Integrated multi-target dynamic economic dispatching method for electric vehicles

Abstract

The invention provides an integrated multi-target dynamic economic dispatching method for electric vehicles. Firstly, a multi-target dynamic economic dispatching mathematical model integrated with the electric vehicle is established; secondly, boundary constraint conditions of a multi-target dynamic economic dispatching problem integrated by the electric vehicle are determined; secondly, on the basis of the built mathematical model, a moth flame algorithm is introduced and improved; and finally, scheduling the electric vehicle integration based on the improved chaos moth flame algorithm, performing comparative analysis on other conventional algorithms through software simulation, and verifying the effectiveness and superiority of the chaos moth flame algorithm provided by the invention. Results show that compared with a conventional electric vehicle integration algorithm, the method has better convergence characteristics, can be optimized to obtain smaller optimal fuel cost and worst fuel cost, can effectively reduce the running time of a CPU, and can improve the efficiency of multi-target dynamic economic dispatching of electric vehicle integration. The method has a good level in the aspects of convergence speed and reliability.

Description

Technical Field

[0001] The invention belongs to the field of electric vehicle optimization scheduling, and relates to a multi-objective dynamic economic scheduling method for electric vehicle integration. Background Art

[0002] With the rapid popularization of electric vehicles (EVs), the power system is facing new challenges and opportunities. As a clean energy means of transportation, electric vehicles not only help reduce greenhouse gas emissions, but also participate in the electricity market to a certain extent, providing support for the power grid through intelligent charging and discharging. Therefore, how to effectively integrate electric vehicles into the power system and achieve multi-objective dynamic economic dispatch has become an important research topic in the field of power engineering. Dynamic Economic Dispatch (DED) aims to optimize the output of generator sets to meet the system load demand while minimizing the cost and emissions of power generation. However, with the diversification of charging needs and charging modes of electric vehicles, traditional economic dispatch methods often find it difficult to meet the needs of real-time dispatch, especially when considering multiple objectives (such as cost, emissions, renewable energy utilization, etc.). Summary of the invention

[0003] 1. Technical problems to be solved:

[0004] Multi-objective dynamic economic dispatch problem with electric vehicle integration, reducing the fuel cost of thermal power plants and grid fluctuations.

[0005] 2. Technical solution:

[0006] In order to solve the above problems, the present invention provides a multi-objective dynamic economic dispatch method for electric vehicle integration, comprising the following steps:

[0007] Step S1: Establish a mathematical model for integrated multi-objective dynamic economic dispatch of electric vehicles with the goal of minimizing the fuel cost of thermal power plants and grid fluctuations.

[0008] Step S2: Determine the boundary constraints for solving the multi-objective dynamic economic dispatch problem of electric vehicle integration.

[0009] Step S3: Using the chaotic moth flame algorithm; using the chaotic mapping sequence to generate the initial solution, and at the same time, using the sine chaotic mapping to update the parameters, the mathematical model is described as:

[0010]

[0011] Step S4: Dynamic economic scheduling of multi-objective electric vehicle integration based on the chaotic moth flame algorithm.

[0012] 3. Beneficial effects:

[0013] The present invention introduces chaotic mapping into the standard moth flame algorithm, so that the initial solution is distributed as evenly as possible in the solution space, and solves the objective function by simulating the behavior of moths moving around flames along spiral trajectories in nature. It effectively reduces the CPU running time, and has a good level in convergence speed and reliability in the multi-objective dynamic economic scheduling of electric vehicle integration. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0015] The present invention is described in detail below in conjunction with the accompanying drawings;

[0016] A multi-objective dynamic economic dispatch method for electric vehicle integration includes the following steps:

[0017] Step S1: Establish a mathematical model for integrated multi-objective dynamic economic dispatch of electric vehicles with the goal of minimizing the fuel cost of thermal power plants and grid fluctuations.

[0018] Step S2: Determine the boundary constraints for solving the multi-objective dynamic economic dispatch problem of electric vehicle integration.

[0019] Step S3: Using the Chaos Moth Flame Algorithm; In order to make the Chaos Moth Flame Algorithm better converge to the optimal solution, the chaotic mapping sequence is used to generate the initial solution, thereby saving the CPU running time. At the same time, the sine chaotic mapping is used to update the parameters. The mathematical model of the logarithmic spiral search of the Chaos Moth Flame Algorithm is described as:

[0020]

[0021] Step S4: Dynamic economic scheduling of multi-objective electric vehicle integration based on the chaotic moth flame algorithm.

[0022] In one embodiment, a mathematical model for distribution network reconstruction is established with the goal of minimizing the fuel cost of thermal power plants and grid fluctuations; the multi-objective dynamic economic dispatch of electric vehicles is used to minimize the fuel cost of thermal power plants and grid fluctuations under all operating constraints; considering that the sudden opening of the steam valve of the steam turbine will have a certain impact on the energy consumption of the unit, the so-called valve point loading effect, the fuel cost calculation formula of the thermal power plant is:

[0023]

[0024] Where: f1(P) is the total fuel cost of the thermal power generating unit; T is the number of periods in the planning cycle; N is the total number of generating units; a i , bi , c i , e i and f i is the cost coefficient of the ith unit; P t,i is the output power of the ith unit at time t; P i min is the minimum power output of the ith unit.

[0025] In addition, large fluctuations in power generation may have a destructive effect on the power grid. Therefore, V2G is introduced to fill valleys and clip peaks, which is formulated as:

[0026]

[0027] Where: f2(P,P PEV ) is to minimize grid fluctuations, P PEV,t is the exchange power between the electric vehicle and the grid at time t; P L,t is the transmission loss at time t.

[0028] The objective functions f1 and f2 are combined into f using the weighting factor ω, which can be expressed as:

[0029] f=f1+ωf2

[0030] In one embodiment, step S2: determining boundary constraints for solving the electric vehicle integration problem; the constraints include generator set power capacity constraints, ramp rate limits, battery charge state constraints, remaining battery power constraints, electric vehicle charge and discharge power constraints, and power balance constraints, and the mathematical expression thereof is:

[0031] The power output of a generator set is determined by the physical characteristics of the set and should be within the capabilities of each specific generator set:

[0032] P i min ≤P t,i ≤P i max

[0033] Where: P i min and P i max are the lower and upper power limits of the i-th generator.

[0034] Due to the inertia of the thermal power unit, the power output cannot change significantly between two adjacent intervals and is limited by the ramp rate, which helps to extend the service life of the unit and is given as:

[0035]

[0036] In the formula: UR i ,DR i are the rising rate limit and falling rate limit of the ith unit.

[0037] The charging constraint state of the electric vehicle battery is described as:

[0038] SOC min ≤SOC t ≤SOC max

[0039] In the formula, SOC min is the lower limit of battery capacity, SOC max is the upper limit of battery capacity, SOC t is the battery capacity at time t.

[0040] The remaining battery power constraint is:

[0041]

[0042] In the formula, SOC t is the remaining capacity of the battery at time t, η PEV , η PEVO is the charging efficiency and discharging efficiency of electric vehicles, SOC Used,t is the amount of electricity used by the electric car at time t.

[0043] Electric vehicle charging and discharging power constraints:

[0044]

[0045] In the formula, is the maximum discharge power of the electric vehicle, is the maximum charging power of the electric vehicle, P PEV,total is the total power of the electric vehicle.

[0046] Power balance constraints:

[0047] In order to maintain power balance, the output power of all units must be equal to the sum of the transmission losses of each demand and each time interval t, which is defined as:

[0048]

[0049] Where P D,t is the load demand at time t, P L,t is the transmission loss at time t, and its mathematical model is expressed as:

[0050]

[0051] In the formula, B ij , B 0i and B00 is the network loss coefficient.

[0052] In one embodiment, the transmission loss of the power grid is a function of the output power of the unit. The relaxation generator is usually used to decompose P L,t and the decision variable P t,N . The formula can be written as:

[0053]

[0054] In one embodiment, the step S4; Figure 1 As shown, it mainly includes the following steps:

[0055] After obtaining the multi-objective dynamic economic dispatch data for solving the electric vehicle integration, clarifying the objective function and constraints in the dispatch problem and using the chaos mapping to initialize the population generation strategy so that the initial solution is distributed as evenly as possible in the solution space,

[0056] Step S41: Initialize moth population parameters:

[0057]

[0058] n is the number of moths and d is the size of the dimension.

[0059] Each moth has a corresponding flame, and the moth flies along the corresponding flame to update its position. The moth corresponds to the F dimension. The position of the flame is represented as:

[0060]

[0061] In one embodiment, the upper bound ub and the lower bound lb in the search space are as follows:

[0062] ub=[ub1,ub2,ub3,……,ub n-1 ,ub n ]

[0063] lb=[lb1,lb2,lb3,…,lb n-1 ,lb n ].

[0064] Step S42: Calculate the fitness of the moth

[0065]

[0066] Where M is the moth population; O M is the adaptability matrix of the moth population; n is the total number of moths; d is the dimension of the variable.

[0067] Step S43: Calculate the flame fitness

[0068]

[0069] In the formula, O F is the flame fitness matrix.

[0070] Step S44: Update the position of the moth

[0071] The position update mechanism of MFO is crucial to unlock its full local search capabilities. Therefore, the best position produced by the moth in the previous iteration should be updated in the current iteration, that is, if the fitness value is better than the flame, the position of the flame is updated. In each iteration, the flame position is saved in the matrix F, and then the moth updates its position according to the matrix F. The update mechanism M i It is expressed as:

[0072] :M i =S(M i ,F j ).

[0073] D i is the distance from the first moth to the first flame, and the formula is:

[0074] D i =|F j -M i |.

[0075] In one embodiment, the moth flies following a logarithmic spiral function S, which is constructed as:

[0076] S(M i ,F i )=D i e bt cos(2πt)+F j

[0077] Where b is the parameter of the logarithmic spiral shape and t∈[-1,1] is the distance parameter. The value t is proportional to the distance of the moth relative to the flame. If t=1, the moth is far away from the flame, and if t=-1, the moth is very close to the flame, so the moth is the entire global space, including the flame itself and the space occupied by the flame.

[0078] Step S45: Update the position of the flame

[0079]

[0080] Where k is the current iteration number, N is the maximum number of flames, and T is the maximum iteration number.

[0081] In the process of moths searching for flames, certain random situations will occur. It takes multiple iterations to find the optimal solution, which makes the algorithm time-consuming. In order to speed up the convergence of the algorithm, in one embodiment, the flame number adaptive update formula is improved, and the original linear decrease of the number of flames is changed to a curve decrease. The improved flame adaptive update formula is as follows:

[0082]

[0083] The number of flames will decrease in a curve as the number of iterations increases, thereby improving the convergence speed of the number of flames and ensuring that the overall convergence speed of the algorithm is accelerated.

[0084] The present invention proposes a multi-objective dynamic economic dispatch method for electric vehicle integration, introduces chaotic mapping into the standard moth flame algorithm, makes the initial solution as evenly distributed as possible in the solution space, and solves the objective function by simulating the behavior of moths moving around flames along spiral trajectories in nature. The algorithm has the advantages of simple structural parameters, low computational cost, and fast computational speed. It has good global optimization capability and high optimization accuracy when solving complex problems with constraints and unknown search spaces. It is used to solve multi-objective dynamic economic dispatch problems for electric vehicle integration, and can optimize to obtain smaller optimal fuel costs and worst fuel costs, which can effectively reduce the CPU running time. In the multi-objective dynamic economic dispatch of electric vehicle integration, this method has a good level in terms of convergence speed and reliability.

Claims

1. A multi-objective dynamic economic dispatch method for electric vehicle integration, comprising the following steps: Step S1: Establishing a mathematical model for integrated multi-objective dynamic economic dispatch of electric vehicles with the goal of minimizing the fuel cost of thermal power plants and grid fluctuations; Step S2: determining boundary constraints for solving the multi-objective dynamic economic dispatch problem of electric vehicle integration; Step S3: Using the chaotic moth flame algorithm; using the chaotic mapping sequence to generate the initial solution, and at the same time, using the sine chaotic mapping to update the parameters, the mathematical model is described as: Step S4: Dynamic economic scheduling of multi-objective electric vehicle integration based on the chaotic moth flame algorithm.

2. The multi-objective dynamic economic dispatch method for electric vehicle integration as claimed in claim 1, characterized in that: In step S1: the fuel cost calculation formula of the thermal power plant is: Where: f1(P) is the total fuel cost of the thermal power generating unit; T is the number of periods in the planning cycle; N is the total number of generating units; a i , b i , c i , e i and f i is the cost coefficient of the ith unit; P t,i is the output power of the ith unit at time t; P i min is the minimum power output of the ith unit, V2G is introduced to fill the valleys and clip the peaks, and its formula is: Where: f2(P,P PEV ) is to minimize grid fluctuations, P PEV,t is the exchange power between the electric vehicle and the grid at time t; P L,t is the transmission loss at time t, The objective functions f1 and f2 are combined into f using a weighting factor ω, expressed as: f=f1+ωf2.

3. The multi-objective dynamic economic dispatch method for electric vehicle integration as claimed in claim 1, characterized in that: In step S2, the constraints include generator set power capacity constraint, ramp rate limit, battery charge state constraint, remaining battery power constraint, electric vehicle charge and discharge power constraint, and power balance constraint. Power of generator set Power capacity constraints of generator set: P i min ≤P t,i ≤P i max Where: P i min and P i max is the lower and upper power limits of the i-th generator; The ramp rate limit is given by: In the formula: UR i ,DR i are the rising rate limit and falling rate limit of the ith unit; The charging constraint state of the electric vehicle battery is described as: SOC min ≤SOC t ≤SOC max In the formula, SOC min is the lower limit of battery capacity, SOC max is the upper limit of battery capacity, SOC t is the battery capacity at time t; The remaining battery power constraint is: In the formula, SOC t is the remaining capacity of the battery at time t, η PEV , η PEVO is the charging efficiency and discharging efficiency of electric vehicles, SOC Used,t is the amount of electricity used by the electric vehicle at time t; Electric vehicle charging and discharging power constraints: In the formula, is the maximum discharge power of the electric vehicle, is the maximum charging power of the electric vehicle, P PEV,total is the total power of the electric vehicle. Power balance constraints: The output power of all units must be equal to the sum of the transmission losses of each demand and each time interval t, which is defined as: Where P D,t is the load demand at time t, P L,t is the transmission loss at time t, and its mathematical model is expressed as: In the formula, B ij , B 0i and B 00 is the network loss coefficient.

4. The multi-objective dynamic economic dispatch method for electric vehicle integration as claimed in claim 3, characterized in that: The transmission loss of the power grid is also included. The transmission loss of the power grid is a function of the output power of the unit. The relaxation generator is used to decompose P L,t and the decision variable P t,N The formula is:

5. The multi-objective dynamic economic dispatch method for electric vehicle integration as claimed in claim 1, characterized in that: The step S4 includes the following steps: Step S41: Initialize moth population parameters: n is the number of moths, and d is the size of the dimension; Each moth has a corresponding flame, and the moth flies along the corresponding flame to update its position. The position of the flame is represented as: Step S42: Calculate the fitness of the moth: Where M is the moth population; O M is the adaptive matrix of the moth population; n is the total number of moths; d is the dimension of the variable; Step S43: Calculate the flame fitness In the formula, O F is the flame fitness matrix; Step S44: Update the position of the moth Update Mechanism M i It is expressed as: M i =S(M i ,F j ) D i is the distance from the first moth to the first flame, and the formula is: D i =|F j -M i | Step S45: Update the position of the flame Where k is the current iteration number, N is the maximum number of flames, and T is the maximum iteration number.

6. The multi-objective dynamic economic dispatch method for electric vehicle integration as claimed in claim 5, characterized in that: The upper bound ub and lower bound lb in the search space are as follows: ub=[ub1,ub2,ub3,……,ub n-1 ,ub n ] <h2 style=";text-align:left;direction:ltr">lb=[lb1,lb2,lb3,……,lb<h2 style=";text-align:left;direction:ltr"> n-1 <h2 style=";text-align:left;direction:ltr"> ,lb<h2 style=";text-align:left;direction:ltr"> n <h2 style=";text-align:left;direction:ltr"> ]。 7. The multi-objective dynamic economic dispatch method for electric vehicle integration as claimed in claim 5, characterized in that: The moth flies following a logarithmic spiral function S, which is constructed as: S(M i ,F i )=D i e bt cos(2πt)+F j Where b is the parameter of the logarithmic spiral shape and t∈[-1,1] is the distance parameter. The value t is proportional to the distance of the moth relative to the flame. If t=1, the moth is far away from the flame, and if t=-1, the moth is in the entire global space, including the flame itself and the space occupied by the flame.

8. The multi-objective dynamic economic dispatch method for electric vehicle integration as claimed in claim 5, characterized in that: In step S45, the flame number adaptive update formula is improved, and the original linear decrease of the flame number is changed into a curve decrease. The improved flame adaptive update formula is as follows: The number of flames decreases in a curve as the number of iterations increases.

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