so that the lower execution module can optimize the charging and discharging plans of electric vehicles in each area based on the first scheduling scheme, obtain optimization results, and send the optimization results to the local, adjust the first scheduling scheme based on the optimization results, and obtain a second scheduling scheme;

[0139] The lower-level execution module is used to receive a first scheduling scheme sent by an upper-level scheduling module, wherein the first scheduling scheme is determined based on the energy information of each region and a preset global optimization target, and based on the first scheduling scheme, the charging and discharging plans of electric vehicles in each region are optimized respectively to obtain optimization results, and the optimization results are sent to the upper-level scheduling module, so that the upper-level scheduling module can adjust the first scheduling scheme based on the optimization results to obtain a second scheduling scheme.

[0140] The present application also provides a computer program product, including a computer program, which implements the steps of the above-mentioned comprehensive energy scheduling method when executed by a processor.

[0141] The computer program product provided by the present application can solve the technical problem that the integrated energy dispatching system is difficult to optimize energy dispatching due to the charging and discharging behavior of electric vehicles, resulting in low energy utilization efficiency and economic benefits. Compared with the prior art, the beneficial effects of the computer program product provided by the present application are the same as the beneficial effects of the integrated energy dispatching method provided by the above embodiment, which will not be repeated here.

[0142] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A comprehensive energy scheduling method, characterized in that: Applied to an upper scheduling module, the method includes: Determine a first scheduling scheme based on the acquired energy information of each region and a preset global optimization target, and send the first scheduling scheme to a lower-level execution module, so that the lower-level execution module optimizes the charging and discharging plans of electric vehicles in each region based on the first scheduling scheme, obtains an optimization result, and sends the optimization result to a local location; The first scheduling scheme is adjusted based on the optimization result to obtain a second scheduling scheme.

2. The method according to claim 1, characterized in that The step of determining the first scheduling scheme based on the energy information of each region and the preset global optimization target includes: Based on the acquired energy information of each region, a preliminary scheduling plan is determined with the goal of minimizing the operating cost of each region, and the energy information of each region is adjusted based on the preliminary scheduling plan to obtain adjusted energy information; The preliminary scheduling plan is adjusted based on the adjusted energy information and the preset equilibrium condition to obtain the first scheduling plan, wherein the first scheduling plan balances the energy supply and energy demand of each region.

3. The method according to claim 1, characterized in that: The step of adjusting the first scheduling scheme based on the optimization result to obtain a second scheduling scheme comprises: When it is detected that the energy information of each region changes, or the optimization result is received, or the charging and discharging related information of the electric vehicle after the change is received, it is determined whether the third scheduling plan can meet the preset global optimization goal, and the third scheduling plan includes the first scheduling plan or the adjusted scheduling plan; If not, the third scheduling scheme is adjusted to obtain an adjusted scheduling scheme, and the adjusted scheduling scheme is sent to the lower-layer execution module, so that the lower-layer execution module optimizes the charging and discharging plans of electric vehicles in each area based on the adjusted scheduling scheme, obtains optimization results, and sends the optimization results to the local area; Otherwise, the third scheduling scheme is not adjusted, and the third scheduling scheme is used as the second scheduling scheme.

4. The method according to claim 1, characterized in that: After the step of adjusting the first scheduling scheme based on the optimization result to obtain the second scheduling scheme, the method further includes: When it is detected that the difference between energy supply and energy demand reaches a preset threshold, if the energy supply of each area can meet the energy demand, the second scheduling plan is adjusted to change the user's energy use behavior so that the energy supply and energy demand of each area are balanced.

5. A comprehensive energy scheduling method, characterized in that: Applied to the lower layer execution module, the method described includes: Receiving a first scheduling scheme sent by an upper scheduling module, wherein the first scheduling scheme is determined based on energy information of each region and a preset global optimization target; Based on the first scheduling scheme, the charging and discharging plans of electric vehicles in each area are optimized respectively to obtain optimization results, and the optimization results are sent to the upper scheduling module so that the upper scheduling module can adjust the first scheduling scheme based on the optimization results to obtain a second scheduling scheme.

6. The method according to claim 5, characterized in that The step of optimizing the charging and discharging plan of the electric vehicle based on the first scheduling scheme to obtain the optimization result includes: Acquiring driving data of the electric vehicle, and determining a trip chain of the electric vehicle based on the driving data; Based on the first scheduling scheme and the trip chain, the charging and discharging plans of the electric vehicles in each area are optimized respectively with the goal of minimizing the charging and discharging costs to obtain optimization results.

7. The method according to claim 6, characterized in that The step of obtaining the driving data of the electric vehicle and determining the itinerary chain of the electric vehicle based on the driving data comprises: Determine the location, status, travel demand and driving condition of the electric vehicle based on the driving data; Based on the position and the state, determining a driving path of the electric vehicle; Determining a probability of the electric vehicle transitioning between different states based on the states; determining a transition probability matrix based on the probabilities; A trip chain of the electric vehicle is determined based on the driving path, the transition probability matrix, the travel demand, the driving condition and the preset charging and discharging power of the electric vehicle.

8. The method according to claim 5, characterized in that The step of optimizing the charging and discharging plan of electric vehicles in each region based on the first scheduling scheme to obtain an optimization result includes: When a change in the charging and discharging related information of the electric vehicle is detected, or the second scheduling scheme is received, judging whether it is necessary to optimize the charging and discharging plan of the electric vehicle in each area based on the received energy supply and energy demand of each area, wherein the second scheduling scheme includes the first scheduling scheme and the adjusted scheduling scheme sent by the upper scheduling module; If necessary, the charging and discharging plan of the electric vehicles in each area is optimized to obtain the optimization result, and the optimization result is sent to the upper scheduling module; Otherwise, the changed electric vehicle charging and discharging related information is sent to the upper scheduling module.

9. A comprehensive energy dispatching platform, characterized in that: The comprehensive energy dispatching platform includes: The upper scheduling module is used to determine a first scheduling scheme based on the acquired energy information of each area and a preset global optimization target, and send the first scheduling scheme to the lower execution module, so that the lower execution module can optimize the charging and discharging plans of electric vehicles in each area based on the first scheduling scheme, obtain optimization results, and send the optimization results to the local, adjust the first scheduling scheme based on the optimization results, and obtain a second scheduling scheme; The lower-level execution module is used to receive a first scheduling scheme sent by an upper-level scheduling module, wherein the first scheduling scheme is determined based on the energy information of each region and a preset global optimization target, and based on the first scheduling scheme, the charging and discharging plans of electric vehicles in each region are optimized respectively to obtain optimization results, and the optimization results are sent to the upper-level scheduling module, so that the upper-level scheduling module can adjust the first scheduling scheme based on the optimization results to obtain a second scheduling scheme.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the integrated energy scheduling method according to any one of claims 1 to 8 are implemented.

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