Price-excited micro-grid layered optimization adjustment method, medium and equipment

Abstract

The invention provides a price-excited micro-grid layered optimization adjustment method, a medium and equipment, and relates to the technical field of distributed energy operation. Designing an electric vehicle layer participation charge and discharge behavior optimization objective function by taking the cost of an electric vehicle user and the comprehensive satisfaction degree of travel as objectives; based on a price incentive mechanism, designing an optimization function by taking the maximum benefit of an electric vehicle user and the minimum interaction power between the micro-grid and the main grid as targets; the electric vehicle charging and discharging scheme is transmitted to a micro-grid layer, the minimum comprehensive cost of the micro-grid layer and the minimum interaction power between the micro-grid and a main power grid serve as a micro-grid layer optimization objective function, the micro-grid layer optimization objective function is solved through an improved particle swarm algorithm, and the micro-grid and electric vehicle charging and discharging are adjusted according to the micro-grid and electric vehicle charging and discharging. According to the method, the economical efficiency of the floor height of the electric vehicle and the stability and safety of the floor height of the micro-grid can be simultaneously realized.

Description

Technical Field

[0001] The present invention relates to the technical field of distributed energy operation, and particularly relates to a hierarchical optimization regulation method, medium and device for a microgrid with price incentives. Background Art

[0002] With the further development of the new power system and the continuous popularization of electric vehicles, the behavior of large-scale electric vehicles accessing the microgrid has brought negative impacts on the stability and safe operation of the microgrid system. In order to enable the stable and economic operation of electric vehicles connected to the microgrid, not only is it necessary for electric vehicles to provide orderly charging behavior, but also a certain price incentive mechanism is required to encourage electric vehicle users to participate in power balance regulation to ensure supply-demand balance. Therefore, how to effectively establish the objective function and apply the control strategy still needs in-depth research. Summary of the Invention

[0003] The purpose of the present invention is to provide a hierarchical optimization regulation method, medium and device for a microgrid with price incentives, aiming to achieve the economy of the electric vehicle layer and the stability and safety of the microgrid layer simultaneously when electric vehicles access the microgrid. The specific technical solutions are as follows:

[0004] A hierarchical optimization regulation method for a microgrid with price incentives, the method includes the following steps:

[0005] S100: Taking the charging and discharging of electric vehicles as the bottom layer, based on the time-of-use electricity price mechanism, designing an optimization objective function for the electric vehicle layer to participate in charging and discharging behavior with the cost of electric vehicle users and the comprehensive satisfaction of travel as the objectives;

[0006] S200: Based on the price incentive mechanism, designing an optimization function with the maximum benefit of electric vehicle users and the minimum interaction power between the microgrid and the main grid as the objectives;

[0007] S300: Transmitting the electric vehicle charging and discharging plan to the microgrid layer, which adjusts the power output of controllable distributed power sources in the microgrid layer, taking the minimum comprehensive cost of the microgrid layer and the minimum interaction power between the microgrid and the main grid as the optimization objective function of the microgrid layer, using an improved particle swarm algorithm to solve the optimization objective function of the microgrid layer, obtaining the distributed energy allocation and power output of the microgrid layer and the charging and discharging power allocation of electric vehicles in each time period, and adjusting the microgrid and electric vehicle charging and discharging accordingly.

[0008] Further, in S100, a hierarchical optimization architecture of the microgrid is established. The components of the grid-connected microgrid include photovoltaic, diesel engine, battery, basic load, intelligent charging pile, and information network equipment. Electric vehicle users send information such as vehicle driving time, distance, battery charging status, charging demand during off-peak hours, discharging demand during peak hours, and charging and discharging willingness during normal hours to the control center of the microgrid layer through the connected charging piles.

[0009] The optimization objective function for the charging and discharging behavior of the electric vehicle layer is: during the off-peak and peak hours of the electric vehicle, with the maximum combined satisfaction value of the cost satisfaction and travel satisfaction of the electric vehicle users as the objective function, optimize the charging and discharging power of the electric vehicles participating; including the following formula:

[0010]

[0011] Among them, f 1max is the optimization objective function of the electric vehicle layer, M is the number of electric vehicles within the selected event time period, α i is the satisfaction of the i-th electric vehicle with the cost, taking a value between 0 and 1, β i is the satisfaction of the i-th electric vehicle's journey, taking a value between 0 and 1, a1 and a2 are the weight coefficients of α i and β i , is the highest cost expenditure and the lowest cost expenditure accepted by the i-th electric vehicle user, T is the selected event time period, B i is the expenditure of the i-th electric vehicle, and are the electric vehicle battery output powers of the i-th electric vehicle user at the maximum and minimum journey satisfactions during the time period t, is the battery output power of the i-th electric vehicle during the t-th time period, P i,t is the charging and discharging power of the i-th electric vehicle during the t-th time period, Price t is the price of charging and discharging during the time period t, E change is the electric vehicle battery replacement cost, E max is the maximum charging and discharging power of the electric vehicle battery.

[0012] Further, in S200, a price incentive mechanism is adopted, with the comprehensive electricity price as the objective function, optimize the charging and discharging power of the electric vehicles participating in the regulation, the satisfaction of the electric vehicle users, and the minimum interaction power of the microgrid; among them, the microgrid interaction power refers to the electricity that the microgrid purchases or sells to the main grid within a certain time during normal hours; the optimization function includes the following formula:

[0013]

[0014] f12min = P PV,t + P DE,t + P SB,t + P EV,t + P load,t (6)

[0015] where f 11max and f 12min are the objective functions for optimizing the charging and discharging behaviors of electric vehicle layers designed with the cost of electric vehicle users and the overall satisfaction of travel as the goals under the price incentive mechanism strategy. P PV,t is the total power of photovoltaic power generation in time period t, P DE,t is the total power generated by the fuel generator in time period t, P SB,t is the total power of battery charging and discharging at time t, P EV,t is the total electric power of electric vehicles in the t-th time period, P load,t is the load power in time period t.

[0016] Furthermore, the constraint conditions of the optimization function include:

[0017] Power limit for electric vehicle charging and discharging, the formula is as follows:

[0018]

[0019] where are the maximum powers for electric vehicle charging and discharging respectively;

[0020] Charge and discharge quantity constraint for electric vehicles, the formula is as follows:

[0021] 0 ≤ N t ≤ N max (8)

[0022] where N t is the number of electric vehicles charging and discharging in time period t, N max is the maximum number of charging piles;

[0023] Battery charge state constraint for electric vehicles, the formula is as follows:

[0024] SOC min ≤ SOC i,t ≤ SOC max (9)

[0025]

[0026] where SOC max 、SOC min are the maximum and minimum values of the battery charge state of electric vehicles, SOC i,t 、SOCi,t+1 is the charging state of electric vehicle i during time period t and t+1, S is the rated capacity of the battery, η charge and η discharge are the charging and discharging power coefficients of the electric vehicle respectively.

[0027] Furthermore, in S300, the optimization objective function of the microgrid layer is constructed as follows: with the minimum comprehensive cost of the microgrid layer and the minimum interaction power between the microgrid and the main grid as the objective function, optimize the battery energy allocation of distributed devices and the energy power output of photovoltaic and fuel units at the microgrid level;

[0028] The formula for the minimum comprehensive cost of the microgrid layer is as follows:

[0029] f 2min = B1 + B2 (11)

[0030]

[0031] Among them, f 2min is the minimum comprehensive cost of the microgrid layer, B1 and B2 are the daily operating cost and daily environmental maintenance cost of the microgrid, n1, n2, n3 are the numbers of photovoltaic, fuel generators, and energy storage battery packs, Price PV , Price DE , Price SB is the purchase cost of a single group of photovoltaic, fuel generator, and energy storage battery pack, B PV is the maintenance cost of the photovoltaic, B SB is the sum of the charging / discharging conversion loss cost and battery maintenance cost of the energy storage battery pack, B grid is the interaction cost between microgrids; P DE,i,t is the power output of the i-th diesel generator during time period t;

[0032]

[0033] Among them, u is the number of battery conversion times, C dis / char is the conversion cost of each battery charge / discharge, K SB is the operation and maintenance coefficient of the battery, P SB,i,t is the power of the battery charging and discharging during time period t, P grid,t is the interaction power between the microgrid and the main grid, is the electricity price for the microgrid to purchase and sell from / to the main grid;

[0034] The formula for the minimum interaction power between the microgrid and the main grid is as follows:

[0035]

[0036] Among them, f3min is the minimum interaction power between the microgrid and the main grid, P grid,t is the interaction power value between the microgrid and the main grid at time t.

[0037] Furthermore, the constraint conditions of the microgrid layer optimization objective function include:

[0038] Power balance constraint, the formula is as follows:

[0039]

[0040] Among them, is the power generated by the i-th group of photovoltaics in the t-th time period, P EV,t is the total charging / discharging power value of the electric vehicle in the time period t;

[0041] Distributed power output constraint, the formula is as follows:

[0042]

[0043] Among them, are the lower and upper limits of the output power of the diesel generator;

[0044] Battery charging and discharging power limit, the formula is as follows:

[0045]

[0046] Among them, represent the lower and upper limits of the battery charging and discharging power.

[0047] Battery state of charge limit, the formula is as follows:

[0048] SOC SB,min ≤SOC SB,t ≤SOC SB,max (20)

[0049]

[0050] Among them, SOC SB,t is the state of charge of the battery at time t, SOC SB,min 、SOC SB,max are the lower and upper limits of the battery state of charge, SOC SB,t+1 is the state of charge of the battery at time t + 1, E SB is the rated capacity of the battery, η SB represents the battery charge and discharge efficiency, and v is the battery self-discharge rate.

[0051] Further, the charging and discharging power problem of the electric vehicle layer is solved by mixed integer linear programming to obtain the charging and discharging schedule of electric vehicle users; the improved particle swarm optimization algorithm is used to solve the nonlinear, multi-constraint and multi-objective optimization problem at the microgrid level; the improved particle swarm optimization algorithm improves the number of iterations g(t), and the formula is as follows:

[0052]

[0053] where g(t) is the improved number of iterations, θ and T0 are the decreasing exponent and iteration threshold, d1 and d2 are control factors, ɡ max 、ɡ min are the upper and lower limits of the values taken by the number of iterations, and t max is the upper limit of the taken event time period.

[0054] Further, the improved particle swarm optimization algorithm is used to solve the optimization objective function of the microgrid layer, obtain the distributed energy allocation and power output of the microgrid layer and the charging and discharging power allocation of electric vehicles in each time period, and adjust the microgrid and electric vehicle charging and discharging accordingly, including the following steps:

[0055] S310. Read the parameters of the electric vehicle and the electricity price announced by the power grid through the charging pile;

[0056] S320. The electric vehicle layer solves the optimal charging and discharging power of the electric vehicle according to the optimization objective function and optimization function of the electric vehicle layer participating in the charging and discharging behavior through the transfer function and constraint conditions, and transmits it to the microgrid layer;

[0057] S330. Read the system parameters of the microgrid layer, set the particle swarm parameter values, and initialize the particle swarm;

[0058] S340. The microgrid layer outputs the distributed energy output power, battery output power, reference day load and optimal electric vehicle charging and discharging power information according to the improved particle swarm optimization algorithm, and adopts the dynamic energy scheduling strategy;

[0059] S350. The microgrid layer adjusts the distributed device battery energy allocation and the energy power output of the photovoltaic and fuel units through the improved particle swarm optimization algorithm based on the objective function and constraint conditions, and solves the adjusted Pareto solution to achieve the global optimum.

[0060] The present invention also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above-mentioned price-incentive microgrid hierarchical optimization regulation method are realized.

[0061] The present invention also provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the steps of the above-mentioned hierarchical optimization regulation method for a price-incentivized microgrid are implemented.

[0062] The price-incentivized microgrid hierarchical optimization regulation method, medium, and device provided by the present invention have the following beneficial effects:

[0063] In the price-incentivized microgrid hierarchical optimization regulation method provided by the present invention, taking electric vehicle charging and discharging as the bottom layer, based on the time-of-use electricity price mechanism, an optimization objective function for the charging and discharging behavior of the electric vehicle layer is designed with the cost of electric vehicle users and the comprehensive satisfaction of travel as the goal; based on the price incentive mechanism, an optimization function is designed with the maximum benefit of electric vehicle users and the minimum interaction power between the microgrid and the main grid as the goal; the electric vehicle charging and discharging plan is transmitted to the microgrid layer, which adjusts the power output of the controllable distributed power sources within the microgrid layer. Taking the minimum comprehensive cost of the microgrid layer and the minimum interaction power between the microgrid and the main grid as the optimization objective function of the microgrid layer, an improved particle swarm algorithm is used to solve the optimization objective function of the microgrid layer, obtaining the distributed energy allocation and power output of the microgrid layer and the charging and discharging power allocation of electric vehicles in each time period, and adjusting the microgrid and electric vehicle charging and discharging accordingly; when electric vehicles are connected to the microgrid, high economy of the electric vehicle layer and high stability and security of the microgrid layer are achieved simultaneously. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 is a schematic flowchart of a price-incentivized microgrid hierarchical optimization regulation method provided by an embodiment of the present invention;

[0065] Figure 2 is the distribution diagram of electric vehicle charging and discharging power under a 20% electricity price discount in the verification example of the present invention;

[0066] Figure 3 is the distribution diagram of electric vehicle charging and discharging power under a 30% electricity price discount in the verification example of the present invention;

[0067] Figure 4 is the distribution diagram of electric vehicle charging and discharging power under a 40% electricity price discount in the verification example of the present invention;

[0068] Figure 5 is the distribution diagram of electric vehicle charging and discharging power under a 50% electricity price discount in the verification example of the present invention;

[0069] Figure 6 is the distribution diagram of the comprehensive satisfaction of electric vehicles under different incentive discounts and optimization strategies in the verification example of the present invention;

[0070] Figure 7For the microgrid interaction power under different discounts and optimization strategies in the verification example of the present invention;

[0071] Figure 8 For the microgrid cost per ten thousand yuan under different discounts and optimization strategies in the verification example of the present invention;

[0072] Figure 9 For the solution set situation of non - hierarchical optimization and hierarchical optimization search in the verification example of the present invention;

[0073] Figure 10 It is the structural block diagram of the computer device in the embodiment of the present invention. Specific embodiments

[0074] Next, in combination with the drawings provided by the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described. According to the following description, the advantages and features of the present invention will be clearer. It should be noted that the drawings are all in a very simplified form and use non - precise scales, only for conveniently and clearly assisting in explaining the purpose of the embodiments of the present invention.

[0075] Embodiment 1

[0076] This embodiment provides a price - incentive - based hierarchical optimization regulation method for a microgrid. Referring to Figure 1 as shown, the method includes the following steps:

[0077] S100. Taking the charging and discharging of electric vehicles as the bottom layer, based on the time - of - use electricity price mechanism, and aiming at the comprehensive satisfaction of the cost and travel of electric vehicle users, design the optimization objective function for the charging and discharging behavior of the electric vehicle layer to participate.

[0078] Specifically, in S100, establish a hierarchical optimization architecture for the microgrid. The components of the grid - connected microgrid include photovoltaic, diesel engine, battery, basic load, intelligent charging pile, and information network equipment. Electric vehicle users send information such as vehicle driving time, distance, battery charging status, charging demand during low - valley periods, discharging demand during peak periods, and charging and discharging willingness during normal periods to the control center of the microgrid layer through the connected charging piles.

[0079] Specifically, in S100, the optimization objective function for the charging and discharging behavior of the electric vehicle layer to participate is: during the low - valley and peak periods of electric vehicles, taking the maximum combined satisfaction value of the cost satisfaction and travel satisfaction of electric vehicle users as the objective function, and optimizing the charging and discharging power of electric vehicles participating. It includes the following formula:

[0080]

[0081] Among them, f 1max is the optimization objective function of the electric vehicle layer, M is the number of electric vehicles within the taken event time period, αi is the satisfaction degree of the ith electric vehicle with respect to cost, taking a value between 0 and 1, β i is the satisfaction degree of the ith electric vehicle with respect to travel distance, taking a value between 0 and 1, and a1 and a2 are the weight coefficients of α i and β i are the maximum and minimum cost expenditures acceptable to the ith electric vehicle user, T is the time period of the selected event, B i is the expenditure of the ith electric vehicle, and are the electric vehicle battery output powers of the ith electric vehicle user at the maximum and minimum travel satisfaction degrees within the time period t, is the battery output power of the ith electric vehicle within the tth time period, P i,t is the charging and discharging power of the ith electric vehicle within the tth time period, Price t is the price of charging and discharging within the time period t, E change is the electric vehicle battery replacement cost, E max is the maximum charging and discharging power of the electric vehicle battery.

[0082] S200. Based on the price incentive mechanism, an optimization function is designed with the maximum benefit of electric vehicle users and the minimum interaction power between the microgrid and the main grid as the goals.

[0083] Specifically, in S200, the price incentive mechanism is adopted, with the comprehensive electricity price as the objective function, to optimize the charging and discharging power of electric vehicles participating in regulation, the satisfaction degree of electric vehicle users, and the minimum interaction power of the microgrid; among them, the microgrid interaction power refers to the electricity purchased or sold by the microgrid from the main grid within a certain period of normal time; the optimization function includes the following formula:

[0084]

[0085] f 12min =P PV,t +P DE,t +P SB,t +P EV,t +P load,t (6)

[0086] where f 11max and f 12min are the optimization objective functions for the charging and discharging behaviors of the electric vehicle layer designed with the comprehensive satisfaction of the cost and travel of electric vehicle users as the goal under the price incentive mechanism strategy, P PV,t is the total power of photovoltaic power generation within the time period t, P DE,t is the total power generated by the fuel generator within the time period t, P SB,t ​is the total power of battery charging and discharging within time t, P EV,t is the total power of the electric vehicle within the t-th time period, P load,t is the load power within time period t.

[0087] In the preferred embodiment, the constraint conditions of the optimization function include:

[0088] The charging and discharging power limit of the electric vehicle is as follows:

[0089]

[0090] Wherein, are the maximum charging and discharging powers of the electric vehicle respectively;

[0091] The charging and discharging amount constraint of the electric vehicle is as follows:

[0092] 0 ≤ N t ≤ N max (8)

[0093] Wherein, N t is the number of electric vehicles charging and discharging within time period t, N max is the maximum number of charging piles;

[0094] The charging state constraint of the electric vehicle battery is as follows:

[0095] SOC min ≤ SOC i,t ≤ SOC max (9)

[0096]

[0097] Wherein, SOC max 、SOC min are the maximum and minimum values of the charging state of the electric vehicle battery, SOC i,t 、SOC i,t+1 are the charging states of electric vehicle i at time period t and time period t + 1, S is the rated capacity of the battery, η charge and η discharge are the charging and discharging power coefficients of the electric vehicle respectively.

[0098] S300. Transmit the electric vehicle charging and discharging scheme to the microgrid layer, which adjusts the power output of the controllable distributed power sources in the microgrid layer. Taking the minimum comprehensive cost of the microgrid layer and the minimum interaction power between the microgrid and the main grid as the optimization objective function of the microgrid layer, use the improved particle swarm algorithm to solve the optimization objective function of the microgrid layer, obtain the distributed energy allocation and power output of the microgrid layer and the charging and discharging power allocation of the electric vehicle for each time period, and accordingly adjust the microgrid and the electric vehicle charging and discharging.

[0099] Specifically, in S300, the optimization objective function of the microgrid layer is constructed as follows: with the minimum comprehensive cost of the microgrid layer and the minimum interaction power between the microgrid and the main grid as the objective function, the battery energy allocation of distributed devices at the microgrid level and the energy power output of photovoltaic and fuel units are optimized;

[0100] The formula for the minimum comprehensive cost of the microgrid layer is as follows:

[0101] f 2min = B1 + B2 (11)

[0102]

[0103] Among them, f 2min is the minimum comprehensive cost of the microgrid layer, B1 and B2 are the daily operating cost and daily environmental maintenance cost of the microgrid, n1, n2, and n3 are the numbers of photovoltaic, fuel generators, and energy storage battery packs, Price PV 、Price DE 、Price SB are the purchase costs of single groups of photovoltaic, fuel generators, and energy storage battery packs, B PV is the maintenance cost of the photovoltaic, B SB is the sum of the charging / discharging conversion loss cost and battery maintenance cost of the energy storage battery pack, B grid is the interaction cost between microgrids; P DE,i,t is the power output of the i-th diesel generator within the time period t;

[0104]

[0105] Among them, u is the number of battery conversion times, C dis / char is the conversion cost of the battery for each charge / discharge, K SB is the operation and maintenance coefficient of the battery, P SB,i,t is the power of the battery for charging and discharging within the time period t, P grid,t is the interaction power between the microgrid and the main grid, is the electricity price for the microgrid to purchase and sell from / to the main grid;

[0106] The formula for the minimum interaction power between the microgrid and the main grid is as follows:

[0107]

[0108] Among them, f 3min is the minimum interaction power between the microgrid and the main grid, P grid,t is the interaction power value between the microgrid and the main grid at time t.

[0109] In the preferred embodiment, the constraint conditions of the optimization objective function of the microgrid layer include:

[0110] Power balance constraint, the formula is as follows:

[0111]

[0112] Wherein, is the power generated by the i-th group of photovoltaic in the t-th time period, P EV,t is the total charging / discharging power value of the electric vehicle in the time period t;

[0113] Distributed power output constraint, the formula is as follows:

[0114]

[0115] Wherein, are the lower limit and upper limit of the output power of the diesel generator;

[0116] Battery charging and discharging power limit, the formula is as follows:

[0117]

[0118] Wherein, represent the lower limit and upper limit of the battery charging and discharging power.

[0119] Battery state of charge limit, the formula is as follows:

[0120] SOC SB,min ≤SOC SB,t ≤SOC SB,max (20)

[0121]

[0122] Wherein, SOC SB,t is the state of charge of the battery at time t, SOC SB,min 、SOC SB,max are the lower limit and upper limit of the battery state of charge, SOC SB,t+1 is the state of charge of the battery at time t+1, E SB is the rated capacity of the battery, η SB represents the charge and discharge efficiency of the battery, and v is the self-discharge rate of the battery.

[0123] In one embodiment, the charging and discharging power problem of the electric vehicle layer is solved by mixed integer linear programming to obtain the charging and discharging schedule of electric vehicle users; the improved particle swarm algorithm is used to solve the non-linear, multi-constraint and multi-objective optimization problem at the microgrid level; in order to improve the rapidity and high convergence of the search results of the particle swarm algorithm, the improved particle swarm algorithm improves the number of iterations g(t), and the formula is as follows:

[0124]

[0125] Among them, g(t) is the improved number of iterations, θ and T0 are the decreasing exponents and iteration thresholds, d1 and d2 are control factors, and the optimal values are d1 = 0.2 and d2 = 0.7. g max and g min are the upper and lower limits of the values taken by the number of iterations, and t max is the upper limit of the taken event time period.

[0126] In one embodiment, an improved particle swarm optimization algorithm is used to solve the microgrid layer optimization objective function, obtaining the distributed energy allocation and power output of the microgrid layer and the electric vehicle charging and discharging power allocation for each time period, and accordingly adjusting the microgrid and electric vehicle charging and discharging, including the following steps:

[0127] S310. Read the parameters of the electric vehicle through the charging pile and the electricity price announced by the power grid;

[0128] S320. The electric vehicle layer solves the optimal charging and discharging power of the electric vehicle through the transfer function and constraint conditions according to the charging and discharging behavior optimization objective function and optimization function of the electric vehicle layer, and transfers it to the microgrid layer;

[0129] S330. Read the system parameters of the microgrid layer, set the particle swarm parameter values, and initialize the particle swarm;

[0130] S340. The microgrid layer outputs the distributed energy output power, battery output power, reference day load, and optimal electric vehicle charging and discharging power information according to the improved particle swarm optimization algorithm, and adopts a dynamic energy scheduling strategy;

[0131] S350. The microgrid layer adjusts the distributed device battery energy allocation and the energy power output of the photovoltaic and fuel units through the improved particle swarm optimization algorithm based on the objective function and constraint conditions, and solves the adjustment Pareto solution to achieve global optimality.

[0132] The price-incentive-based hierarchical optimization regulation method for microgrids provided by the present invention takes the charging and discharging of electric vehicles as the bottom layer. Based on the time-of-use electricity price mechanism, an optimization objective function for the charging and discharging behavior of the electric vehicle layer is designed with the cost of electric vehicle users and the comprehensive satisfaction of travel as the goals; based on the price incentive mechanism, an optimization function is designed with the maximum benefit of electric vehicle users and the minimum interaction power between the microgrid and the main grid as the goals; the electric vehicle charging and discharging scheme is transmitted to the microgrid layer, which adjusts the power output of the controllable distributed power sources in the microgrid layer. With the minimum comprehensive cost of the microgrid layer and the minimum interaction power between the microgrid and the main grid as the optimization objective function of the microgrid layer, an improved particle swarm algorithm is used to solve the optimization objective function of the microgrid layer, obtaining the distributed energy allocation and power output of the microgrid layer and the charging and discharging power allocation of electric vehicles in each time period, and accordingly adjusting the microgrid and the charging and discharging of electric vehicles; when electric vehicles are connected to the microgrid, the high economy of the electric vehicle layer and the high stability and safety of the microgrid layer are simultaneously achieved.

[0133] Verification example

[0134] (1) Set the example parameters:

[0135] Taking a community grid-connected microgrid with 100 electric vehicles as an example, the driving habits, grid-connected and off-grid times, and initial battery charging states of electric vehicles are simulated using the Monte Carlo method. The basic information of the electric vehicle batteries is shown in Table 1. The parameters in the system have been set, the battery interval is set to 0.9 and 0.2, the capacity of a single group of photovoltaic power sources is 500 kWp, the capacity of a single group of wind turbines is 405 kW, the rated capacity of the battery pack is 300 kW·h, the maximum output power of other distributed generating units is 300 kW, and the minimum decision-making duration is 15 minutes. The population size of the improved particle swarm algorithm is 500, the total number of iterations is 30, =6, T0=50.

[0136] Table 1 Basic information of electric vehicle layer batteries in the example

[0137]

[0138] (2) Optimization results of the electric vehicle layer

[0139] Consider two other strategies for comparison:

[0140] Strategy 1: Unordered charging mode according to the travel convenience of electric vehicle users; the power distribution in the unordered charging mode of Strategy 1 is from 9:00 am to 1:00 pm and from 6:00 pm to 10:00 pm. These two time periods are mainly used for charging electric vehicle users in the work area and residential area, which leads to peak loads in the area according to the willingness of users to charge disorderly, increasing the load burden on the microgrid layer.

[0141] Strategy 2: Conduct orderly charging and discharging according to the lowest cost expenditure of electric vehicle users. In Strategy 2, the orderly charging mode is guided by time-of-use electricity prices, charging from 0:00 to 7:00 in the early morning and from 22:00 to 24:00 at night. Discharging from 10:00 in the morning to 3:00 in the afternoon and from 7:00 to 9:00 at night, so that electric vehicle users can obtain the greatest benefits. However, most electric vehicle users focus on both charging and discharging at the same time. During the charging and discharging process, new load peak-valley problems will occur.

[0142] A price-incentive hierarchical optimization regulation method for a microgrid of the present invention, the charging and discharging power of an electric vehicle is as Figures 2 - 5 shown. Different from Strategy 1 of disorderly charging and Strategy 2 of the lowest charging cost, it effectively reduces the problems of high load valley difference and expenditure cost, and at the same time effectively balances travel satisfaction and cost to obtain the best comprehensive satisfaction. As Figures 2 - 5 shown, the hierarchical optimization strategy including price incentives better promotes the participation of electric vehicles in microgrid dispatching. As the incentive discount increases, more and more electric vehicles participate in microgrid energy dispatching. Among them, the hierarchical optimization strategy for the conventional segment promotes 9.16%, 11.03%, 10.56% and 10.94% of electric vehicle users to participate in microgrid energy dispatching under a discount of 20% to 50% compared with the non-hierarchical optimization strategy, and the number of vehicles participating in microgrid power dispatching is the largest under a 30% discount.

[0143] From Figure 6 the histogram, it can be seen that under a discount of 20% to 50%, the hierarchical optimization strategy improves the overall satisfaction of electric vehicle users by 2.57%, 3.26%, 1.72% and 0.33% compared with the non-hierarchical optimization strategy, and the effect of hierarchical optimization is the most obvious under a 30% discount; under a 20% discount, the hierarchical optimization strategy only makes the overall satisfaction of electric vehicle users 50.7%; within the discount range of 30% to 50%, the change in the comprehensive satisfaction of electric vehicle users by the hierarchical optimization strategy does not exceed 3%.

[0144] 3) Microgrid layer optimization results

[0145] Transfer the optimal charging and discharging power of the electric vehicle layer under different discounts to the microgrid layer. The microgrid layer, together with the base load and photovoltaic output, constitutes the net load and participates in the dynamic dispatching of controllable distributed energy within the microgrid layer. The Pareto front is obtained by solving the improved particle swarm parameters. Table 2 shows the optimal solutions under different discounts. Combining the satisfaction of electric vehicle users and the microgrid target value, when the discount is 30%, the benefits of both the electric vehicle layer and the microgrid layer can be optimized. Therefore, the microgrid interaction power is 6132.5 kW and the cost is 10,000 yuan. From Figures 7 - 8It can be seen that the optimization strategies with 20% to 50% discounts optimize the economic indicators by 23.14%, 26.07%, 19.37%, and 1.86% compared to the optimization strategy without stratification. The microgrid economy is maximized at a 30% discount. The system safety and stability indicators are optimized by 695.31%, 1209.57%, 1196.19%, and 796.65%. The microgrid safety and stability are optimal below a 30% discount.

[0146] Table 2 Optimal values of the microgrid layer optimization objectives under different incentive discounts and optimizations

[0147]

[0148]

[0149] As Figure 9 shown, under the hierarchical optimization strategy, the distribution of the optimal solution set is closer to the ideal Pareto solution set. In the case of the non - hierarchical optimization strategy, the optimal solution set shows a random phenomenon, indicating that the hierarchical optimization strategy is reliable for microgrid regulation.

[0150] Based on the above comparisons in different scenarios, for the problem of optimizing the orderly charging regulation of electric vehicles, the present invention proposes a hierarchical optimization regulation method for a microgrid based on price incentives. At the electric vehicle layer, the charging and discharging behavior of electric vehicle users is optimized with the goal of the comprehensive satisfaction of the cost and travel of electric vehicle users. On this basis, a price incentive mechanism is added. With the goal of the comprehensive satisfaction of electric vehicle users and the minimum interaction power of the microgrid layer, it promotes electric vehicle users to participate in the power balance of the microgrid layer. The charging and discharging plan of electric vehicles is optimized and transmitted to the microgrid layer. Based on this, the objective functions of the minimum operating cost and the minimum system interaction power of the microgrid layer are established, and the improved particle swarm parameters are used to solve the distributed energy allocation and power output of the microgrid layer and the charging and discharging power allocation of electric vehicles in each time period. The improved particle swarm algorithm parameters have good Pareto - front convergence for high - dimensional non - linear multi - objective optimization models. Hierarchical optimization can better achieve a win - win situation in terms of economy and stability between the electric vehicle layer and the microgrid layer. When the electricity price discount is 30%, the optimization results of the electric vehicle layer and the microgrid layer are the best, which demonstrates the effectiveness and practicality of the technical solution of the present invention.

[0151] Embodiment 2

[0152] This embodiment provides a computer - readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the steps of the aforementioned hierarchical optimization regulation method for a microgrid with price incentives.

[0153] Among them, the storage medium can be a magnetic disk, an optical disc, a read-only memory (ROM), a random access memory (RAM), a flash memory, a hard disk drive (HDD), or a solid-state drive (SSD), etc.; the storage medium can also include a combination of the above-mentioned types of memories.

[0154] Embodiment 3

[0155] This embodiment provides a computer device, which includes: a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the steps of the price-incentive-based hierarchical optimization regulation method for a microgrid described above.

[0156] As Figure 10 shown, the computer device may include: at least one processor 71, such as a CPU (Central Processing Unit, central processor), at least one communication interface 73, a memory 74, and at least one communication bus 72. Among them, the communication bus 72 is used to realize the connection and communication between these components. Among them, the communication interface 73 may include a display screen (Display) and a keyboard (Keyboard). Optionally, the communication interface 73 may further include a standard wired interface and a wireless interface. The memory 74 may be a high-speed RAM memory (Random Access Memory, volatile random access memory), or a non-volatile memory, such as at least one disk memory. Optionally, the memory 74 may further be at least one storage device located far from the aforementioned processor 71. Among them, an application program is stored in the memory 74, and the processor 71 calls the program code stored in the memory 74 to execute any of the above method steps.

[0157] Among them, the communication bus 72 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The communication bus 72 can be divided into an address bus, a data bus, a control bus, etc. For the sake of convenience of representation, Figure 10 only a thick line is shown in the figure, but it does not mean that there is only one bus or one type of bus.

[0158] Among them, the memory 74 may include volatile memory, such as random-access memory (RAM); the memory may also include non-volatile memory, such as flash memory, hard disk drive (HDD) or solid-state drive (SSD); the memory 74 may further include a combination of the above types of memory.

[0159] Among them, the processor 71 may be a central processing unit (CPU), a network processor (NP), or a combination of a CPU and an NP.

[0160] Among them, the processor 71 may further include a hardware chip. The above hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The above PLD may be a complex programmable logic device (CPLD), a field-programmable gate array (FPGA), a generic array logic (GAL), or any combination thereof.

[0161] Optionally, the memory 74 is further used to store program instructions. The processor 71 may call the program instructions to implement the hierarchical optimization regulation method of the price-incentive microgrid as described in the present invention.

[0162] Those skilled in the art of the present technology should understand that the present invention may be implemented in many other specific forms without departing from the spirit and scope of the present invention. Based on the embodiments of the present invention, any changes and modifications made by those of ordinary skill in the art of the present invention according to the above disclosure shall fall within the protection scope of the claims.

Claims

1. A price-incentive microgrid hierarchical optimization and regulation method, characterized in that: The method comprises the following steps: S100, taking electric vehicle charging and discharging as the bottom layer, based on the time-of-use electricity price mechanism, and taking the cost and comprehensive travel satisfaction of electric vehicle users as the target, the electric vehicle layer participates in the charging and discharging behavior optimization objective function is designed; S200, based on the price incentive mechanism, the optimization function is designed with the maximum benefit of electric vehicle users and the minimum interaction power between the microgrid and the main grid as the goal; S300, passing the electric vehicle charging and discharging plan to the microgrid layer, which adjusts the power output of the controllable distributed power source in the microgrid layer, takes the minimum comprehensive cost of the microgrid layer and the minimum interaction power between the microgrid and the main grid as the microgrid layer optimization objective function, adopts the improved particle swarm algorithm to solve the microgrid layer optimization objective function, obtains the distributed energy distribution and power output of the microgrid layer and the electric vehicle charging and discharging power distribution in each time period, and adjusts the microgrid and electric vehicle charging and discharging accordingly.

2. The price-incentive microgrid hierarchical optimization and regulation method according to claim 1 is characterized in that: In S100, a hierarchical optimization architecture of microgrids is established. The components of the grid-connected microgrid include photovoltaics, diesel engines, batteries, basic loads, smart charging piles and information network equipment. Electric vehicle users send information such as vehicle driving time, distance, battery charging status, charging demand during off-peak hours, discharging demand during peak hours, and charging and discharging willingness during normal hours to the control center of the microgrid layer through the connected charging piles. The objective function for optimizing the charging and discharging behavior of electric vehicles is: during the off-peak and peak hours of electric vehicles, the maximum combined satisfaction value of the cost satisfaction and travel satisfaction of electric vehicle users is used as the objective function to optimize the charging and discharging power of electric vehicles participating in the electric vehicle; including the following formula: Among them, f 1max is the electric vehicle layer optimization objective function, M is the number of electric vehicles in the event time period, α i is the satisfaction of the i-th electric car with the cost, taking a value between 0 and 1, β i is the satisfaction with the trip of the i-th electric car, taking a value between 0 and 1, and a1 and a2 are α i and β i The weight coefficient of is the maximum and minimum cost expenditures accepted by the i-th electric vehicle user, T is the event time period, and B i is the expenditure of the ith electric car, and is the battery output power of the electric vehicle when the i-th electric vehicle user has the maximum and minimum travel satisfaction in time period t, is the battery output power of the ith electric vehicle in the tth time period, P i,t is the charging and discharging power of the i-th electric vehicle in the t-th time period, Price t is the price of charging and discharging in time period t, E change is the replacement cost of electric vehicle batteries, E max It is the maximum charge and discharge power of an electric vehicle battery.

3. The price-incentive microgrid hierarchical optimization and regulation method according to claim 2 is characterized in that: In S200, a price incentive mechanism is adopted, and the comprehensive electricity price is used as the objective function to optimize the charging and discharging power of electric vehicles participating in the regulation, the user satisfaction of electric vehicles, and the minimum interactive power of the microgrid; wherein the microgrid interactive power refers to the power purchased or sold by the microgrid to the main grid within a certain period of time during normal hours; the optimization function includes the following formula: f 12min =P PV,t +P DE,t +P SB,t +P EV,t +P load,t (6) Among them, f 11max and f 12min The objective function of optimizing the charging and discharging behavior of electric vehicles is designed based on the cost and comprehensive travel satisfaction of electric vehicle users under the price incentive mechanism strategy. PV,t is the total power of photovoltaic power generation in time period t, P DE,t is the total power generated by the fuel generator in time period t, P SB,t is the total power of battery charging and discharging in time t, P EV,t is the total power of the electric vehicle in the tth time period, P load,t is the load power in time period t.

4. The price-incentive microgrid hierarchical optimization and regulation method according to claim 3 is characterized in that: The constraints of the optimization function include: The charging and discharging power limit of electric vehicles is as follows: in, are the maximum power for charging and discharging electric vehicles, respectively; The constraint of electric vehicle charging and discharging capacity is as follows: 0≤N t ≤N max (8) Among them, N t is the number of electric vehicles charged and discharged in time period t, N max is the maximum number of charging posts; The electric vehicle battery charging state constraint is as follows: SOC min ≤SOC i,t ≤SOC max (9) Among them, SOC max , SOC min It is the maximum and minimum value of the state of charge of the electric vehicle battery, SOC i,t , SOC i,t+1 is the charging state of electric vehicle i during time period t and time period t+1, S is the rated capacity of the battery, η charge and η discharge are the charging and discharging power coefficients of electric vehicles respectively.

5. The price-incentive microgrid hierarchical optimization and regulation method according to claim 4 is characterized in that: In S300, the optimization objective function of the microgrid layer is constructed as follows: taking the minimum comprehensive cost of the microgrid layer and the minimum interaction power between the microgrid and the main grid as the objective function, the distributed equipment battery energy distribution at the microgrid level and the energy and power output of the photovoltaic and fuel units are optimized; The minimum microgrid layer comprehensive cost formula is as follows: <h2 style=";text-align:left;direction:ltr">f<h2 style=";text-align:left;direction:ltr"> 2min <h2 style=";text-align:left;direction:ltr"> =B1+B2 (11) Among them, f 2min is the minimum comprehensive cost of the microgrid layer, B1 and B2 are the daily operation cost and daily environmental maintenance cost of the microgrid, n1, n2, n3 are the number of photovoltaic, fuel generator, and energy storage battery group, Price PV 、Price DE 、Price SB is the purchase cost of a single photovoltaic, fuel generator, and energy storage battery pack, B PV is the maintenance cost of photovoltaics, B SB It is the sum of the charge / discharge conversion loss cost of the energy storage battery pack and the battery maintenance cost. grid is the interaction cost between microgrids; P DE,i,t is the power output of the i-th group of diesel generators in time period t; Where u is the number of battery conversions, C dis / char is the conversion cost of each charge / discharge of the battery, K SB is the battery operation and maintenance factor, P SB,i,t is the power of charging and discharging the battery in time period t, P grid,t is the interaction power between the microgrid and the main grid, is the price of electricity purchased and sold by the microgrid from the main grid; The minimum interactive power formula between the microgrid and the main grid is as follows: Among them, f 3min is the minimum interaction power between the microgrid and the main grid, P grid,t is the interactive power value between the microgrid and the main grid at time t.

6. The price-incentive microgrid hierarchical optimization and regulation method according to claim 5 is characterized in that: The constraints of the optimization objective function at the microgrid level include: Power balance constraint, the formula is as follows: in, is the power generated by the ith photovoltaic group in the tth time period, P EV,t is the total charging / discharging power value of the electric vehicle in time period t; Distributed power output constraint, the formula is as follows: in, It is the lower and upper limits of the diesel generator output power; Battery charging and discharging power limit, the formula is as follows: in, Indicates the lower and upper limits of battery charging and discharging power. Battery charge state limit, the formula is as follows: SOC SB,min ≤SOC SB,t ≤SOC SB,max (20) Among them, SOC SB,t is the state of charge of the battery at time t, SOC SB,min , SOC SB,max is the lower and upper limits of the battery state of charge, SOC SB,t+1 is the charge state of the battery at time t+1, E SB is the rated capacity of the battery, η SB represents the battery charging and discharging efficiency, and v is the battery self-discharge rate.

7. The price-incentive microgrid hierarchical optimization and regulation method according to claim 6 is characterized in that: The charging and discharging schedule of electric vehicle users is obtained by solving the mixed integer linear programming charging and discharging power problem at the electric vehicle layer; The improved particle swarm algorithm is used to solve nonlinear, multi-constraint and multi-objective optimization problems at the microgrid level; the improved particle swarm algorithm improves the number of iterations g(t), and the formula is as follows: Among them, g(t) is the number of improved iterations, θ and T0 are the decreasing index and iteration threshold, d1 and d2 are control factors, and ɡ max 、ɡ min are the upper and lower limits of the number of iterations, t max is the upper limit of the event time period.

8. The price-incentive microgrid hierarchical optimization and regulation method according to claim 7 is characterized in that: The improved particle swarm algorithm is used to solve the optimization objective function of the microgrid layer, and the distributed energy distribution and power output of the microgrid layer and the charging and discharging power distribution of electric vehicles in each time period are obtained. Based on this, the charging and discharging of the microgrid and electric vehicles are adjusted, including the following steps: S310, reading the parameters of the electric vehicle and the electricity price published by the power grid through the charging pile; S320, the electric vehicle layer optimizes the objective function and the optimization function according to the charging and discharging behavior of the electric vehicle layer, solves the optimal charging and discharging power of the electric vehicle through the transfer function and the constraint conditions, and transmits it to the microgrid layer; S330, read microgrid layer system parameters, set particle swarm parameter values, and initialize the particle swarm; S340, the microgrid layer outputs the distributed energy output power, battery output power, benchmark daily load and optimal electric vehicle charging and discharging power information according to the improved particle swarm algorithm, and adopts a dynamic energy scheduling strategy; S350, the microgrid layer adjusts the distributed equipment battery energy distribution and the photovoltaic and fuel unit energy and power output through the improved particle swarm algorithm based on the objective function and constraints, and solves the Pareto solution to achieve the global optimum.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the price-incentivized microgrid hierarchical optimization and regulation method as described in any one of claims 1 to 8 are implemented.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of the price-incentivized microgrid hierarchical optimization and regulation method as described in any one of claims 1 to 8 are implemented.

Images