Layered optimization scheduling method, control system and computer readable storage medium

Abstract

The invention discloses a hierarchical optimization scheduling method. The method comprises the following steps: S1, acquiring micro-grid system parameters, electric vehicle parameters and power grid time-of-use electricity price information; s2, establishing an EV layer optimization model, and performing optimization solution by taking the user satisfaction degree of the electric vehicle user as a target function to obtain an optimal charging and discharging power plan of each electric vehicle; s3, transmitting the optimal charging and discharging power plan to a micro-grid layer; s4, establishing a micro-grid layer optimization model, taking the comprehensive operation cost of the micro-grid and the interaction power fluctuation between the micro-grid and the main grid as objective functions, and performing optimization solution by combining the optimal charging and discharging power plan to obtain a scheduling plan of distributed energy in the micro-grid; wherein the EV layer optimization model and the micro-grid layer optimization model jointly form a layered optimization framework. The scheduling method can promote the electric vehicle user to join the microgrid scheduling to a greater extent.

Description

Technical Field

[0001] This invention relates to the field of intelligent dispatching of microgrids, specifically to a hierarchical optimization dispatching method, a control system, and a computer-readable storage medium. Background Technology

[0002] A microgrid is an intelligent, small-scale power supply system that integrates local power generation, energy storage, and power consumption. It can operate in parallel with the main power grid or independently when needed, thus achieving a higher level of energy self-sufficiency, economic efficiency, and power supply reliability. Microgrid charging stations, which integrate photovoltaics, energy storage, and intelligent dispatching, are rapidly developing globally as a more economical, greener, and grid-relieving innovative model.

[0003] Vehicle-to-grid (V2G) technology transforms electric vehicles from mere energy-consuming transportation tools into mobile "giant power banks." During off-peak hours, electric vehicles charge at low prices; during peak hours, they can sell the energy stored in their batteries back to the grid, earning the difference in price. As a result, more and more electric vehicle owners are choosing V2G technology.

[0004] However, the uncontrolled and disorderly connection of large numbers of electric vehicles to microgrids for charging can have a significant negative impact on the stability, security, and economic operation of microgrids due to their spatiotemporal randomness and volatility. This is mainly manifested in exacerbating the "peak-on-peak" phenomenon of load peaks, increasing the peak-shaving pressure on the power grid, and affecting the power quality of the system.

[0005] To guide the orderly charging and discharging of electric vehicles and tap their potential as mobile energy storage units, demand response technology is considered an effective solution. Currently, it mainly falls into two categories: price-based demand response, such as implementing time-of-use pricing to guide user behavior through price signals; and incentive-based demand response, which motivates users to participate in grid dispatch by signing agreements or providing additional compensation. Integrating electric vehicles into the microgrid dispatch framework through these response strategies is expected to improve the stability and economy of microgrid operation, while also increasing user satisfaction.

[0006] However, the microgrid layer pursues system stability and economic optimization, while the EV user layer focuses on personal costs and travel convenience. The two goals are inherently conflicting, making it difficult for scheduling strategies to be widely accepted and effectively implemented by users in practice. Summary of the Invention

[0007] The purpose of this invention is to provide a hierarchical optimization scheduling method that can achieve a win-win situation for the EV layer and the microgrid layer in terms of economy and stability, thereby encouraging electric vehicle users to participate in microgrid scheduling to a greater extent.

[0008] To achieve the above objectives, the present invention provides a hierarchical optimization scheduling method, comprising the following steps: S1: Obtain microgrid system parameters, electric vehicle parameters, and grid time-of-use electricity price information; S2: Establish an EV layer optimization model, take the user satisfaction of electric vehicle users as the objective function, and optimize the solution to obtain the optimal charging and discharging power plan for each electric vehicle; S3: Transmit the optimal charging and discharging power plan to the microgrid layer; S4: Establish a microgrid layer optimization model, with the comprehensive operating cost of the microgrid and the power fluctuation of the interaction between the microgrid and the main grid as the objective function, and combine the optimal charging and discharging power plan to optimize and solve the scheduling plan of distributed energy in the microgrid; The EV layer optimization model and the microgrid layer optimization model together constitute a hierarchical optimization architecture.

[0009] Preferably, during off-peak and peak hours, the objective function is to maximize the user satisfaction of electric vehicle users, and the user satisfaction is a weighted average of cost satisfaction and travel satisfaction.

[0010] Preferably, in step 2, during the normal period, the objective function is a combination of maximizing the user satisfaction of electric vehicle users and minimizing the microgrid interaction power.

[0011] Preferably, the cost satisfaction is calculated based on the charging and discharging costs and battery wear costs of electric vehicle users; The travel satisfaction is calculated based on the relationship between the output power of the electric vehicle battery and the user's expected travel convenience.

[0012] Preferably, in step S2, the CPLEX optimizer is used to solve the EV layer optimization model, which is a mixed integer linear programming model.

[0013] Preferably, in step S4, an improved particle swarm optimization algorithm is used to solve the microgrid layer optimization model. The inertia weight ω of the improved particle swarm optimization algorithm adopts a nonlinear dynamic decreasing strategy, and its update formula is:

[0014] in, λ T0 is the decreasing exponent and the iteration threshold; d1 and d2 are control factors, with optimal values ​​of d1=0.2 and d2=0.7. ω max , ω min for ω The upper and lower limits of the value.

[0015] Preferably, in step S4, the Pareto solution set is obtained by solving the microgrid layer optimization model, and a compromise solution is selected from the Pareto solution set as the final optimal scheduling scheme using a fuzzy membership function.

[0016] The present invention also provides a control system, comprising: The data acquisition module is used to acquire microgrid system parameters, electric vehicle parameters, and time-of-use electricity price information. The EV layer optimization module is used to run the EV layer optimization model, with the user satisfaction of electric vehicle users as the objective function, and considers introducing a price incentive mechanism with the additional objective of minimizing microgrid interaction power during normal periods, and optimizes the solution to obtain the optimal charging and discharging power plan for each electric vehicle. The communication module is used to transmit the optimal charging and discharging power plan to the microgrid layer optimization module; The microgrid layer optimization module is used to run the microgrid layer optimization model. The objective function is the comprehensive operating cost of the microgrid and the power fluctuation of the interaction between the microgrid and the main grid. Combined with the optimal charging and discharging power plan, the module optimizes and solves the scheduling plan of distributed energy in the microgrid. The scheduling execution module is used to control the operation of distributed energy resources within the microgrid according to the scheduling plan.

[0017] Preferably, the EV layer optimization module uses the CPLEX optimizer for solving, and the microgrid layer optimization module uses an improved particle swarm optimization algorithm for solving.

[0018] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of a hierarchical optimized scheduling method.

[0019] According to the above technical solution, electric vehicle users of this invention send information such as vehicle driving time, distance, battery state of charge, charging demand during off-peak hours, discharging demand during peak hours, and charging / discharging intentions during normal hours to the microgrid layer control center through networked charging piles. The microgrid layer then sets charging and discharging prices for each time period. During off-peak and peak hours, the optimization objective is electric vehicle user satisfaction; during normal hours, the optimization objectives are electric vehicle user satisfaction and minimum microgrid power. Combined with corresponding constraints, the EV charging and discharging power is obtained. Within the microgrid layer, the optimization objectives are minimum operating cost and minimum interactive power fluctuation. Combined with the system and individual device operating requirements as constraints, dynamic energy scheduling is used to obtain the distributed energy output power in the microgrid to meet the system's power balance requirements.

[0020] This approach encourages electric vehicle (EV) users to participate in microgrid dispatching, fully utilizes EV charging and discharging behavior, increases the system's dispatchable energy margin, and enhances the benefits for EV users. It achieves a win-win situation for both the EV layer and the microgrid layer in terms of economy and stability.

[0021] Other features and advantages of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0022] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the following detailed description to explain the invention, but do not constitute a limitation thereof. In the drawings: Figure 1 This is a schematic diagram of a microgrid layer and its hierarchical optimization architecture. Figure 2 This is an optimization algorithm solution process; Figure 3 These are the power curves corresponding to Comparative Example 1 and Comparative Example 2; Figure 4 The charging and discharging power of the embodiment under a 0.2 discount is compared with that of the electric vehicle in Comparative Example 1. Figure 5 The charging and discharging power of the embodiment under a 0.3 discount is compared with that of the electric vehicle in Comparative Example 1. Figure 6 This is a comparison of the charging and discharging power of the embodiment under a 0.4 discount with that of the electric vehicle in Comparative Example 1; Figure 7 This is a comparison of the charging and discharging power of the embodiment under a 0.5 discount with that of the electric vehicle in Comparative Example 1; Figure 8 The bar charts compare user satisfaction with electric vehicle users in the examples corresponding to discounts of 0.2-0.5 and Comparative Example 1. Figure 9 The bar charts compare the overall cost of the microgrid layer with the examples corresponding to discounts of 0.2-0.5 and Comparative Example 1. Figure 10 The images show a comparison of the interactive power histograms between the microgrid and the main grid in the examples corresponding to discounts of 0.2-0.5 and Comparative Example 1. Detailed Implementation

[0023] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0024] In this invention, unless otherwise stated, directional terms included in the terminology represent only the orientation of the term in its normal use or as commonly understood by those skilled in the art, and should not be regarded as a limitation on the term.

[0025] A hierarchical optimization scheduling method includes the following steps: S1: Obtain microgrid system parameters, electric vehicle parameters, and grid time-of-use electricity price information; S2: Establish an EV layer optimization model, take the user satisfaction of electric vehicle users as the objective function, and optimize the solution to obtain the optimal charging and discharging power plan for each electric vehicle; S3: Transmit the optimal charging and discharging power plan to the microgrid layer; S4: Establish a microgrid layer optimization model, with the comprehensive operating cost of the microgrid and the power fluctuation of the interaction between the microgrid and the main grid as the objective function, and combine the optimal charging and discharging power plan to optimize and solve the scheduling plan of distributed energy in the microgrid; The EV layer optimization model and the microgrid layer optimization model together constitute a hierarchical optimization architecture.

[0026] Through the implementation of the above technical solutions, the microgrid components include distributed energy sources such as wind turbines (WT), photovoltaic modules (PV), and diesel engines (DE), as well as storage batteries (SB), base loads, smart charging piles, and information networking equipment.

[0027] Electric vehicle (EV) users send information such as vehicle driving time, distance traveled, battery state of charge, off-peak charging demand, peak-peak discharging demand, and willingness to charge / discharge during normal times to the microgrid control center via networked charging stations. The microgrid then sets charging / discharging prices for each time period. During off-peak and peak hours, the optimization objective is EV user satisfaction; during normal times, it's EV user satisfaction and minimum microgrid power output. These factors, combined with appropriate constraints, determine the EV charging / discharging power. Within the microgrid, the optimization objectives are minimum operating cost and minimum interactive power fluctuations. These are combined with system and individual device operating requirements as constraints. Dynamic energy scheduling is used to determine the output power of distributed energy resources within the microgrid to meet the system's power balance requirements.

[0028] In this factual approach, preferably, in step S2, during off-peak and peak periods, the objective function is to maximize the user satisfaction of electric vehicle users, and the user satisfaction is a weighted average of cost satisfaction and travel satisfaction.

[0029] In this embodiment, preferably, the cost satisfaction is calculated based on the charging and discharging costs and battery wear costs of electric vehicle users; The travel satisfaction is calculated based on the relationship between the output power of the electric vehicle battery and the user's expected travel convenience.

[0030] Electric vehicle batteries possess both load and energy storage characteristics, serving as mobile source-load systems. Electric vehicle users formulate charging and discharging plans based on their travel habits and affordability, which can also affect the convenience of travel in unexpected situations. To better promote the participation of electric vehicles in microgrid power balancing, we will discuss the charging and discharging prices for different time periods.

[0031] Considering electric vehicle user cost satisfaction α during off-peak and peak hours. i and travel satisfaction β i The objective function is to optimize the charging and discharging power of electric vehicles participating in the scheduling process by maximizing user satisfaction.

[0032] The objective function formula is as follows: (1) In the formula, a 1. a 2 is α i and β i The weighting coefficients, N This represents the number of time periods.

[0033] No. i The formula for calculating the satisfaction level of electric vehicle cost is as follows: (2) In the formula, , For the first i Electric vehicle users accept the maximum and minimum cost expenditures; C i For the first i The expenses incurred by electric vehicle users consist of charging and discharging costs and battery wear and tear costs during the charging and discharging process. The calculation formula is as follows: (3) In the formula, P i,t For the first i electric vehicles in the first t Charging and discharging power over a given time period; P rice,t For the first t The charging and discharging electricity price within the time period; EV change Cost of replacing electric vehicle batteries; E maxThis refers to the maximum charge and discharge capacity of an electric vehicle battery.

[0034] No. i The formula for calculating the travel satisfaction of electric vehicles is as follows: (4) In the formula, Poutmax i,t , Poutmin i,t For the first i Electric vehicle users in the first t The maximum and minimum travel satisfaction during the time period are measured at the electric vehicle battery output power. Pout i,t For the first i electric vehicles in the first t The output power of electric vehicle batteries during the specified time period. Among these, electric vehicle users showed the highest satisfaction (β) when selecting the disordered charging mode. i =1; Electric vehicle users choose the option with the lowest cost and ignore travel convenience for charging and discharging, resulting in the lowest satisfaction, β. i =0.

[0035] In this embodiment, preferably, in step 2, during the normal period, the objective function is a combination of maximizing the user satisfaction of electric vehicle users and minimizing the microgrid interaction power.

[0036] During normal periods, to encourage electric vehicle users to actively participate in the power balancing of the microgrid, a price incentive mechanism was adopted. The charging and discharging power of electric vehicles participating in the dispatch was optimized with the objective function of electric vehicle user satisfaction and minimizing the microgrid's interactive electricity consumption. Here, microgrid interactive electricity consumption refers to the amount of electricity the microgrid purchases or sells to the main grid during a specific time period within the normal period.

[0037] The objective function is as follows: (5) In the formula, P PV,t For the first t Total photovoltaic power generation during the period; P WT,t For the first t Total power generation of wind turbines during the period; P DE,t For the first t Total power generation of diesel generators during the time period; P SB,t For the first t Total battery charging and discharging power during the period; P load,t For the first t Load power during the time period; P EV,t For the first t Total electric power of electric vehicles during the time period.

[0038] When solving the objective function, some constraints also need to be considered.

[0039] 1) Electric vehicle charging and discharging power constraints: (6) In the formula, P i,t For the first i electric vehicles in the first t Charging and discharging power over a given time period; Pchar max , Pdis max These are the maximum power outputs for charging and discharging EVs, respectively.

[0040] 2) Constraints on the number of electric vehicles charging and discharging (7) In the formula, n t For the first t The number of electric vehicles that are charging or discharging during the time period; n lim This represents the maximum number of charging stations.

[0041] 3) Battery state of charge constraints for electric vehicles (8) (9) In the formula, for i The state of charge of the electric vehicle's battery in time period t. soc max , soc min These represent the maximum and minimum states of charge of the electric vehicle battery. S EV η is the rated capacity of the battery. char , η dis These are the charging and discharging power coefficients for electric vehicles.

[0042] In this embodiment, preferably, in step S2, the CPLEX optimizer is used to solve the EV layer optimization model, which is a mixed integer linear programming model.

[0043] In this embodiment, preferably, in step S4, an improved particle swarm optimization algorithm is used to solve the microgrid layer optimization model. The inertia weight ω of the improved particle swarm optimization algorithm adopts a nonlinear dynamic decreasing strategy, and its update formula is:

[0044] in, λ ,T 0 represents the decreasing exponent and the iteration threshold; d1 and d2 are control factors, with optimal values ​​of d1=0.2 and d2=0.7. ω max , ω min for ω The upper and lower limits of the value.

[0045] To ensure stable and economical operation of the microgrid and reduce its impact on the main grid, the distribution configuration and power output of the microgrid layer are optimized with the objective functions of minimizing the overall cost of the microgrid and minimizing the power fluctuations in interaction with the main grid.

[0046] The formula for calculating the minimum overall cost of the microgrid layer is as follows: (10) In the formula, C 1. C 2 represents the daily operating cost and daily environmental maintenance cost of the microgrid, respectively. C The mathematical expression is as follows: (11) In the formula, n 1. n 2. n 3. n 4 represents the number of photovoltaic panels, wind turbines, diesel engines, and battery packs; P rice,PV , P rice,WT , P rice,DE , P rice,SB The cost of purchasing a single photovoltaic unit, wind turbine, diesel engine, and battery; C PV,WT Maintenance costs for photovoltaic and wind power generation; C SB This is the sum of the system battery charge / discharge conversion loss cost and battery maintenance cost; C grid This represents the interaction cost between the microgrid and the main grid.

[0047] The maintenance costs for photovoltaic and wind power generation equipment are as follows: (12) In the formula, T The scheduling operation time period; P´ rice,PV P' rice, WT represents the maintenance cost per kW of photovoltaic and wind power generation. P PV,i,t , P WT,i,t For the first t Within the time periodi The power generation capacity of photovoltaic and wind turbines.

[0048] System battery cost C SB The calculation formula is as follows: (13) In the formula, C dis / char Cost conversion for each charge and discharge cycle of the battery; u This refers to the number of battery conversion cycles. P SB,i,t For the first t The first time period i Battery charge / discharge power; K SB This is the battery operation and maintenance factor.

[0049] Interaction fees with the mainnet C grid The calculation formula is as follows: (14) In the formula, P grid,t For the first t Interaction power between the microgrid and the main grid during a given time period; Psell_buy rice,t For the first t The electricity price for microgrids to purchase and sell electricity from the main grid during specific time periods.

[0050] Environmental maintenance costs C 2. The calculation formula is as follows: (15) In the formula, K DE This refers to the environmental protection factor for diesel generator operation. P DE,i,t For the first t Time period i The output power of the diesel generator set.

[0051] The formula for calculating the minimum interaction power between the microgrid and the main grid is as follows: (16) In the formula, P grid,t For the first t Interaction power between the microgrid and the main grid during a given time period.

[0052] When solving the above objective function, some constraints also need to be considered.

[0053] 1) Power balance constraints (17) In the formula,P grid,t For the first t Interaction power between the microgrid and the main grid during a given time period; P PV,i,t , P WT,i,t 、P DE,i,t , P SB,i,t For the first t Within the time period i The power generation capacity of photovoltaic, wind turbine, and diesel engine units, as well as the purchased battery capacity. P load,t For the first t Load power during the time period; P EV,t For the first t Total charging and discharging power of electric vehicles during the time period.

[0054] 2) Output constraints of distributed power sources (18) In the formula, PDE i,t,max , PDE i,t,min for PDE i,t The upper and lower limits.

[0055] 3) Distributed power generation ramping constraints (19) In the formula, up i For the first i The biggest ramp-up for distributed energy.

[0056] 4) Battery charging and discharging power constraints (20) In the formula, Pchar SB,max , Pdis SB,min This represents the maximum charging and discharging power of the battery.

[0057] 5) Battery state of charge (twenty one) In the formula, soc SB,max , soc SB,min These are the upper and lower limits of the battery's state of charge. soc SB,t For the storage battery t State of charge over a period of time; E SB This refers to the rated capacity of the battery. η SB,char , η SB,disThe charging efficiency of the batteries respectively; a This refers to the battery self-discharge rate.

[0058] A multi-objective hierarchical optimization scheduling model for the microgrid layer is solved using a combination of CPLEX and an improved particle swarm optimization algorithm. CPLEX is used to solve the mixed-integer linear programming problem for charging and discharging power in the electric vehicle layer, yielding the charging and discharging plans for electric vehicle users. The improved particle swarm optimization algorithm is then used to solve the nonlinear, multi-constraint, and multi-objective optimization problem in the microgrid layer.

[0059] To improve the speed and convergence of the particle swarm optimization (PSO) algorithm, the PSO formula is improved. (Initial iteration) ω A larger value allows the particle to travel through the entire search space at a higher speed, thus determining the initial range of the optimal value. As iterations proceed... ω The nonlinear dynamic reduction gradually decreases the search space for most particles, concentrating them within the neighborhood of the optimal value. Upon reaching the iteration threshold, the inertia weight is limited to... ω max ;exist ω During the decreasing process, when searching for the optimal result of the high-dimensional objective function, the particle finds the global optimal solution within the optimal value range with a nearly constant flight speed, which is beneficial to improving the convergence speed of the algorithm. It has obvious advantages in terms of search accuracy, convergence speed, and stability.

[0060] (twenty two) In the formula, λ , T 0 represents the decreasing exponent and the iteration threshold; d 1. d 2 is the control factor, and the optimal value is d 1 = 0.2 d 2 = 0.7; ω max , ω min for ω The upper and lower limits of the value.

[0061] The solution flowchart is as follows: Figure 2 As shown, the specific steps are as follows: 1) Read the electric vehicle's parameters and the electricity price published by the power grid through the charging station; 2) The electric vehicle layer uses equations (1) and (5) as objective functions, and combines the constraint conditions (6) to (9) to solve the optimal charging and discharging power distribution of electric vehicles through CPLEX and transmit it to the microgrid layer; 3) Read the microgrid layer system parameters, set the particle swarm parameter values, and initialize the particle swarm; 4) The microgrid layer integrates information on distributed energy output, battery output, basic daily load, and optimal electric vehicle charging and discharging power, and adopts a dynamic energy dispatching strategy; 5) The microgrid layer uses equations (10) and (16) as objective functions and equations (17)-(21) as constraints to solve the Pareto solution using an improved particle swarm optimization algorithm; 6) Use fuzzy membership functions to obtain a compromise solution from the Pareto front and use it as the optimal solution.

[0062] Preferably, in step S4, the Pareto solution set is obtained by solving the microgrid layer optimization model, and a compromise solution is selected from the Pareto solution set as the final optimal scheduling scheme using a fuzzy membership function.

[0063] The present invention also provides a control system, comprising: The data acquisition module is used to acquire microgrid system parameters, electric vehicle parameters, and time-of-use electricity price information. The EV layer optimization module is used to run the EV layer optimization model, with the user satisfaction of electric vehicle users as the objective function, and considers introducing a price incentive mechanism with the additional objective of minimizing microgrid interaction power during normal periods, and optimizes the solution to obtain the optimal charging and discharging power plan for each electric vehicle. The communication module is used to transmit the optimal charging and discharging power plan to the microgrid layer optimization module; The microgrid layer optimization module is used to run the microgrid layer optimization model. The objective function is the comprehensive operating cost of the microgrid and the power fluctuation of the interaction between the microgrid and the main grid. Combined with the optimal charging and discharging power plan, the module optimizes and solves the scheduling plan of distributed energy in the microgrid. The scheduling execution module is used to control the operation of distributed energy resources within the microgrid according to the scheduling plan.

[0064] Preferably, the EV layer optimization module uses the CPLEX optimizer for solving, and the microgrid layer optimization module uses an improved particle swarm optimization algorithm for solving.

[0065] The present invention also provides a computer-readable storage medium having a computer program stored thereon, the program being executed by a processor to implement the steps of a hierarchical optimized scheduling method.

[0066] Taking a grid-connected microgrid in an isolated island community as an example, containing 100 electric vehicles, the Monte Carlo method was used to simulate the driving habits of the electric vehicles, including grid connection and disconnection times, and initial battery state of charge. Basic information about the electric vehicle batteries is shown in Table 1. System parameter settings included battery limits of 0.9 and 0.2, a single photovoltaic power supply capacity of 500 kWp, a single wind turbine capacity of 405 kW, a battery bank rated capacity of 300 kWh, and a maximum diesel generator output of 300 kW. A minimum decision duration of 15 minutes was used. The improved particle swarm optimization algorithm had a population size of 500 and a total of 30 iterations. λ= 6 , T 0 = 50.

[0067] Table 1:

[0068] Comparative Example 1 The disorderly charging mode should be adopted to facilitate the travel of electric vehicle users.

[0069] In the disordered charging mode, the power distribution of the microgrid is from 9 am to 1 pm and from 6 pm to 10 pm. These two time periods are mainly for EV users in the work area and residential area to charge. Because the disordered charging intentions of users lead to the "peak-on-peak" phenomenon in the area, the load burden of the microgrid layer is increased.

[0070] Comparative Example 2 Orderly charging and discharging according to the minimum cost for electric vehicle users.

[0071] Time-of-use pricing encourages EV users to charge between 0:00 and 7:00 and between 22:00 and 24:00, and to discharge between 10:00 and 3:00 and between 7:00 and 9:00. This allows EV users to maximize their benefits. However, when most EV users charge and discharge at the same time, it creates a new peak-valley load problem.

[0072] Example Guided by the hierarchical optimization scheduling method, and based on the impact of cost expenditure and travel convenience, electric vehicle users are guided to charge and discharge reasonably, which effectively alleviates the load pressure and enhances the effect of "peak shaving and valley filling".

[0073] Time-of-use pricing strategies with price incentives encourage electric vehicle users to actively participate in the energy dispatch of the microgrid through price changes. For example, the charging and discharging power of electric vehicles... Figure 4-7As shown, under discounts of 0.2-0.5, the hierarchical optimization strategy during the normal period promoted the participation of electric vehicle users in microgrid energy dispatch by 9.16%, 11.03%, 10.56%, and 10.94% respectively compared to the non-hierarchical optimization strategy, with the highest number of vehicles participating in microgrid energy dispatch under a discount of 0.3.

[0074] Unlike Comparative Example 1 (disordered charging) and Comparative Example 2 (minimum charging cost), this embodiment effectively reduces the problems of peak-valley load differences and excessive expenses. At the same time, it can effectively balance travel satisfaction and cost satisfaction, achieving optimal user satisfaction.

[0075] from Figure 4-7 It can be seen that the hierarchical optimization strategy with price incentives is more effective in promoting the participation of electric vehicles in microgrid dispatch. At the same time, as the incentive discount increases, more and more electric vehicles participate in microgrid energy dispatch.

[0076] from Figure 8 The bar chart shows that, under discounts of 0.2 to 0.5, the hierarchical optimization strategy increased electric vehicle user satisfaction by 2.57%, 3.26%, 1.72%, and 0.33% respectively compared to the non-hierarchical optimization strategy, with the most significant effect observed at a discount of 0.3. At a discount of 0.2, the hierarchical optimization strategy resulted in an electric vehicle user satisfaction increase of only 0.507; and at discounts of 0.3 to 0.5, the hierarchical optimization strategy resulted in a change in electric vehicle user satisfaction of no more than 3%. In summary, as the incentive discount increases, the difference in satisfaction between non-hierarchical and hierarchical optimization randomly decreases, only reaching its maximum when the discount reaches its maximum value, at which point the satisfaction levels of non-hierarchical and hierarchical optimization are the same. Compared to the non-hierarchical optimization strategy, the hierarchical optimization strategy significantly promotes the participation of electric vehicle users in microgrid dispatch, fully utilizes the charging and discharging behavior of electric vehicles, and increases the system's dispatchable energy margin and the benefits for electric vehicle users.

[0077] The optimal charging and discharging power of the EV layer under different discounts is transferred to the microgrid layer, forming a net load with the base load, photovoltaic output, and wind turbine output. It also participates in the dynamic scheduling within the microgrid layer with controllable distributed energy. The Pareto front is obtained by using the improved PSO algorithm. Table 2 shows the optimal solutions under different discounts.

[0078] Table 2:

[0079] in accordance with Figure 9It can be seen that, under discounts of 0.2 to 0.5, the hierarchical optimization strategy improves the economic efficiency by 23.14%, 26.07%, 19.37%, and 1.86% respectively compared to the non-hierarchical optimization strategy, with the microgrid showing the highest economic efficiency at a discount of 0.3. In terms of system security and stability, the strategies improve by 695.31%, 1209.57%, 1196.19%, and 796.65% respectively, with the microgrid showing the best security and stability at a discount of 0.3. In summary, under different discounts, the optimal cost under the hierarchical optimization strategy is lower than that under the non-hierarchical optimization strategy, and the difference in optimal cost between the two strategies gradually decreases as the discount increases, indicating that the hierarchical optimization strategy increases the economic efficiency of the microgrid system. Furthermore, the optimal interaction power under the hierarchical optimization strategy is significantly lower than that under the non-hierarchical optimization strategy, indicating that the hierarchical optimization strategy reduces the interaction power between the microgrid system and the main grid during operation, thereby increasing the security and stability of the microgrid system.

[0080] Figure 6 As can be seen, the distribution of the optimal solution set under the hierarchical optimization strategy is closer to the ideal Pareto solution set, while the optimal solution set under the non-hierarchical optimization strategy exhibits a chaotic phenomenon. The hierarchical optimization strategy has reliability for microgrid scheduling.

[0081] Therefore, by considering the interests of both the EV layer and the microgrid layer, and formulating different objective functions based on different stakeholders, a hierarchical optimization strategy with an incentive mechanism is proposed. The EV layer uses user satisfaction to optimize EV charging and discharging plans, while the microgrid layer aims to reduce the overall system cost and the interaction power between the main grid and the microgrid layer, adjusting the output of internally controllable distributed energy resources, thereby achieving a win-win situation for both parties. Simulation verification yields the following conclusions: (1) A user satisfaction model under the incentive mechanism was proposed, which promoted electric vehicle users to participate in charging and discharging operations and better realized the role of "peak shaving and valley filling"; (2) Compared with non-layered optimization, layered optimization can better achieve a win-win situation for the EV layer and the microgrid layer in terms of economy and stability.

[0082] The preferred embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the specific details of the above embodiments. Within the scope of the technical concept of the present invention, various simple modifications can be made to the technical solution of the present invention, and these simple modifications all fall within the protection scope of the present invention.

[0083] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the present invention will not describe the various possible combinations separately.

[0084] Furthermore, various different embodiments of the present invention can be combined in any way, as long as they do not violate the spirit of the present invention, they should also be regarded as the content disclosed by the present invention.

Claims

1. A hierarchical optimization scheduling method, characterized in that, Includes the following steps: S1: Obtain microgrid system parameters, electric vehicle parameters, and grid time-of-use electricity price information; S2: Establish an EV layer optimization model, take the user satisfaction of electric vehicle users as the objective function, and optimize the solution to obtain the optimal charging and discharging power plan for each electric vehicle; S3: Transmit the optimal charging and discharging power plan to the microgrid layer; S4: Establish a microgrid layer optimization model, with the comprehensive operating cost of the microgrid and the power fluctuation of the interaction between the microgrid and the main grid as the objective function, and combine the optimal charging and discharging power plan to optimize and solve the scheduling plan of distributed energy in the microgrid; The EV layer optimization model and the microgrid layer optimization model together constitute a hierarchical optimization architecture.

2. The hierarchical optimization scheduling method according to claim 1, characterized in that, During off-peak and peak hours, the objective function is to maximize the user satisfaction of electric vehicle users, which is a weighted average of cost satisfaction and travel satisfaction.

3. The hierarchical optimization scheduling method according to claim 2, characterized in that, In step 2, during the normal period, the objective function is a combination of maximizing the user satisfaction of electric vehicle users and minimizing the microgrid interaction power.

4. The hierarchical optimization scheduling method according to claim 2, characterized in that, The cost satisfaction is calculated based on the charging and discharging costs and battery wear costs of electric vehicle users. The travel satisfaction is calculated based on the relationship between the output power of the electric vehicle battery and the user's expected travel convenience.

5. The hierarchical optimization scheduling method according to claim 1, characterized in that, In step S2, the CPLEX optimizer is used to solve the EV layer optimization model, which is a mixed integer linear programming model.

6. The hierarchical optimization scheduling method according to claim 1, characterized in that, In step S4, an improved particle swarm optimization algorithm is used to solve the microgrid layer optimization model. The inertia weight ω of the improved particle swarm optimization algorithm adopts a nonlinear dynamic decreasing strategy, and its update formula is as follows: Where λ and T0 are the decreasing exponent and iteration threshold, respectively; d1 and d2 are control factors, with optimal values ​​of d1=0.2 and d2=0.7; ω max ω min The upper and lower limits of the value of ω.

7. The hierarchical optimization scheduling method according to claim 1, characterized in that, In step S4, the Pareto solution set is obtained by solving the microgrid layer optimization model, and a compromise solution is selected from the Pareto solution set as the final optimal scheduling scheme using a fuzzy membership function.

8. A control system, characterized in that, include: The data acquisition module is used to acquire microgrid system parameters, electric vehicle parameters, and time-of-use electricity price information. The EV layer optimization module is used to run the EV layer optimization model, with the user satisfaction of electric vehicle users as the objective function, and considers introducing a price incentive mechanism with the additional objective of minimizing microgrid interaction power during normal periods, and optimizes the solution to obtain the optimal charging and discharging power plan for each electric vehicle. The communication module is used to transmit the optimal charging and discharging power plan to the microgrid layer optimization module; The microgrid layer optimization module is used to run the microgrid layer optimization model. The objective function is the comprehensive operating cost of the microgrid and the power fluctuation of the interaction between the microgrid and the main grid. Combined with the optimal charging and discharging power plan, the module optimizes and solves the scheduling plan of distributed energy in the microgrid. The scheduling execution module is used to control the operation of distributed energy resources within the microgrid according to the scheduling plan.

9. The control system according to claim 8, characterized in that, The EV layer optimization module uses the CPLEX optimizer for solving, while the microgrid layer optimization module uses an improved particle swarm optimization algorithm for solving.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the hierarchical optimized scheduling method as described in any one of claims 1 to 6.

Images