CN110097237A - A kind of main distribution coordinated scheduling optimization method based on electric car and multiple-energy-source - Google Patents
A kind of main distribution coordinated scheduling optimization method based on electric car and multiple-energy-source Download PDFInfo
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Abstract
This application discloses a kind of main distribution coordinated scheduling optimization method based on electric car and multiple-energy-source, first according to the load condition of the electric car of Grid to be optimized, obtain the electric car daily load curve of Grid to be optimized, calculate the schedulable range of electricity, then it constructs with the cost mathematical model of the minimum optimization aim of system generator group operating cost, setting constraint condition constrains cost mathematical model, obtain basic Controlling model, finally basic Controlling model is optimized using improvement explosion fireworks algorithm, export optimal solution, the corresponding generating set power output scheme of optimal solution is optimal power output scheme, improve the operational efficiency and reliability of the main distribution of the existing multiple-energy-source containing electric car.
Description
Technical Field
The application relates to the technical field of power system planning, in particular to a main and distribution network coordinated scheduling optimization method based on electric vehicles and multiple energy sources.
Background
The electric automobile has incomparable advantages compared with the traditional automobile in the aspects of energy conservation and emission reduction, greenhouse effect suppression, energy safety guarantee and the like, so the electric automobile is widely concerned. The network access of the electric automobile can have a profound influence on the planning and operation of the power system and the operation of the power duration.
Under the dual pressure of energy demand and environmental protection, renewable energy is rapidly developed, wherein wind power generation and photovoltaic power generation are the most representative, the scale is enlarged year by year, and the renewable energy power generation mode has the advantages of small investment, small occupied area, energy conservation, environmental protection and the like, is undoubtedly a better scheme for solving the energy crisis, and is also an indispensable supplement for building smart energy cities. At present, flexible loads represented by electric vehicles are more and more, and after massive electric vehicles are connected to the grid, the electric vehicles have good controllability, can participate in coordinated dispatching of a power grid, can absorb power generation output of renewable energy sources such as wind power and photovoltaic, peak clipping and valley filling, reduce operation cost, and improve the safe operation level of the power grid, so that research on a coordinated dispatching optimization strategy of a main distribution network containing multiple energy sources of the electric vehicles and improvement of the operation efficiency and reliability of the main distribution network is a key point and a difficult point in the field.
Disclosure of Invention
The embodiment of the application provides a main and distribution network coordinated scheduling optimization method based on electric automobiles and multiple energy sources, which is used for improving the operation efficiency and reliability of the existing multi-energy-source main and distribution network containing the electric automobiles.
In view of this, the application provides a method for coordinating and scheduling optimization of a main network and a distribution network based on electric vehicles and multiple energy sources, which includes the following steps:
101. acquiring an electric vehicle daily load curve of a power grid area to be optimized, and calculating an electric quantity schedulable range of the power grid area to be optimized according to the electric vehicle daily load curve;
102. constructing a system operation cost mathematical model taking the minimum operation cost of the generator set in the power grid area to be optimized as an optimization target, and constructing a constraint condition of the system operation cost mathematical model according to the electric quantity schedulable range to obtain a basic control model consisting of the system operation cost mathematical model and the constraint condition;
103. and optimizing the basic control model based on an improved explosion firework algorithm, outputting an optimal solution, and obtaining optimal output schemes corresponding to all the generator sets.
Preferably, step 101 further comprises:
100. and constructing the electric automobile centralized controller of the power grid area to be optimized, and acquiring the parameter information of the electric automobile in the control area of the electric automobile centralized controller through the electric automobile centralized controller.
Preferably, the parameter information includes: user demand, battery capacity, and travel data.
Preferably, step 101 specifically includes:
and predicting the daily load curve of the electric vehicle in the electric network area to be optimized according to the parameter information, and calculating the electric quantity schedulable range of the electric network area to be optimized according to the daily load curve of the electric vehicle.
Preferably, step 102 specifically includes:
1021. constructing a system operation cost mathematical model comprising a wind turbine generator set, a photovoltaic generator set and a conventional generator set in the power grid area to be optimized, wherein the system operation cost mathematical model takes the minimum operation cost of the generator set in the whole power grid area to be optimized as an optimization target;
1022. and constructing a constraint condition of the system operation cost mathematical model according to the electric quantity schedulable range to obtain a basic control model consisting of the system operation cost mathematical model and the constraint condition.
Preferably, the mathematical model of the system operation cost is as follows:
Ctotal＝Ci(Pi p)+Cwt，l(Pl)+Cpv，m(Pm)；
wherein, i is 1,2P，l＝1,2,...,Nl，m＝1,2,...,NmCi(Pi p) For the cost of electricity generation of conventional units, Cwt,l(Pl) For the cost of wind power generation, Cpv,m(Pm) For photovoltaic generator set cost, NPNumber of conventional generator sets, NlNumber of wind-power generator sets, NmThe number of photovoltaic generator sets.
Preferably, the conventional unit power generation cost is:
wherein, Pi pIs the generated power of a conventional generator set, ai，bi，ciAll are cost factors of conventional generator sets, di，eiTo account for the cost factor of the valve point effect of the generator,is the lower limit of the active power output of the ith conventional generator set, NPThe number of power generation sources only.
Preferably, the wind turbine generator set generates electricity at a cost of:
Cwt,l(Pl)＝Cd,wt,l(Pl)+Coe,wt,l(Pl)+Cue,wt,l(Pl)；
wherein, Cd,wt,l(Pl) Direct cost to purchase wind power, Coe,wt,l(Pl) Penalty cost due to underestimation of wind power output, Cue,wt,l(Pl) The auxiliary service cost generated by wind power output is overestimated.
Preferably, the photovoltaic generator set has a cost of:
Cpv,m(Pm)＝Cd,pv,m(Pm)+Coe,pv,m(Pm)+Cue,pv,m(Pm)；
wherein, Cd,pv,m(Pm) To purchase direct cost of photovoltaics, Coe,pv,m(Pm) Penalty cost due to underestimation of photovoltaic output, Cue,pv,m(Pm) The ancillary service cost of photovoltaic output is overestimated.
Preferably, the constraints comprise operating constraints and load balancing constraints of the generator set;
the operation constraint conditions of the generator set are as follows:
0≤Pwt,l≤Pwt,rate,l；
0≤Ppv,m≤Ppv,rate,m；
the load balance constraint conditions are as follows:
wherein,respectively the upper limit and the lower limit of the generated power, P, of the ith wind generating setwt,rate,lRated power, P, of the first wind turbinepv,rate,mRated power, P, of the mth wind turbinedFor normal loading, PaggThe charging load of the centralized controller of the electric automobile is realized.
According to the technical scheme, the embodiment of the application has the following advantages:
the application provides a main and distribution network coordinated scheduling optimization method based on electric vehicles and multiple energy sources, which comprises the following steps: 101. acquiring an electric vehicle daily load curve of the power grid area to be optimized, and calculating the electric quantity schedulable range of the power grid area to be optimized according to the electric vehicle daily load curve; 102. constructing a system operation cost mathematical model taking the minimum operation cost of a generator set in a power grid area to be optimized as an optimization target, and constructing a constraint condition of the system operation cost mathematical model according to the electric quantity schedulable range to obtain a basic control model consisting of the system operation cost mathematical model and the constraint condition; 103. and optimizing the basic control model based on the improved explosion firework algorithm, outputting an optimal solution, and obtaining optimal output schemes corresponding to all the generator sets. The method comprises the steps of firstly obtaining an electric automobile daily load curve of a power grid area to be optimized according to the load condition of an electric automobile of the power grid area to be optimized, calculating the electric quantity scheduling range, then constructing a cost mathematical model taking the minimum running cost of a system generator set as an optimization target, setting a constraint condition to constrain the cost mathematical model to obtain a basic control model, finally performing optimization solution on the basic control model by using an improved explosion firework algorithm, outputting an optimal solution, wherein a generator set output scheme corresponding to the optimal solution is an optimal output scheme The advantage that the reliability is high has improved the operating efficiency and the reliability of the main and distribution network of the multipotency source that has the electric automobile now.
Drawings
Fig. 1 is a schematic flowchart of an embodiment of a method for coordinating and scheduling optimization of a main network and a distribution network based on electric vehicles and multiple energy sources according to the present application;
fig. 2 is a schematic flowchart of another embodiment of a method for coordinating and scheduling optimization of a main network and a distribution network based on electric vehicles and multiple energy sources according to the present application;
FIG. 3 is a schematic diagram of a valve point effect of a conventional genset;
fig. 4 is a flowchart of the interaction between the electric vehicle and the power grid provided in the embodiment of the present application.
Detailed Description
In order to make the technical solutions of the present application better understood, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
For convenience of understanding, please refer to fig. 1, an embodiment of a method for coordinated scheduling and optimizing a main network and a distribution network based on electric vehicles and multiple energy sources provided by the present application includes:
step 101, acquiring a daily load curve of the electric vehicle in the electric network area to be optimized, and calculating an electric quantity schedulable range of the electric network area to be optimized according to the daily load curve of the electric vehicle.
It should be noted that, after the power grid area to be optimized is established, firstly, according to the load condition of the electric vehicle in the power grid area to be optimized, a daily load curve of the electric vehicle in the area needs to be obtained, and an electric quantity schedulable range of the area is calculated according to the daily load curve of the electric vehicle, the electric quantity schedulable ranges may be overlapped according to the controllability of each electric vehicle, and the controllability of the electric vehicle may be represented as:
tin＜t＜tout；
wherein，toutAnd tinRespectively representing the off-grid time and the on-grid time, Ssoc,wantExpected amount of electricity, S, representing user of electric vehicleSOCRepresenting the current charge of the electric vehicle, C representing the battery capacity of the electric vehicle, p representing the charging power, Δ t representing the time interval, and η representing the charging efficiency.
And 102, constructing a system operation cost mathematical model taking the minimum operation cost of the generator set in the power grid area to be optimized as an optimization target, and constructing a constraint condition of the system operation cost mathematical model according to the electric quantity schedulable range to obtain a basic control model consisting of the system operation cost mathematical model and the constraint condition.
Further, the mathematical model of the system operation cost is as follows:
Ctotal＝Ci(Pi p)+Cwt,l(Pl)+Cpv,m(Pm)；
wherein, i is 1,2P，l＝1,2,...,Nl，m＝1,2,...,Nm，Ci(Pi p) For the cost of electricity generation of conventional units, Cwt,l(Pl) For the cost of wind power generation, Cpv,m(Pm) For photovoltaic generator set cost, NPNumber of conventional generator sets, NlNumber of wind-power generator sets, NmThe number of photovoltaic generator sets.
It should be noted that the energy system of the power grid region to be optimized is mainly composed of a conventional gas power plant, a wind power plant and a photovoltaic power plant, and therefore, the mathematical model of the system operation cost constructed in the embodiment of the present application is as follows:
Ctotal＝Ci(Pi p)+Cwt,l(Pl)+Cpv,m(Pm)。
in general, the fuel cost of the conventional fuel assembly can be expressed by a quadratic function, but in actual operation, when the steam inlet valve starts to start, the fuel loss will be increased sharply due to the wire drawing effect, so that the fuel cost is increased. In order to more accurately represent the electricity generation cost of the conventional fuel unit, it is actually necessary to superimpose a sine function on the original quadratic function, which can increase the non-smooth and non-convex characteristics, as shown in fig. 3, the dotted line in fig. 3 is a quadratic function, and the fuel cost can be generally represented by the quadratic function, and in order to more accurately represent the electricity generation cost of the unit, the embodiment of the present application superimposes a sine function on the quadratic function, so that the solid line curve in fig. 3 is obtained, and it can be seen that the non-smooth characteristics and the non-convex characteristics of the curve are increased compared with the dotted line curve.
In order to constrain the system operation cost mathematical model, in the embodiment of the application, a constraint condition of the system operation cost mathematical model is established according to the electric quantity schedulable range of the power grid area to be optimized, so as to determine a final basic control model based on the system operation cost mathematical model and the constraint condition.
And 103, optimizing the basic control model based on the improved explosion firework algorithm, outputting an optimal solution, and obtaining optimal output schemes corresponding to all the generator sets.
It should be noted that after the basic control model is established, optimization solution needs to be performed on the model, and there are many optimization solution methods for the model, such as a gray Wolf optimization algorithm (GWO), a Cultural algorithm (culturral Algorithms, CA), and the like, but these Algorithms have problems of too long calculation and search time, premature convergence, and the like, and are not preferred solutions for solving the embodiments of the present application.
The method comprises the steps of firstly obtaining an electric automobile daily load curve of a power grid area to be optimized according to the load condition of an electric automobile of the power grid area to be optimized, calculating an electric quantity scheduling range, then constructing a cost mathematical model taking the minimum operation cost of a system generator set as an optimization target, setting a constraint condition to constrain the cost mathematical model to obtain a basic control model, finally performing optimization solution on the basic control model by using an improved explosion firework algorithm, outputting an optimal solution, wherein a generator set output scheme corresponding to the optimal solution is an optimal output scheme The advantage that the reliability is high has improved the operating efficiency and the reliability of the main and distribution network of the multipotency source that has the electric automobile now.
For easy understanding, please refer to fig. 2, the present application also provides another embodiment of a method for coordinated scheduling and optimizing a main network and a distribution network based on electric vehicles and multiple energy sources, including:
step 201, constructing an electric vehicle centralized controller of a power grid area to be optimized, and acquiring parameter information of an electric vehicle in a control area of the electric vehicle centralized controller through the electric vehicle centralized controller.
Further, the parameter information includes: user demand, battery capacity, and travel data.
It should be noted that in the embodiment of the present application, an electric vehicle centralized controller in each area needs to be constructed first, and information such as user requirements, battery capacity, form data, and the like of an electric vehicle is collected, where the user requirements include a charging frequency, a charging period of the electric vehicle at an initial charging time, and an expected state of charge, and the form data may include a charging efficiency, a charging/discharging power limit, and an initial SOC of each vehicle accessing a power grid.
To explain more specifically, please refer to fig. 4, fig. 4 is a flowchart of interaction between an electric vehicle and a power grid provided in an embodiment of the present application, and 10 electric vehicle centralized controllers are collectively arranged in a power grid area to be optimized in fig. 4, and each centralized controller includes 10 electric vehicles. The electric automobile grid-connected condition is obtained by adopting Monte Carlo sampling simulation, the travel data of a user adopts the running survey data of family vehicles counted by the department of transportation in 2001, the charging efficiency of electric automobiles in the region is 95%, the initial SOC of each electric automobile connected to a power grid is in accordance with the normal distribution of N (0.6, 0.1), the battery capacity is 24 kW.h, the charging power limit is 6kW, the discharging power limit is-6 kW, the charging frequency is 3 times, the charging time period of the electric automobiles at the initial charging time is 09: 00-12: 00, 14: 00-17: 00, 19:00 to the next day 07:00, the charging time period is in accordance with the normal distributions of N (9, 12), N (14, 22), 19, 22), the charging time length is calculated based on the charging state, and the following formula is shown:
wherein, TcIn h for the length of the charge, α for the state of charge after the desired completion of the charge (typically full, α is taken to be 1), SOC for the initial state of charge, E for the battery capacity in kW.h, PcThe unit is kW for charging power, η is charging efficiency, and the battery capacity of the electric vehicle in the embodiment of the present application is 82 kW.h.
Step 202, an electric vehicle daily load curve of the power grid area to be optimized is predicted according to the parameter information, and an electric quantity schedulable range of the power grid area to be optimized is calculated according to the electric vehicle daily load curve.
It should be noted that, in the embodiment of the present application, a daily load curve of the electric vehicle in the power grid area to be optimized may be predicted according to the parameter information, so as to obtain a daily electric quantity (SOC) variation curve of the electric vehicle, and accordingly, an electric quantity schedulable range of the integrated controller of the electric vehicle may be calculated. The method for predicting the daily load curve in the embodiment of the application is not an improvement of the application, and the daily load curve can be predicted according to the conventional daily load prediction method. The electric quantity schedulable range can be overlapped according to the controllability of each electric automobile, and the controllability of the electric automobile can be expressed as:
tin＜t＜tout；
wherein, toutAnd tinRespectively representing the off-grid time and the on-grid time, Ssoc,wantExpected amount of electricity, S, representing user of electric vehicleSOCRepresenting the current charge of the electric vehicle, C representing the battery capacity of the electric vehicle, p representing the charging power, Δ t representing the time interval, and η representing the charging efficiency.
And 203, constructing a system operation cost mathematical model comprising the wind generating set, the photovoltaic generating set and the conventional generating set in the power grid area to be optimized, wherein the system operation cost mathematical model takes the minimum operation cost of the generating set in the whole power grid area to be optimized as an optimization target.
And 204, constructing a constraint condition of the system operation cost mathematical model according to the electric quantity schedulable range, and obtaining a basic control model consisting of the system operation cost mathematical model and the constraint condition.
Further, the mathematical model of the system operation cost is as follows:
Ctotal＝Ci(Pi p)+Cwt,l(Pl)+Cpv,m(Pm)；
wherein, i is 1,2P，l＝1,2,...,Nl，m＝1,2,...,Nm，Ci(Pi p) For the cost of electricity generation of conventional units, Cwt,l(Pl) For the cost of wind power generation, Cpv,m(Pm) For photovoltaic generator set cost, NPNumber of conventional generator sets, NlFor wind-driven generatorsNumber of groups, NmThe number of photovoltaic generator sets.
Further, the conventional unit power generation cost is as follows:
wherein, Pi pIs the generated power of a conventional generator set, ai，bi，ciAll are cost factors of conventional generator sets, di，eiTo account for the cost factor of the valve point effect of the generator,is the lower limit of the active power output of the ith conventional generator set, NPThe number of power generation sources only.
Further, the generating cost of the wind turbine set is as follows:
Cwt,l(Pl)＝Cd,wt,l(Pl)+Coe,wt,l(Pl)+Cue,wt,l(Pl)；
wherein, Cd,wt,l(Pl) Direct cost to purchase wind power, Coe,wt,l(Pl) Penalty cost due to underestimation of wind power output, Cue,wt,l(Pl) The auxiliary service cost generated by wind power output is overestimated.
Further, the cost of the photovoltaic generator set is as follows:
Cpv,m(Pm)＝Cd,pv,m(Pm)+Coe,pv,m(Pm)+Cue,pv,m(Pm)；
wherein, Cd,pv,m(Pm) To purchase direct cost of photovoltaics, Coe,pv,m(Pm) Penalty cost due to underestimation of photovoltaic output, Cue,pv,m(Pm) To overestimate lightThe auxiliary service cost due to the volt-out force.
Further, the constraint condition comprises an operation constraint condition and a load balance constraint condition of the generator set;
the operation constraint conditions of the generator set are as follows:
0≤Pwt,l≤Pwt,rate,l；
0≤Ppv,m≤Ppv,rate,m；
the load balance constraint conditions are as follows:
wherein,respectively the upper limit and the lower limit of the generated power, P, of the ith wind generating setwt,rate,lRated power, P, of the first wind turbinepv,rate,mRated power, P, of the mth wind turbinedFor normal loading, PaggThe charging load of the centralized controller of the electric automobile is realized.
It should be noted that the generator set in the embodiment of the present application includes a wind generator set, a photovoltaic generator set, and a conventional generator set, and therefore, in the embodiment of the present application, a mathematical model of the system operation cost of the wind generator set, the photovoltaic generator set, and the conventional generator set in the power grid area to be optimized needs to be constructed, and the minimum operation cost of the generator set in the whole power grid area to be optimized is taken as an optimization target.
In general, the fuel cost of the conventional fuel assembly can be expressed by a quadratic function, but in actual operation, when the steam inlet valve starts to start, the fuel loss will be increased sharply due to the wire drawing effect, so that the fuel cost is increased. In order to more accurately represent the electricity generation cost of the conventional fuel unit, actually, a sine function needs to be superimposed on the original quadratic function, so that the non-smooth and non-convex characteristics can be added, and therefore, the characteristic equation of the electricity generation cost of the conventional unit in the embodiment of the present application is as follows:
as shown in fig. 3, the dotted line in fig. 3 is a quadratic function, which can be used to represent the fuel cost in general, and in order to more accurately represent the generating cost of the unit, in the embodiment of the present application, a sine function is superimposed on the quadratic function, so that the solid line curve in fig. 3 is obtained, and it can be seen that the non-smooth characteristic and the non-convex characteristic of the curve are increased compared with the dotted line curve.
For the power generation cost of the wind generating set, the following considerations need to be considered:
direct cost of purchasing wind:
Cd,wt,l(Pl)＝dwt,lPwt,l；
wherein d iswt,lFor direct cost factor of wind power, Pwt,lThe planned output power for the ith wind turbine.
The penalty cost caused by underestimating the wind power output is as follows:
wherein, Kue,wt,lFor underestimation of the cost coefficient, P, of wind powerwt,rate,lRated power, p, for the first wind turbinewtIs the output power of the wind generating set,the probability density function of the wind generating set is subjected to a Weir distribution as the probability density function of the wind generating set:
where k is a shape parameter, c is a scale parameter, pwtIs the output power of a single wind turbine set,at rated power, vinFor the cut-in wind speed, v, of the wind turbinerThe rated wind speed of the wind turbine set.
Overestimating the auxiliary service cost generated by wind power output:
wherein, Koe,wt,lThe cost coefficient is overestimated for the wind power.
Thus, the total wind power generation cost is:
Cwt,l(Pl)＝Cd,wt,l(Pl)+Coe,wt,l(Pl)+Cue,wt,l(Pl)。
for the power generation cost of the photovoltaic generator set, the following considerations need to be considered:
direct cost of purchasing photovoltaics:
Cd,pv,m(Pm)＝dpv,mPpv,m；
wherein d ispv,mIs a direct cost coefficient of photoelectricity, Ppv,mThe planned output power for the mth wind turbine.
The penalty cost due to photovoltaic output is underestimated:
wherein, Kue,pv,mFor photoelectric underestimation of the cost coefficient, Ppv,rate,mRated power, p, for the mth wind turbinepvIs the output power of the photovoltaic generator set,obeying a Beta distribution as a probability density function of the photovoltaic generator set:
wherein p ispvIs the output power of the photovoltaic unit,the method is characterized in that the method is a maximum output system of a photovoltaic unit, A is the total area of a photovoltaic unit array, η is the total photoelectric conversion efficiency of the photovoltaic unit, mu is mathematical expectation, and sigma is a standard deviation.
Overestimating the ancillary service cost due to photovoltaic output:
wherein, Koe,pv,mThe cost coefficient is overestimated for the photoelectricity.
Thus, the total photovoltaic power generation cost is:
Cpv,m(Pm)＝Cd,pv,m(Pm)+Coe,pv,m(Pm)+Cue,pv,m(Pm)。
therefore, the mathematical model of the system operation cost in the embodiment of the present application is:
Ctotal＝Ci(Pi p)+Cwt,l(Pl)+Cpv,m(Pm)。
in the embodiment of the application, the basic control model is determined according to the upper and lower limits of the generating power of the generator set, the charging load demand of the electric automobile and related constraint conditions.
The constraint conditions in the embodiment of the application comprise operation constraint conditions and load balance constraint conditions of the generator set, wherein the operation constraint conditions comprise operation constraint of a pure generator set, operation constraint of wind power generation and operation constraint of photovoltaic power generation, and the operation constraint conditions respectively comprise:
0≤Pwt,l≤Pwt,rate,l；
0≤Ppv,m≤Ppv,rate,m；
the load balance constraint conditions are as follows:
constraint conditions can also be set for the electric automobile integrated controller:
andand respectively obtaining an actual dispatching load value and a predicted load value of the electric automobile centralized controller at the moment t.
For the schedulable charging load, the schedulable load quantity of the electric automobile meets the upper and lower limits of the charging capacity provided by the electric automobile centralized controller:
the total charging power of the electric automobile can be scheduled at the moment t.
In the schedulable charge-discharge load, the schedulable discharge capacity of the electric automobile meets the upper and lower limits of the scheduling discharge capacity provided by the electric automobile centralized controller:
the discharge power of the electric automobile can be scheduled at the time t.
And step 205, optimizing the basic control model based on the improved explosion firework algorithm, outputting an optimal solution, and obtaining optimal output schemes corresponding to all the generator sets.
It should be noted that after the basic control model is established, the model needs to be optimized and solved through an algorithm, and controllable variables are initialized according to parameters and constraint conditions of the basic control model, and the controllable variables are the generated output of each generator set, namely, the controllable variables are used as initial fireworks for improving the explosive fireworks. As a specific implementation, the parameters of the basic control model are: the capacities of the wind generating sets are 130MW, 94MW and 94MW respectively; the shape parameter k is 2 and the scale parameter c is 15; the cut-in wind speed is 15m/s, the rated wind speed is 5m/s, and the cut-out wind speed is 45 m/s; for a photovoltaic unit, the total area of a photovoltaic array of a photovoltaic power station is 80000m2, the photoelectric conversion efficiency is 14%, the maximum illumination intensity is 700W/m2, and the shape parametersIs 0.95, the scale parameterIs 0.95. The direct cost coefficient of the wind turbine set is 120$/MWh, the underestimated cost coefficient is 15$/MWh, and the overestimated cost coefficient is 20 $/MWh. The direct cost coefficient of the photovoltaic unit is 200$/MWh, the underestimated cost coefficient is 15$/MWh, and the overestimated cost coefficient is 20 $/MWh.
And then, evaluating the solution fitness by using an improved firework algorithm and a fitness function, wherein the calculation formula of the improved firework algorithm on the explosion radius of each firework is as follows:
wherein, Ca、CrAre all constants, Ca>1，Cr<1；XiRepresenting the set of sparks and fireworks generated by the explosion in the ith iteration; x is the number ofiIs the explosive firework of the ith iteration; f (x)i) Denotes xiThe fitness of (2); minf (X)i) Represents XiThe minimum fitness in the set.
And determining a search direction according to an updating mechanism of the improved explosion firework algorithm, updating the generated output of each unit, optimizing the charging load of the electric automobile integrated controller, and issuing an optimized result to the electric automobile integrated controller as an instruction. In the search mode, the entities approach to the optimal solution direction, generate new variables, and finally converge to the optimal solution.
And finally, judging whether the maximum iteration times or convergence is reached, if the maximum iteration times or convergence is reached, finishing the calculation and outputting the output of each generator set to obtain an optimal output scheme, and if not, returning to the step of evaluating the fitness function by using the improved explosive firework algorithm.
The electric vehicle and multi-energy-source-based main and distribution network coordinated scheduling optimization method provided by the embodiment of the application has the following advantages:
(1) a mathematical model containing a large number of conventional generator sets, wind generating sets and photovoltaic generator sets is constructed, and a foundation is laid for the specification of a large-scale power generating set power generation plan.
(2) The controllability of the electric automobile is considered, the charging load of the integrated controller is used as a control variable to participate in the optimal scheduling problem of the wind generating set and the photovoltaic generating set, the power generation cost is reduced, and scheduling personnel on the system side can perform scheduling in a short time.
(3) Compared with a classical Grey Wolf optimization algorithm (GWOlf Optimizer, GWOO), a Cultural algorithm (Cultural Algorithms, CA) and the like, the method has the characteristics of higher convergence speed, stronger overall convergence and the like, so that the scheduling arrangement of the unit is more reasonable.
The above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions in the embodiments of the present application.
Claims (10)
1. A main and distribution network coordinated dispatching optimization method based on electric vehicles and multiple energy sources is characterized by comprising the following steps:
101. acquiring an electric vehicle daily load curve of a power grid area to be optimized, and calculating an electric quantity schedulable range of the power grid area to be optimized according to the electric vehicle daily load curve;
102. constructing a system operation cost mathematical model taking the minimum operation cost of the generator set in the power grid area to be optimized as an optimization target, and constructing a constraint condition of the system operation cost mathematical model according to the electric quantity schedulable range to obtain a basic control model consisting of the system operation cost mathematical model and the constraint condition; optimizing the basic control model based on an improved explosion firework algorithm, and outputting an optimal solution to obtain optimal output schemes corresponding to all the generator sets;
103. and optimizing the basic control model based on an improved explosion firework algorithm, outputting an optimal solution, and obtaining optimal output schemes corresponding to all the generator sets.
2. The electric vehicle and multi-energy-source-based main and distribution network coordinated scheduling optimization method according to claim 1, wherein step 101 further comprises:
100. and constructing the electric automobile centralized controller of the power grid area to be optimized, and acquiring the parameter information of the electric automobile in the control area of the electric automobile centralized controller through the electric automobile centralized controller.
3. The electric vehicle and multi-energy-source-based main and distribution network coordinated scheduling optimization method according to claim 2, wherein the parameter information comprises: user demand, battery capacity, and travel data.
4. The electric vehicle and multi-energy-source-based main and distribution network coordinated scheduling optimization method according to claim 2 or 3, wherein the step 101 specifically comprises:
and predicting the daily load curve of the electric vehicle in the electric network area to be optimized according to the parameter information, and calculating the electric quantity schedulable range of the electric network area to be optimized according to the daily load curve of the electric vehicle.
5. The electric vehicle and multi-energy-source-based main and distribution network coordinated scheduling optimization method according to claim 1, wherein the step 102 specifically comprises:
1021. constructing a system operation cost mathematical model comprising a wind turbine generator set, a photovoltaic generator set and a conventional generator set in the power grid area to be optimized, wherein the system operation cost mathematical model takes the minimum operation cost of the generator set in the whole power grid area to be optimized as an optimization target;
1022. and constructing a constraint condition of the system operation cost mathematical model according to the electric quantity schedulable range to obtain a basic control model consisting of the system operation cost mathematical model and the constraint condition.
6. The electric vehicle and multi-energy-source-based main and distribution network coordinated scheduling optimization method according to claim 5, wherein the system operation cost mathematical model is as follows:
Ctotal＝Ci(Pi p)+Cwt,l(Pl)+Cpv,m(Pm)；
wherein, i is 1,2P，l＝1,2,...,Nl，m＝1,2,...,Nm，Ci(Pi p) For the cost of electricity generation of conventional units, Cwt,l(Pl) For the cost of wind power generation, Cpv,m(Pm) For photovoltaic generator set cost, NPNumber of conventional generator sets, NlNumber of wind-power generator sets, NmThe number of photovoltaic generator sets.
7. The electric vehicle and multi-energy-source-based main and distribution network coordinated scheduling optimization method according to claim 6, wherein the conventional unit power generation cost is as follows:
wherein, Pi pIs the generated power of a conventional generator set, ai，bi，ciAll are cost factors of conventional generator sets, di，eiTo account for the cost factor of the valve point effect of the generator,is the lower limit of the active power output of the ith conventional generator set, NPThe number of power generation sources only.
8. The electric vehicle and multi-energy-source-based main and distribution network coordinated scheduling optimization method according to claim 6, wherein the wind turbine generator set power generation cost is as follows:
Cwt,l(Pl)＝Cd,wt,l(Pl)+Coe,wt,l(Pl)+Cue,wt,l(Pl)；
wherein, Cd,wt,l(Pl) Direct cost to purchase wind power, Coe,wt,l(Pl) Penalty cost due to underestimation of wind power output, Cue,wt,l(Pl) The auxiliary service cost generated by wind power output is overestimated.
9. The electric vehicle and multi-energy-source-based main and distribution network coordinated scheduling optimization method according to claim 6, wherein the photovoltaic generator set cost is as follows:
Cpv,m(Pm)＝Cd,pv,m(Pm)+Coe,pv,m(Pm)+Cue,pv,m(Pm)；
wherein, Cd,pv,m(Pm) To purchase direct cost of photovoltaics, Coe,pv,m(Pm) Penalty cost due to underestimation of photovoltaic output, Cue,pv,m(Pm) The ancillary service cost of photovoltaic output is overestimated.
10. The electric vehicle and multi-energy-source-based main and distribution network coordinated scheduling optimization method according to claim 6, wherein the constraint conditions comprise operation constraint conditions and load balance constraint conditions of a generator set;
the operation constraint conditions of the generator set are as follows:
0≤Pwt,l≤Pwt,rate,l；
0≤Ppv,m≤Ppv,rate,m；
the load balance constraint conditions are as follows:
wherein,respectively the upper limit and the lower limit of the generated power, P, of the ith wind generating setwt,rate,lRated power, P, of the first wind turbinepv,rate,mRated power, P, of the mth wind turbinedFor normal loading, PaggThe charging load of the centralized controller of the electric automobile is realized.
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Application publication date: 20190806 |