Microgrid dispatching method based on electric vehicle dispatching potential and travel risk

Abstract

The application discloses a kind of microgrid scheduling methods based on electric vehicle scheduling potential and travel risk, comprising the following steps: S1, the two-stage scheduling model of industrial microgrid is established, the two-stage scheduling model includes day-ahead stage scheduling model and real-time stage scheduling model;S2, the two-stage scheduling model established in S1 is initialized;S3, the day-ahead stage scheduling model is mixed integer linear model, and is solved using Gurobi solver, to obtain the optimal scheduling plan of industrial microgrid;Real-time stage scheduling model solves real-time adjustment cost and electric vehicle charging and discharging plan using double-layer optimization model, the optimal scheduling plan of industrial microgrid is substituted into real-time stage scheduling model, the objective function of real-time stage scheduling model is solved, and the optimal charging and discharging plan of electric vehicle is obtained.The method obtains the optimal scheduling plan of industrial microgrid by the two-stage model of industrial microgrid based on electric vehicle scheduling potential and travel risk.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of micro-grid scheduling strategy, and particularly relates to a micro-grid scheduling method based on scheduling potential and travel risk of electric vehicles. BACKGROUND

[0002] Renewable energy will play an important role in the energy transformation process of the power system due to its environmental friendliness and renewability. However, the volatility, intermittency and uncertainty of renewable energy generation may have a negative impact on the stable operation of the power grid. Micro-grid, as a new type of power system, can reduce the impact of the uncertainty of renewable energy on the power grid, and with the characteristics of high proportion of renewable energy generation, high degree of power automation, and customizable personalized power services, it has become one of the key researches in the transformation of the power system. However, the uncertainty of renewable energy and load brings great challenges to the operational stability and reliability of the micro-grid, thereby affecting the power quality of the power grid and users. One of the effective measures to solve this problem is to use electric vehicles to connect to the grid, and through the orderly charging and discharging of electric vehicles, the burden of the micro-grid during the power consumption peak can be reduced.

[0003] In summary, with the increasing development of electric vehicles and the increasing penetration rate of electric vehicles in the power grid, an effective solution to the uncertainty of renewable energy of the micro-grid is to apply the vehicle-to-grid technology of electric vehicles to provide coordinated charging and discharging auxiliary services for the micro-grid, and to realize the stable operation of the micro-grid. However, when electric vehicles provide auxiliary services, it will inevitably cause battery wear and tear of electric vehicle users and affect travel. How to utilize electric vehicles while meeting the travel needs of users and increasing the willingness of users to participate, realizing the benign interaction between electric vehicles and micro-grids and improving the enthusiasm of electric vehicle users to participate in providing auxiliary services will play an important role in the future power system industry and energy field. Therefore, analyzing the application of electric vehicle scheduling potential and travel risk in industrial micro-grid scheduling will provide certain guidance for the stable and reliable operation of industrial micro-grids. SUMMARY

[0004] The present application is based on the deficiencies of the prior art, and the micro-grid scheduling method based on the scheduling potential and travel risk of electric vehicles, which obtains the optimal scheduling plan of the industrial micro-grid through the two-stage model of the industrial micro-grid based on the scheduling potential and travel risk of electric vehicles, including the optimal day-ahead scheduling plan of the industrial micro-grid, the real-time correction plan and the optimal charging and discharging plan of the real-time electric vehicle.

[0005] In order to solve the above technical problems, the technical scheme of the present application is as follows:

[0006] A micro-grid scheduling method based on the scheduling potential and travel risk of electric vehicles, comprising the following steps:

[0007] S1, a two-stage scheduling model of the industrial micro-grid is established, the two-stage scheduling model comprising a day-ahead stage scheduling model and a real-time stage scheduling model,

[0008] The day-ahead stage scheduling model takes the minimum industrial micro-grid operation cost as an optimization target, and a target function thereof is:

[0009] min F ahaed = F1+ F CVaR

[0010] Wherein, F1 is a micro-grid operation cost, F CVaR is a conditional risk cost caused by net load uncertainty;

[0011] The real-time stage scheduling model takes the minimum industrial micro-grid real-time correction cost and the maximum cooperation alliance benefit as optimization targets, and a target function thereof is:

[0012]

[0013] Wherein, is an incentive compensation cost that the micro-grid needs to give to the electric vehicle alliance at time t, i.e., total alliance benefits, is an additional penalty cost that the micro-grid needs to pay to the grid at time t, is a risk cost of the charging flexibility and power of the electric vehicle i at time t;

[0014] S2, the two-stage scheduling model established in S1 is initialized;

[0015] S3, the day-ahead stage scheduling model is a mixed integer linear model, and is solved by using a Gurobi solver to obtain an optimal scheduling plan of the industrial micro-grid;

[0016] The real-time stage scheduling model adopts a double-layer optimization model to solve the real-time adjustment cost and the charging and discharging plan of the electric vehicle, the optimal scheduling plan of the industrial micro-grid is substituted into the real-time stage scheduling model, the target function of the real-time stage scheduling model is solved, and an optimal charging and discharging plan of the electric vehicle is obtained.

[0017] As preferred, the target function of the day-ahead stage scheduling model is as follows:

[0018] Taking the industrial micro-grid power consumption cost F ahaed as an optimization target, a target function thereof is:

[0019]

[0020] Wherein, F1 is a micro-grid operation cost, F CVaR is a conditional risk cost caused by net load uncertainty, The grid purchase cost, the photovoltaic operation cost, and the wind turbine operation cost at time t, respectively; The net power of the microgrid at time t, The grid sale price at time t, CVaR t The average net load error of the microgrid when exceeding the allowable interval at time t, H(Er t ) is a penalty function when exceeding the power allowable interval, Er t The net load error at time t, VaR t The boundary of the power allowable interval at time t, ΔEr is the ceiling function, and D is the basic penalty multiple, [x] + The ceiling function.

[0021] As preferred, the objective function of the real-time stage scheduling model is as follows:

[0022] The real-time stage is a node with a unit of 15 minutes, and each time node optimizes the minimum microgrid real-time adjustment cost and the maximum electric vehicle alliance benefit, and the objective function is:

[0023]

[0024] Among them, The incentive compensation cost of the microgrid to the electric vehicle alliance at time t, that is, the total income of the alliance, The additional penalty cost of the microgrid to the grid at time t, The actual net load error, The actual net load of the microgrid, VaR is the allowable error, The risk cost of the electric vehicle for one-time sale of charging flexibility and electricity, that is, the separate participation in scheduling income; Z is the number of large alliance members.

[0025] As preferred, the day-ahead stage scheduling model is provided with a constraint condition, and the constraint condition of the day-ahead stage scheduling model includes a microgrid power balance constraint, a microgrid peak-valley difference constraint, a microgrid interaction power constraint, and an EV charging constraint.

[0026] As preferred, the real-time stage scheduling model is provided with a constraint condition, and the constraint condition of the real-time stage scheduling model includes a real-time power balance constraint, a grid constraint, and an EV constraint.

[0027] As preferred, in the step S2, the parameters of the day-ahead stage scheduling model are initialized, including the industrial microgrid basic load The photovoltaic output power The fan output power Time-of-use price of grid, charging price, number of time periods T, number of electric vehicles N, rated capacity of electric vehicle battery E, electric vehicle trip characteristics, electric vehicle charging power Charging and discharging efficiency

[0028] As preferred, in the step S2, the parameters of the real-time stage scheduling model are initialized, including time-of-use price of grid, charging price, number of time periods T, number of electric vehicles N, rated capacity of electric vehicle battery E, electric vehicle trip characteristics, electric vehicle charging and discharging power Charging and discharging efficiency Cyclic life of electric vehicle battery under nominal conditions CL nom Depth of discharge of electric vehicle battery DoD nom Purchase and recycling price of electric vehicle battery

[0029] As preferred, in the step S3, the solving method of the day-ahead stage scheduling model is as follows:

[0030] Industrial micro-grid basic load Photovoltaic output power Fan output power Calculate the day-ahead predicted industrial micro-grid net load The expression is as follows:

[0031]

[0032] Industrial micro-grid net load error Er t The expression is as follows:

[0033]

[0034] The actual net load power is historical;

[0035] From the historical net load error data of the industrial micro-grid and the day-ahead predicted industrial micro-grid data, the conditional risk cost F caused by the uncertainty of the net load is calculated by using the CVaR method CVaR And the net load power tolerance interval, i.e. VaR;

[0036] Number of electric vehicles N, rated capacity of electric vehicle battery E, electric vehicle trip characteristics, electric vehicle charging power Charging and discharging efficiency According to the electric vehicle trip distribution, the electric vehicle adjustable potential is calculated by using the Minkowski additivity;

[0037] Electric vehicle dispatchable potential, conditional risk cost F CVaR, the industrial micro-grid predicted net load, to minimize the industrial micro-grid day-ahead electricity cost as the goal to build a mixed integer linear model, using Gurobi solver to solve the mixed integer linear model to obtain the optimal power purchase and the optimal charging power Form the day-ahead optimal scheduling plan.

[0038] As preferred, the net load uncertainty caused by the condition risk cost F CVaR And the calculation method of the net load power tolerance interval VaR is as follows:

[0039] The loss function Wherein The decision variable and the random variable, namely the actual net load power and the predicted net load power, are designed according to the historical data of the predicted net load power. The probability density θ(Er t ) of the predicted net load power, the decision variable And the threshold δ are determined, the cumulative distribution function of the loss function And the loss function As follows:

[0040]

[0041] Therefore, under a certain confidence level α∈(0,1), for the determined decision variable The expression of the VaR function is as follows:

[0042]

[0043] Wherein, the confidence level α is determined by the decision maker according to the system internal resources and the resource schedulable capacity, and R is a real set;

[0044] The expression of CVaR is as follows:

[0045]

[0046] Simplify the CVaR expression by constructing an auxiliary function:

[0047]

[0048] By sampling points instead of integration, the above formula can be further simplified as:

[0049]

[0050] Wherein, N is the total number of samples, and the threshold δ is an auxiliary variable, and the optimal value thereof is VaR;

[0051] Therefore, the conditional risk value cost F CVaR As follows:

[0052]

[0053] As preferred, in the step S3, the method for calculating the adjustable potential of the electric vehicle is:

[0054] The electric vehicle trip feature quantity is obtained and initialized by the charging pile of the charging station in the micro-grid, the electric vehicle trip feature quantity includes the initial SOC, the arrival time, the departure time reported by the electric vehicle user, the expected SOC information, the load model corresponding to the electric vehicle individual is established through the electric vehicle trip feature quantity, and the expression is as follows:

[0055]

[0056] Among them, Respectively, the charging and discharging rates of the electric vehicle at t time; Respectively, the upper limit of the charging and discharging power of the electric vehicle; Respectively, the Boolean variable of the charging and discharging state of the electric vehicle, The Boolean variable of the arrival state of the electric vehicle, when The electric vehicle can charge and discharge; The state of charge of the electric vehicle at t time, SOC x，min , SOC x，max Respectively, the upper and lower limits of the state of charge of the electric vehicle; Respectively, the charging and discharging efficiency of the electric vehicle; Δt is the time period of charging and discharging of the electric vehicle; t ar , t le Respectively, the arrival time and the departure time of the electric vehicle, x is the type of the electric vehicle;

[0057] In view of the difference between the electric vehicle individuals in arrival and departure time, the Boolean variable All the access time of the electric vehicle to the industrial micro-grid is unified to the same definition domain through the Boolean variable, the power and the amount of electricity of the charging station electric vehicle aggregation are calculated by using the Minkowski additivity, and the expression is as follows:

[0058]

[0059] Among them, SOC n，ar , SOC n，le Respectively, the state of charge of the battery at the arrival time and the departure time of the electric vehicle; X EV Is the set of types of electric vehicles;

[0060] For the variable, parameter and adjustable potential Ω EV of the charging station electric vehicle aggregation, the expression is as follows:

[0061]

[0062] wherein, are the charging and discharging power of the charging station electric vehicle aggregation at time t, respectively, and are the electric quantity of the charging station electric vehicle aggregation at time t, respectively; are the upper limits of the charging and discharging of the charging station electric vehicle aggregation at time t, respectively; are the upper and lower limits of the electric quantity of the charging station electric vehicle aggregation at time t, respectively; ΔSOC t is the change of the electric quantity of the charging station electric vehicle aggregation at time t caused by the driving behavior of the electric vehicle user.

[0063] As preferred, in the step S3, the solving method of the real-time stage scheduling model is:

[0064] Through the electric vehicle charging pile, the expected battery charge of the electric vehicle x at time t is obtained and initialized The current battery charge SOC at time t x，t , the residence time T stop , the required charging time T ch , the travel risk of the electric vehicle is calculated by modeling the travel risk through the generalized clock membership function, and the expression is as follows:

[0065]

[0066] wherein, SOC x，t are the travel risk and state of charge of the electric vehicle at time t, respectively, is the expected SOC when the EV leaves, and σ is the ratio of the current required charging time T ch to the residence time T stop ;

[0067] The electric vehicle battery attenuation model is used to calculate the degree of battery charge attenuation caused by the sale of electric vehicle electric quantity, and the expression is as follows:

[0068]

[0069] wherein, CL nom , DoD nom is the cycle life and discharge depth of the electric vehicle battery under nominal conditions, e t，x is the current discharge electric quantity, E x is the maximum electric quantity of the electric vehicle battery;

[0070] Through the electric vehicle travel risk function and the degree of battery charge attenuation, the risk cost of the sale of charging flexibility and electric quantity of the electric vehicle at this time is calculated by using the incentive-based electric vehicle compensation method, and the expression is as follows:

[0071]

[0072] wherein, is the power of the electric vehicle participating in the dispatch at time t, is the maximum charging price of the industrial micro-grid, is the selling price of the EV battery purchased alone, is the EV battery scrap recycling price;

[0073] Initialize the parameters related to the industrial micro-grid, electric vehicle and cooperation alliance at time t, including the industrial micro-grid power gap, grid price, electric vehicle travel risk and battery selling and recycling cost;

[0074] Set the initial number of cooperation alliance electric vehicles M, expressed as follows:

[0075]

[0076] wherein, ΔP del is the industrial micro-grid power gap, is the minimum non-zero discharge power of the cooperation alliance electric vehicle, is the optimal power purchase power;

[0077] According to the number of participating electric vehicles, the total power of the electric vehicle response is calculated The lower model is passed into the double-layer optimization model;

[0078] The lower model calculates the real-time adjustment cost without the participation of electric vehicles, and then extracts EVs that meet the requirements according to the total power of the electric vehicle response and the number of electric vehicles, with the minimum industrial micro-grid real-time adjustment cost as the optimization objective, and uses the Gurobi solver to optimize and solve the industrial micro-grid real-time adjustment cost and the total benefit of the cooperation alliance participating in the regulation, which is passed into the upper model;

[0079] The upper model in the double-layer optimization model calculates the contribution degree of the electric vehicle individual in the alliance according to the non-linear energy mapping function, thereby calculating the benefit obtained by the electric vehicle individual in the alliance, expressed as follows:

[0080]

[0081] wherein, are the output power of member i and the maximum output power of the members in the alliance, respectively; is the risk coefficient of member i; is the non-linear energy contribution value of member i, F(x) is a non-linear mapping function, and the exponential function of natural logarithm e is adopted; γ i is the overall contribution degree of member i;

[0082] If the number of electric vehicles reaches the upper and lower limits, the iteration is stopped, and the real-time adjustment cost and individual profit of electric vehicles are output, otherwise, the number of electric vehicles is adjusted, and the total power response of electric vehicles is recalculated

[0083] The present application has the following characteristics and advantages:

[0084] By using the above technical solution, the two-stage model of industrial micro-grid based on the scheduling potential of electric vehicles and the travel risk is suitable for research, planning and optimization problems between multiple subjects, so as to realize the orderly charging and discharging of electric vehicles, guarantee the travel demand and basic interests of electric vehicle users, reduce the operation cost of industrial micro-grid, and realize the stable and reliable operation of industrial micro-grid. The two-stage scheduling method of the industrial micro-grid can better meet the demand of the actual industrial micro-grid scheduling problem, which can not only reduce the operation cost of the industrial micro-grid and alleviate the phenomenon of "peak on peak" of the industrial micro-grid, but also meet the travel demand of electric vehicle users and reduce the charging cost of electric vehicle users. BRIEF DESCRIPTION OF DRAWINGS

[0085] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0086] Figure 1 The two-stage scheduling flowchart of the industrial micro-grid in the embodiment of the present application.

[0087] Figure 2 The industrial micro-grid load curve comparison before and after day-ahead scheduling in the embodiment of the present application.

[0088] Figure 3 The electric vehicle schedulable potential in the day-ahead stage in the embodiment of the present application.

[0089] Figure 4 The electric vehicle scheduling result in the embodiment of the present application.

[0090] Figure 5 The relationship between the electric vehicle characteristic quantity and the travel risk in the embodiment of the present application.

[0091] Figure 6 The real-time correction stage industrial micro-grid load curve comparison in the embodiment of the present application.

[0092] Figure 7A result comparison chart before and after real-time stage power purchase power adjustment in the embodiment of the present application.

[0093] Figure 8 The overall charging and discharging situation of the electric vehicle in the real-time correction stage in the embodiment of the present application. DETAILED DESCRIPTION

[0094] It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict.

[0095] In order to make the purpose, technical scheme and advantages of the present application more clear, the present application will be further described in detail below with reference to the drawings.

[0096] On the contrary, the present application covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present application defined by the claims. Further, in order to make the public have a better understanding of the present application, some specific details are described in detail in the following detailed description of the present application. The present application can also be completely understood without the description of these details by those skilled in the art.

[0097] The present application relates to an optimization method of industrial microgrid scheduling problem, and generates an industrial microgrid day-ahead scheduling and real-time correction plan with the lowest operation cost by using a two-stage scheduling model, and the optimization objective of the two-stage scheduling model is to minimize the operation cost of the industrial microgrid and the real-time correction cost, as shown in the formula (1). Figure 1 The purpose of the present application is to propose a microgrid scheduling method based on the scheduling potential and travel risk of electric vehicles, so as to realize the orderly charging and discharging of electric vehicles, realize the protection of travel demand and basic interests of electric vehicle users, realize the reduction of operation cost of industrial microgrid, and realize the stable and reliable operation of industrial microgrid. In order to illustrate the effect of the present application, the industrial microgrid system is taken as the implementation object of the present application to describe the present application in detail:

[0098] S1, a two-stage scheduling model of the industrial microgrid is established, and the two-stage scheduling model includes a day-ahead scheduling model and a real-time scheduling model.

[0099] I) The day-ahead scheduling model takes the minimum operation cost of the industrial microgrid as the optimization objective, and the objective function is:

[0100] min F ahaed =F1+F CVaR (2)

[0101] Wherein, F1 is the operation cost of the microgrid, and F CVaR is the conditional risk cost caused by the uncertainty of the net load.

[0102] Specifically, the micro-grid operation cost F1 generally includes grid purchase cost, photovoltaic operation cost, wind turbine operation cost, and the expression is as follows:

[0103]

[0104] wherein, are the grid purchase cost, the photovoltaic operation cost, and the wind turbine operation cost at time t respectively.

[0105] Further, the grid purchase cost is calculated as shown below:

[0106]

[0107] wherein, is the net power of the micro-grid at time t, is the grid selling price at time t, is the total charging power of the electric vehicle at time t, is the micro-grid load except the total charging power of the electric vehicle.

[0108] Further, the conditional risk cost caused by the net load uncertainty is calculated as follows:

[0109] The net load uncertainty caused by the photovoltaic, the wind turbine, and the load is mainly calculated, wherein the conditional risk value CVaR of the uncertainty is t The conditional risk cost calculation is calculated by formula (5) as described in detail in S3:

[0110]

[0111] wherein, is the net power of the micro-grid at time t, is the grid selling price at time t, CVaR t is the average net load error of the micro-grid beyond the allowable interval at time t, H(Er t ) is the penalty function beyond the power allowable interval, Er t is the net load error at time t, VaR t is the boundary of the power allowable interval at time t, ΔEr is the jump, and D is the basic penalty multiple, [x] + is the upward rounding function.

[0112] It should be noted that the FCVaR includes the penalty part, and therefore the objective function of the day-ahead stage scheduling model includes the penalty function.

[0113] The further arrangement of the embodiment is that the day-ahead stage scheduling model is provided with constraint conditions, and the constraint conditions of the day-ahead stage scheduling model include micro-grid power balance constraint, micro-grid peak-valley difference constraint, micro-grid interactive power constraint and EV charging constraint.

[0114] The specific constraint conditions are as follows:

[0115] The micro-grid power balance constraint is:

[0116]

[0117] Wherein, is the power of other loads of the MG except EVs at t time, is the charging load of the conventional EV user at t time, is the net load, which is also the power purchase of the MG.

[0118] The micro-grid peak-valley difference constraint is:

[0119]

[0120] Wherein, ΔP MG The optimization is solved before the peak-valley difference of the MG load curve, The optimization is solved after the peak-valley difference of the MG load curve.

[0121] The micro-grid interactive power constraint is:

[0122]

[0123] Wherein, respectively represent the upper and lower limits of the power purchase from the grid.

[0124] The EV charging constraint is:

[0125] In the day-ahead scheduling, only the charging flexibility of the EV is considered, and the discharging compensation of the EV is not considered. At present, the common process of scientific charging of a lithium ion battery can be divided into three stages: pre-stabilization, constant current charging and constant voltage charging. Therefore, in order to protect the charging life of the EV battery, the charging safety and improve the charging efficiency, the charging process of the EV battery and the charging frequency constraint of the EV battery are as follows:

[0126]

[0127] Wherein, is the charging frequency, and reducing the charging frequency can increase the charging persistence and protect the charging life of the battery; is the maximum charging power of the battery, which changes with the SOC, so as to match the three-stage charging. When 20%≤SOC x ≤80%, it is in the constant current charging state, and a large current can be used for charging, so that the charging power is When SOC x <20% or SOC x >80%, in pre-regulation and constant voltage charging state, the charging current is small for protecting battery life, so the charging power is In actual charging process, It varies with current.

[0128] II) Real-time stage scheduling model takes the minimum real-time correction cost of industrial micro-grid and the maximum benefit of cooperation alliance as the optimization target, and its objective function is:

[0129]

[0130] Wherein, is the incentive compensation cost of micro-grid to electric vehicle alliance at t moment, that is, the total income of alliance, is the additional penalty cost of micro-grid to power grid at t moment, is the risk cost of charging flexibility and power of electric vehicle i at t moment.

[0131] Specifically, the penalty cost is calculated by the part exceeding the day-ahead power purchase plan in the specific penalty mode of power grid, and the specific penalty cost calculation mode in the embodiment of the application is as formula (11):

[0132]

[0133] Further, the constraint conditions set by the real-time stage scheduling model include real-time power balance constraint, power grid constraint and EV constraint.

[0134] Real-time power balance constraint:

[0135]

[0136] Wherein, respectively, the actual total load of the system at t moment, the actual net load, that is, the actual power purchase, the actual photovoltaic output power, the actual wind power output power respectively, the actual discharge power and the actual charging power of CSEVA at t moment.

[0137] Power grid constraint:

[0138]

[0139] Wherein, when , it means that the MG exceeds (or is lower than) the power purchase amount required by the power purchase plan, and will be punished for exceeding or falling below the upper and lower limits of power purchase.

[0140] EV constraint:

[0141] In real-time scheduling process to ensure, EV individual not to leave the big alliance, thus need to ensure the travel demand of EV individual and profit, thus the specific constraints are as follows:

[0142]

[0143] Wherein, The actual expected SOC when EV leaves, The EV in the big alliance distribution profit.

[0144] S2, initialize the two-stage scheduling model established in S1. The parameters of the two-stage scheduling model include the day-ahead predicted industrial micro-grid basic load Photovoltaic output power Fan output power Time-of-use electricity price, charging price, time period number T, electric vehicle number N, electric vehicle battery rated capacity E, electric vehicle travel characteristic quantity, electric vehicle charging and discharging power Charging and discharging efficiency Nominal condition electric vehicle battery cycle life CL nom , electric vehicle battery discharge depth DoD nom , electric vehicle battery purchase and recycling price

[0145] Specifically, initialize the parameters of the day-ahead stage scheduling model, including the industrial micro-grid basic load Photovoltaic output power Fan output power Time-of-use electricity price, charging price, time period number T, electric vehicle number N, electric vehicle battery rated capacity E, electric vehicle travel characteristic quantity, electric vehicle charging power Charging and discharging efficiency

[0146] Initialize the parameters of the real-time stage scheduling model, including the time-of-use electricity price, the charging price, the time period number T, the electric vehicle number N, the electric vehicle battery rated capacity E, the electric vehicle travel characteristic quantity, and the electric vehicle charging and discharging power Charging and discharging efficiency Nominal condition electric vehicle battery cycle life CL nom , electric vehicle battery discharge depth DoD nom , electric vehicle battery purchase and recycling price

[0147] S3, in the two-stage scheduling model of the industrial micro-grid, the day-ahead stage scheduling model adopts a day-ahead energy scheduling model based on conditional value at risk (CVaR) considering the scheduling potential of electric vehicles, which is a mixed integer linear model and is solved by Gurobi solver; the real-time stage scheduling model adopts a real-time correction model based on dynamic game considering the travel risk of electric vehicles, the real-time stage scheduling model solves the real-time adjustment cost and the charging and discharging plan of electric vehicles by using a bi-level optimization model, solves the objective function of the two-stage scheduling model, and obtains the optimal scheduling plan of the industrial micro-grid and the optimal charging and discharging plan of electric vehicles. Specifically, the following sub-steps are included:

[0148] S3-1, solve the day-ahead stage scheduling model.

[0149] S3-1-1, initialize the day-ahead predicted industrial micro-grid basic load photovoltaic output power fan output power time-of-use electricity price, charging price, number of time periods T, number of electric vehicles N, rated capacity of electric vehicle battery E, electric vehicle travel characteristic quantity, electric vehicle charging power charging and discharging efficiency

[0150] S3-1-2, calculate the day-ahead predicted industrial micro-grid net load photovoltaic output power fan output power calculate the day-ahead predicted industrial micro-grid net load calculate the historical net load error Er of the industrial micro-grid from the historical data of the industrial micro-grid t , the calculation formula is as follows:

[0151]

[0152] wherein, is the actual net load power.

[0153] S3-1-3, calculate the conditional risk cost F caused by the net load uncertainty from the historical net load error data of the industrial micro-grid and the day-ahead predicted industrial micro-grid related data by using the CVaR method CVaR and the net load power tolerance interval, i.e. VaR. The specific method is as follows:

[0154] S3-1-3-1, a loss function is established wherein are the decision variables and random variables, i.e. actual net load power and predicted net load power, the probability density of the predicted net load power is designed according to the historical data of the predicted net load power t ), and the decision variable with threshold δ determination, loss function cumulative distribution function and loss function as follows:

[0155]

[0156] S3-1-3-2, therefore, at a certain confidence level α ∈ (0, 1), for the determined decision variable The expression of VaR function is as follows:

[0157]

[0158] Wherein, the confidence level α is determined by the decision maker according to the system internal resources and resource schedulable ability, R is a real set.

[0159] S3-1-3-3, through VaR can be obtained CVaR expression as follows:

[0160]

[0161] S3-1-3-4, but, in the process of solving practical problems, it is too complex to accurately solve the above expression, therefore we construct auxiliary function, simplify the expression, after simplification as follows:

[0162]

[0163] S3-1-3-5, by sampling point instead of integral, can be further simplified to the above formula (20):

[0164]

[0165] Wherein, N is the total number of samples, threshold δ is an auxiliary variable, and the optimal value is VaR.

[0166] S3-1-3-6, thus, the conditional value at risk cost F CVaR as follows:

[0167]

[0168] S3-1-4, by the number of electric vehicles N, electric vehicle battery rated capacity E, electric vehicle trip characteristic quantity, electric vehicle charging power charge and discharge efficiency According to the electric vehicle trip distribution, the adjustable potential of electric vehicle is calculated by using Minkowski additivity. Specifically, the following sub steps are as follows:

[0169] S3-1-4-1, obtaining and initializing electric vehicle travel characteristic quantities from charging piles of charging stations in the micro-grid, including: initial SOC, arrival time, departure time reported by electric vehicle users, expected SOC, through the above information, a load model corresponding to the electric vehicle individual can be established, and the expression is as follows:

[0170]

[0171] Wherein, respectively, the charging and discharging rates of the electric vehicle at t time; respectively, the upper limit of the charging and discharging power of the electric vehicle; respectively, the Boolean variables of the charging and discharging state of the electric vehicle, that is, the charging and discharging of a single electric vehicle cannot be carried out at the same time, is a Boolean variable of the arrival state of the electric vehicle, when the electric vehicle can carry out charging and discharging; is the state of charge of the electric vehicle at t time, SOC x，min , SOC x，max respectively, the upper and lower limits of the state of charge of the electric vehicle; respectively, the charging and discharging efficiency of the electric vehicle; Δt is the time period of charging and discharging of the electric vehicle; t ar , t le respectively, the arrival time and departure time of the electric vehicle, and x is the type of the electric vehicle.

[0172] S3-1-4-2, since there is a difference between the arrival and departure times of the electric vehicle individuals, the Boolean variable is used to unify the access time of all electric vehicles to the industrial micro-grid into the same definition domain. Therefore, the electric vehicle individuals have the Minkowski additivity, and then the Minkowski additivity is used to calculate the power and energy of the electric vehicle aggregation of the charging station, and the expression is as follows:

[0173]

[0174] Wherein, SOC n，ar , SOC n，le respectively, the state of charge of the battery at the arrival time and the departure time of the electric vehicle; X EV is a set of electric vehicle types.

[0175] S3-1-4-3, as described above, for the variables, parameters and schedulable potential Ω EV of the electric vehicle aggregation of the charging station, the expression is as follows:

[0176]

[0177] wherein, are the charging and discharging power of CS electric vehicle A at time t, respectively, and are the upper limits of charging and discharging of CS electric vehicle A at time t, respectively; are the upper and lower limits of the state of charge of the charging station electric vehicle aggregation at time t, respectively; t is the change in the state of charge of the charging station electric vehicle aggregation at time t due to the driving behavior of the electric vehicle user.

[0178] S3-1-5, the electric vehicle dispatchable potential, the conditional risk cost F CVaR , the predicted net load of the industrial microgrid, to build a mixed integer linear model with the minimum industrial microgrid daily power consumption cost as the target, and use the Gurobi solver to solve the mixed integer linear model to obtain the optimal power purchase power and the optimal charging power to form the optimal dispatching plan in the day-ahead stage, and the optimal power purchase plan is transmitted to the real-time stage.

[0179] S3-2, solve the real-time stage dispatching model.

[0180] S3-2-1, in the real-time stage, at each unit time node, by the charging price, the number of time periods T, the electric vehicle travel characteristic quantity, including the expected battery state of charge of electric vehicle x the current battery state of charge SOC x，t , the stay time T stop , the required charging time T ch , the travel risk of the electric vehicle at the current time t is calculated using the generalized clock membership function, and the risk cost of the sold charging flexibility and the state of charge is calculated using the battery capacity attenuation function and the electric vehicle incentive function. Specifically, the following sub-steps are as follows:

[0181] S3-2-1-1, through the electric vehicle charging pile, the expected battery state of charge of electric vehicle x the current battery state of charge SOC x，t , the stay time T stop , the required charging time T ch , the travel risk is modeled by the generalized clock membership function, and the electric vehicle travel risk is calculated, and the expression is as follows:

[0182]

[0183] wherein, SOC x，t are the electric vehicle travel risk and state of charge at time t, respectively, is the expected SOC when the EV leaves, and σ is the current required charging time Tch The ratio of the residence time T stop .

[0184] S3-2-1-2, using electric vehicle battery attenuation model, the sale of electric vehicle power will cause the degree of battery power attenuation, expression as follows:

[0185]

[0186] Where, CL nom , DoD nom is the cycle life and discharge depth of electric vehicle battery under nominal conditions, e t，x is the current discharge power, E x is the maximum power of electric vehicle battery.

[0187] Specifically, the nominal conditions refer to: at room temperature, the battery is charged with 1C current, and after 1 hour, the battery is discharged with 1C current.

[0188] S3-2-1-3, through the electric vehicle travel risk function and the degree of battery power attenuation, using the incentive-based electric vehicle compensation method, the risk cost of electric vehicle sale charging flexibility and power at this moment is calculated, the expression is as follows:

[0189]

[0190] Where, is the power of electric vehicle participating in dispatch at t, is the maximum charging price of industrial microgrid, is the price of EV battery purchased alone, is the electric vehicle battery scrap recycling price.

[0191] S3-2-2, by each time node using cooperative game model, the deviation value of actual net load and day-ahead optimal power purchase plan net load and electric vehicle travel risk cost at the time node, with electric vehicle charging and discharging power as decision variable, with minimum microgrid real-time adjustment cost and maximum electric vehicle alliance benefit as optimization objective, a real-time correction model based on dynamic game considering electric vehicle travel risk is constructed, the real-time adjustment cost and electric vehicle individual profit are calculated by double-layer optimization method, and the real-time electric vehicle charging and discharging power and microgrid real-time correction result are output. Specifically, the following sub-steps are as follows:

[0192] S3-2-2-1, initialize the related parameters of industrial microgrid, electric vehicle and cooperative alliance at t, including industrial microgrid power gap, power grid price, electric vehicle travel risk and battery sale and recycling cost;

[0193] S3-2-2-2, set the initial number of cooperative alliance electric vehicles M, the expression is as follows:

[0194]

[0195] Where, ΔP del is the industrial micro-grid power gap, is the minimum non-zero discharge power of the cooperative alliance electric vehicle.

[0196] S3-2-2-3, according to the number of participating electric vehicles, the total power of electric vehicle response is calculated The lower model in the double-layer optimization model is transmitted;

[0197] S3-2-2-4, the lower model calculates the real-time adjustment cost without the participation of electric vehicles. Then, according to the total power of electric vehicle response and the number of electric vehicles, the EVs that meet the requirements are extracted, and the industrial micro-grid real-time adjustment cost is minimized as the optimization objective, and the Gurobi solver is used to optimize the industrial micro-grid real-time adjustment cost and the total benefit of the cooperative alliance, which is transmitted to the upper model in the double-layer optimization model.

[0198] Specifically, the lower model calculates the real-time adjustment cost without the participation of electric vehicles, and then extracts the EVs that meet the requirements according to the total power of electric vehicle response and the number of electric vehicles, and minimizes the industrial micro-grid real-time adjustment cost as the optimization objective.

[0199] S3-2-2-5, the upper model calculates the contribution degree of electric vehicle individual in the alliance according to the non-linear energy mapping function, so as to calculate the benefit of electric vehicle individual in the alliance, the expression is as follows:

[0200]

[0201]

[0202] Wherein, are the output power of member i and the maximum output power of members in the alliance, respectively; is the risk coefficient of member i; is the non-linear energy contribution value of member i, F(x) is the non-linear mapping function, and the exponential function of natural logarithm e is adopted; γ i is the overall contribution degree of member i.

[0203] Specifically, the upper model takes the maximum benefit of the cooperative alliance as the optimization objective, and judges whether all participating electric vehicle individuals in the cooperative alliance are profitable according to the risk cost and the size of the benefit of electric vehicle selling charging flexibility and electric quantity. If all are profitable and the benefit of the cooperative alliance is not increasing, the real-time adjustment cost and the profit of electric vehicle individual are output, otherwise the subsequent steps are executed;

[0204] S3-2-2-6, judging whether all participating cooperative alliance electric vehicle individuals are profitable according to the risk cost and the size of the benefit obtained by selling charging flexibility and electric quantity of the electric vehicle, if all are profitable, output real-time adjustment cost and electric vehicle individual profit situation, otherwise execute S3-2-2-7;

[0205] S3-2-2-7, judging whether the number of electric vehicles reaches the upper and lower limits, if so, stop iteration, output real-time adjustment cost and electric vehicle individual profit situation, otherwise adjust the number of electric vehicles, execute S3-2-2-3.

[0206] In the optimization process, the load curve comparison results before and after the day-ahead industrial micro-grid dispatching are shown in Figure 2 , the day-ahead electric vehicle dispatching potential and the electric vehicle dispatching results are shown in Figure 3 , Figure 4 The above pictures can reflect that the application achieves the purpose of relieving the phenomenon of "peak on peak" of the power distribution network, reducing the load of the power distribution network, at the same time, the day-ahead load curve becomes smoother, reduces the volatility of the park load curve and fully utilizes the dispatchability of the electric vehicle. At the same time, in the real-time correction stage, the effect of the real-time stage correction power and the verification effect between the electric vehicle travel risk and the characteristic quantity are investigated, based on the day-ahead stage dispatching result, the industrial micro-grid load curve comparison in the real-time correction stage is obtained, the results comparison before and after the real-time stage power purchase power adjustment and the adjustment cost comparison are shown in Figure 5 , Figure 6 , the verification effect between the electric vehicle travel risk and the electric vehicle characteristic quantity, the electric vehicle dispatching result and the electric vehicle charging cost comparison are shown in Figure 7 , Figure 8 and Table 2. As shown in the above charts, the two-stage optimization model of the industrial micro-grid described in the application not only reduces the industrial power grid electricity cost, but also reduces the charging cost of the EV user participating in the regulation under the condition of meeting the user travel demand, and stabilizes the load fluctuation of the micro-grid.

[0207] Table 1 comparison of system operation cost and adjustment cost

[0208]

[0209] Table 2 comparison of EV participation in regulation and charging cost within a day

[0210]

[0211] The embodiments of the present application are described in detail above with reference to the accompanying drawings, but the present application is not limited to the described embodiments. Various changes, modifications, replacements, and variations of the embodiments including components can be made by those skilled in the art without departing from the principles and spirit of the present application, and still fall within the scope of the present application.

Claims

1. A micro-grid scheduling method based on electric vehicle scheduling potential and travel risk, characterized in that, The method comprises the following steps: S1, establishing a two-stage scheduling model of the industrial micro-grid, the two-stage scheduling model comprising a day-ahead stage scheduling model and a real-time stage scheduling model, The day-ahead stage scheduling model takes the minimum industrial micro-grid operation cost as an optimization target, and a target function thereof is: minF ahced = F1 + F CVaR wherein F1is the microgrid operation cost, F CVaR is the conditional risk cost due to net load uncertainty; The real-time stage scheduling model takes the minimum industrial micro-grid real-time correction cost and the maximum cooperation alliance benefit as optimization targets, and a target function thereof is: wherein, is the incentive compensation cost that the microgrid needs to give to the electric vehicle alliance at time t, i.e., the total income of the alliance, is the additional penalty cost that the microgrid needs to pay to the grid at time t, is the risk cost of the charging flexibility and power of the electric vehicle i at time t; S2, initializing the two-stage scheduling model established in S1; S3, the day-ahead stage scheduling model is a mixed integer linear model, and a Gurobi solver is used to solve the day-ahead stage scheduling model to obtain an optimal scheduling plan of the industrial micro-grid; The real-time stage scheduling model adopts a double-layer optimization model to solve the real-time adjustment cost and the charging and discharging plan of the electric vehicle, the optimal scheduling plan of the industrial micro-grid is substituted into the real-time stage scheduling model, the target function of the real-time stage scheduling model is solved, and an optimal charging and discharging plan of the electric vehicle is obtained. 2.The microgrid dispatching method based on the electric vehicle dispatching potential and trip risk according to claim 1, wherein, The target function of the day-ahead stage scheduling model is as follows: F ahead As an optimization objective, its objective function is: where F1 is the microgrid operation cost, F2 is the conditional risk cost caused by the net load uncertainty, CVaR are the grid purchase cost, the PV operation cost, and the wind turbine operation cost at time t, respectively; is the net power of the microgrid at time t, is the grid sale price at time t, CVaR t is the average net load error of the microgrid beyond the tolerance interval at time t, H(Er t ) is the penalty function beyond the power tolerance interval, Er t is the net load error at time t, VaR t is the boundary of the power tolerance interval at time t, ΔEr is the step, and D is the basic penalty multiplier, [x] + is the upward rounding function.​ 3.The microgrid dispatching method based on the electric vehicle dispatching potential and trip risk according to claim 2, wherein, The target function of the real-time stage scheduling model is as follows: The real-time stage takes each time node as a unit, and each time node takes the minimum micro-grid real-time adjustment cost and the maximum electric vehicle cooperation alliance benefit as optimization targets, and a target function thereof is: wherein, is the incentive compensation cost that the micro-grid needs to give to the electric vehicle alliance at time t, i.e., the total income of the alliance, is the additional penalty cost that the micro-grid needs to pay to the grid at time t, is the actual net load error, is the actual net load of the micro-grid, and VaR is the allowable error, is the risk cost of the electric vehicle for one sale of charging flexibility and electricity, i.e., the individual participation in dispatch income; Z is the number of members of the large alliance. 4.The microgrid dispatching method based on the electric vehicle dispatching potential and trip risk according to claim 3, wherein, The day-ahead stage scheduling model is provided with constraint conditions, and the constraint conditions of the day-ahead stage scheduling model comprise a micro-grid power balance constraint, a micro-grid peak-valley difference constraint, a micro-grid interaction power constraint and an EV charging constraint.

5. The microgrid dispatching method based on electric vehicle dispatching potential and trip risk according to claim 4, characterized in that, The real-time stage scheduling model is provided with constraint conditions, and the constraint conditions of the real-time stage scheduling model comprise a real-time power balance constraint, a power grid constraint and an EV constraint. 6.The microgrid dispatching method based on the electric vehicle dispatching potential and trip risk according to claim 5, wherein, The step S2 initializes the parameters of the day-ahead stage scheduling model, including the industrial micro-grid basic load Photovoltaic output power Fan output power Time-of-use electricity price, charging price, number of time periods T, number of electric vehicles N, rated capacity E of electric vehicle battery, electric vehicle travel characteristic quantity, electric vehicle charging power Charging and discharging efficiency 7. The microgrid dispatching method based on electric vehicle dispatching potential and trip risk according to claim 6, characterized in that, The step S2 initializes parameters of the real-time stage scheduling model, including grid time-of-use price, charging price, time period number T, electric vehicle number N, electric vehicle battery rated capacity E, electric vehicle trip characteristic quantity, and electric vehicle charging and discharging power Charging and discharging efficiency Electric vehicle battery cycle life CL under nominal conditions nom Electric vehicle battery discharge depth DoD nom Electric vehicle battery purchase and recycling price 8.The microgrid dispatching method based on the electric vehicle dispatching potential and trip risk according to claim 7, wherein, In the step S3, the solving method of the day-ahead stage scheduling model is: from industrial microgrid base load photovoltaic output power wind turbine output power computing a day-ahead forecast industrial microgrid net load The expression is as follows: Industrial microgrid net load error Er t The expression is as follows: Pactual is the actual net load power; The conditional risk cost F caused by the uncertainty of the net load is calculated by using the CVaR method based on the historical net load error data of the industrial micro-grid and the day-ahead predicted industrial micro-grid data CVaR and the net load power tolerance interval, i.e. VaR; By the number of electric vehicles N, electric vehicle battery rated capacity E, electric vehicle trip characteristics, electric vehicle charging power Charge-discharge efficiency According to the distribution of electric vehicle trips, the adjustable potential of electric vehicles is calculated by using Minkowski's additivity. F CVaR , industrial micro-grid forecast net load, to minimize the industrial micro-grid day-ahead electricity cost as the target to build a mixed integer linear model, and use Gurobi solver to solve the mixed integer linear model to obtain the optimal power purchase and the optimal charging power to form the day-ahead optimal scheduling plan. 9.The microgrid dispatching method based on electric vehicle dispatching potential and trip risk according to claim 8, wherein, The conditional risk cost F caused by the net load uncertainty CVaR And the calculation method of the net load power allowable interval VaR is as follows: A loss function is established wherein are decision variables and random variables, i.e. actual net load power and predicted net load power, a probability density of the predicted net load power is designed according to historical data of the predicted net load power, and the probability density is θ(Er t ), the decision variable of which is determined with a threshold value δ, and the loss function is determined with a cumulative distribution function of the loss function as follows: Therefore, at a certain confidence level α∈(0, 1), for a certain decision variable The expression of the VaR function is as follows: Wherein, the confidence level α is determined by a decision maker according to the internal resources of the system and the adjustable capacity of the resources, and R is a real set; The CVaR expression can be obtained through the VaR as follows: Wherein, N is the total number of samples, and the threshold δ is an auxiliary variable, and the optimal value of the threshold δ is VaR; From this, the conditional value at risk cost F CVaR As follows: 10.The microgrid dispatching method based on the electric vehicle dispatching potential and trip risk according to claim 9, wherein, In the step S3, the method for calculating the adjustable potential of the electric vehicle is: The electric vehicle travel characteristic quantity is obtained and initialized from the charging piles of the charging station in the micro-grid, a load model corresponding to each electric vehicle is established through the electric vehicle travel characteristic quantity, and an expression is as follows: wherein, are the charging and discharging rates of the electric vehicle at time t, respectively; are the upper limits of the charging and discharging power of the electric vehicle, respectively; are the Boolean variables of the charging and discharging state of the electric vehicle, respectively, is the Boolean variable of the arrival state of the electric vehicle, when the electric vehicle can charge and discharge; is the state of charge of the electric vehicle at time t, SOC x,min , SOC x,max are the upper and lower limits of the state of charge of the electric vehicle, respectively; are the charging and discharging efficiencies of the electric vehicle, respectively; Δt is the time period of charging and discharging of the electric vehicle; t ar , t le are the arrival and departure times of the electric vehicle, respectively, and x is the type of the electric vehicle; The arrival and departure time of each individual electric vehicle is different, and the charging and discharging power is calculated by considering the Boolean variable The access time of all electric vehicles to the industrial micro-grid is unified into the same domain by the Boolean variable, and the power and energy of the electric vehicle aggregation at the charging station are calculated by using the Minkowski additivity, so as to calculate the schedulable potential Ω EV , and the expression is as follows: wherein, respectively are the charging and discharging power of the charging station electric vehicle aggregate at time t and the electric quantity of the charging station electric vehicle aggregate at time t; respectively are the upper limits of the charging and discharging of the charging station electric vehicle aggregate at time t; respectively are the upper and lower limits of the electric quantity of the charging station electric vehicle aggregate at time t; ΔSOC t is the change of the electric quantity of the charging station electric vehicle aggregate at time t caused by the driving behavior of the electric vehicle user. 11.The microgrid dispatching method based on electric vehicle dispatching potential and trip risk according to claim 10, characterized in that, In the step S3, the solving method of the real-time stage scheduling model is: Obtaining and initializing the x-th desired battery charge of the electric vehicle through the electric vehicle charging pile The current battery charge SOC at time t x，t The residence time T stop The required charging time T ch Modeling the travel risk through a generalized clock-type membership function to calculate the electric vehicle travel risk; An electric vehicle battery attenuation model is adopted to calculate the degree of battery power attenuation caused by the sale of the electric vehicle; Through the electric vehicle travel risk function and the degree of battery power attenuation, the risk cost of the charging flexibility and the power of the electric vehicle at the moment is calculated by using an electric vehicle compensation method based on incentives; The related parameters of the industrial micro-grid, the electric vehicle and the cooperation alliance at the moment t are initialized, including the industrial micro-grid power gap, the power grid price, the electric vehicle travel risk and the battery sale and recycling cost; The initial number of cooperation alliance electric vehicles M is set, and an expression is as follows: where ΔΡ del is the industrial microgrid power gap, is the minimum non-zero discharging power of the cooperative alliance electric vehicles, is the optimal power purchase power; Calculating total power response of electric vehicles according to number of participating electric vehicles Incoming lower model into a bi-level optimization model The Gurobi solver is used to optimize and solve the industrial micro-grid real-time adjustment cost and the total benefit of the cooperation alliance participating in the adjustment, and is input into the upper model in the double-layer optimization model; The upper model in the double-layer optimization model calculates the contribution degree of the electric vehicle individual in the alliance according to a nonlinear energy mapping function, thereby calculating the benefit obtained by the electric vehicle individual in the alliance, and the expression is as follows: wherein, Pmaxis the maximum output power of the members in the alliance; is the risk coefficient of member i; is the non-linear energy contribution value of member i, F(x) is a non-linear mapping function, using the exponential function of natural logarithm e; γ i is the overall contribution degree of member i; If the number of electric vehicles reaches the upper and lower limits, the iteration is stopped, and the real-time adjustment cost and individual profit of electric vehicles are output, otherwise the number of electric vehicles is adjusted, and the total power response of electric vehicles is recalculated

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