CN111064227B - Power distribution network operation efficiency optimization method - Google Patents
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- B—PERFORMING OPERATIONS; TRANSPORTING
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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Abstract
The application discloses a power distribution network operation efficiency optimization method, which comprises the following steps: acquiring a cost function of the consumption cost of each subunit of each SECC system at a preset moment, and establishing a target function of each SECC system according to the cost function; and acquiring the constraint conditions of each subunit of each SECC system, and establishing an optimization model of each SECC system according to the constraint conditions and the objective function. According to the method and the device, only by respectively establishing the optimization model of each SECC system, the technical problems that the distribution network in the prior art is used as a power transmission terminal, the satisfaction degree of users is greatly influenced by stability and high efficiency, but the existing research on the operation influence factors of the SECC combined distribution network is too little are solved.
Description
Technical Field
The application relates to the technical field of SECC distribution networks, in particular to a method for optimizing the operation efficiency of a distribution network.
Background
With the shortage of global energy, the proportion of new energy such as photovoltaic and the like in the distribution network energy supply is gradually increased. In the optimized operation of the distribution network, a transformer substation-energy center system formed by matching with an energy center plays an obvious role in improving the consumption of primary new energy in operation efficiency. Meanwhile, due to community distribution of users, research on joint operation of multiple SECCs (substations-energy center systems) has important significance on operation efficiency and stability of a distribution network. The distribution network is used as a terminal of power transmission, the stability and the efficiency of the distribution network have great influence on the satisfaction degree of users, but the existing research on the operation influence factors of the SECC combined distribution network is less.
Disclosure of Invention
The application provides a power distribution network operation efficiency optimization method, and the technical problems that the distribution network in the prior art is used as a power transmission terminal, the satisfaction degree of users is greatly influenced stably and efficiently, and the conventional research on the SECC combined distribution network operation influence factors is too little are solved only by respectively establishing an optimization model of each SECC system.
The application provides a power distribution network operation efficiency optimization method, which comprises the following steps:
acquiring a cost function of the consumption cost of each subunit of each SECC system at a preset moment, and establishing a target function of each SECC system according to the cost function;
and acquiring the constraint conditions of each subunit of each SECC system, and establishing an optimization model of each SECC system according to the constraint conditions and the objective function.
Optionally, the obtaining a cost function of the consumption cost of each subunit of each SECC system at a preset time includes, before establishing an objective function of each SECC system according to the cost function:
acquiring output power models corresponding to a plurality of sub energy supply modules in each SECC system;
and acquiring a building temperature control model of the transformer substation in each SECC system and/or an energy model of the electric automobile in the SECC system.
Optionally, the sub-energy supply module comprises a photovoltaic power plant; the obtaining of the output power models corresponding to the plurality of sub-energy supply modules in each SECC system includes: and establishing a photovoltaic clustering model for the photovoltaic power station, and acquiring an output power model of the photovoltaic power station.
Optionally, the output power model comprises: and acquiring output power models corresponding to different supply modules according to the different supply modules.
Optionally, the building temperature control model comprises: and acquiring the approximate energy storage effect of the building temperature control model, and supplementing the output power model in each SECC system.
Optionally, the cost function comprises: the maintenance cost, the natural gas cost, the main network interaction cost, the power transmission cost and the temperature transmission cost of each sub-distribution network.
Optionally, the constraint condition includes a first preset constraint condition that is satisfied when the electric vehicle participates in the distribution network and includes an electric heating and cooling load.
Optionally, the constraint condition includes a second preset constraint condition that is satisfied by power interaction of the substation.
Optionally, the constraint condition comprises a third preset constraint condition satisfied by the controllable unit gas turbine.
Optionally, the constraint condition includes a fourth constraint condition that the electric vehicle satisfies.
According to the technical scheme, the embodiment of the application has the following advantages:
the application provides a method for optimizing the operation efficiency of a power distribution network, which comprises the following steps: acquiring a cost function of the consumption cost of each subunit of each SECC system at a preset moment, and establishing a target function of each SECC system according to the cost function; and acquiring the constraint conditions of each subunit of each SECC system, and establishing an optimization model of each SECC system according to the constraint conditions and the objective function.
According to the method for optimizing the operation efficiency of the power distribution network, the objective function of each SECC system is established through the cost function of the consumption cost of each subunit of each SECC system, the optimization model of each SECC system is established through the objective function, the optimization model is established based on the constraint condition with the lowest total operation cost, the contact power between the corresponding SECC systems is enabled to operate cooperatively, the operation cost of the system is reduced, and the economic operation efficiency is improved. According to the method and the device, only by respectively establishing the optimization model of each SECC system, the technical problems that the distribution network in the prior art is used as a power transmission terminal, the satisfaction degree of users is greatly influenced by stability and high efficiency, but the existing research on the operation influence factors of the SECC combined distribution network is too little are solved.
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Fig. 1 is a schematic flowchart of an embodiment of a method for optimizing operating efficiency of a power distribution network according to the present application;
fig. 2 is a schematic flow chart of another embodiment of a power distribution network operation efficiency optimization method provided by 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.
According to the optimization method for the operation efficiency of the power distribution network, the technical problems that the distribution network in the prior art is used as a power transmission terminal, the satisfaction degree of users is greatly affected by stability and high efficiency, but the existing research on the operation influence factors of the SECC combined distribution network is too little are solved only by respectively establishing the optimization models of each SECC system.
For convenience of understanding, please refer to fig. 1, where fig. 1 is a schematic flowchart of an embodiment of a method for optimizing operating efficiency of a power distribution network provided in the present application.
The embodiment of the application provides a method for optimizing the operating efficiency of a power distribution network, which comprises the following steps:
100, acquiring a cost function of the consumption cost of each subunit of each SECC system at a preset moment, and establishing a target function of each SECC system according to the cost function;
200, obtaining the constraint conditions of each subunit of each SECC system, and establishing an optimization model of each SECC system according to the constraint conditions and the objective function.
It should be noted that, when the target output power is established according to the obtained output power model, the total operation cost of the M distribution networks of the optimization model of the multi-SECC distribution network in the operation period T is the lowest. The method comprises the steps of firstly establishing a cost function of the consumption cost of each subunit of each SECC system at a preset moment, then establishing an objective function corresponding to the SECC system according to the cost function, establishing constraint conditions of each subunit of the SECC system in an optimization process based on the lowest total operation cost according to the objective function in the process of the lowest total operation cost, and establishing an optimization model of the objective function according to the constraint conditions. After the optimization model is established, an optimization engine can be used for obtaining an optimization result, wherein the optimization engine can adopt CPLEX, and the engine can be used as an efficient and stable optimization engine and can be used for solving optimization such as Linear programming, mixed integer programming and quadratic constraint programming. Compared with other optimization engines, the operation optimization can be converted into the mixed integer programming problem when the mixed integer programming is processed.
For easy understanding, please refer to fig. 2, which is a schematic flow chart of another embodiment of a method for optimizing operating efficiency of a power distribution network according to the present application.
Further, obtaining a cost function of the consumption cost of each subunit of each SECC system at a preset time, and before establishing the objective function of each SECC system according to the cost function, the method includes:
300, acquiring output power models corresponding to a plurality of sub energy supply modules in each SECC system;
and 400, acquiring a building temperature control model of the transformer substation in each SECC system and/or an energy model of the electric automobile in the SECC system.
It should be noted that, in the distribution network interconnection system with multiple SECCs, each SECC distribution network energy supply module may be multiple, for example, a substation, a distributed photovoltaic, a gas turbine energy center, etc., that is, correspond to multiple sub-energy supply modules, at this time, different energy supply module characteristics may be provided to obtain a power output model corresponding to each supply module so as to obtain energy storage capacity of each supply module in the SECC distribution network, and meanwhile, in consideration of the substation, an outdoor heat source of a substation building is mainly formed by heat transfer of a building enclosure (an outer wall and a roof) and solar radiation penetrating through a glass window, and an indoor load is caused by heat dissipation of a human body and heat dissipation of electric equipment. And the heat storage property of the building is related to the cold load, and the building has the property similar to energy storage, so that the economy of the system can be further improved. The expression of the heat storage property of the building obtained according to the energy conservation is shown as the following formula:
QTv(t)＝ρCV(Tin(t)-Tin(t-1)) (1)
in the formula, QTv(t) represents the heat storage capacity of the building; ρ is the air density; c is the specific heat capacity of air; v is the indoor air capacity; tin (t) is the room temperature at time t.
Thus, the building cooling load demand can be written in the form of:
Qcl(t)＝Q1+Q2+Q3+Qheat-QTv(t) (2)
in the formula, Q1＝kwallFwall(Tout(t)-Tin(t)) is the heat transferred from the exterior walls of the building to the outside, where KwallIs the heat transfer coefficient of the building exterior wall, FwallIs the building exterior wall area; t isout(t)Is the outdoor temperature at time t. Q2＝kwinFwin(Tout(t)-Tin(t)) is the heat transferred from the exterior window of the building to the outside, where KwinIs the heat transfer coefficient of the building exterior window, FwinIs the area of the building exterior window. Q3＝λSCItFwinHeat transfer for solar thermal radiation, where I is solar radiation power，λSCThe shading coefficient is shown. QheatThe heat energy is the heating power of indoor heat sources, such as the heating of human bodies and electric equipment. Namely, the approximate energy storage effect of the building temperature control model is obtained through the building temperature control model of the transformer substation so as to supplement the output power model in the SECC system.
The electric automobile can participate in the regulation of a distribution network as a mobile energy source, smoothes a load curve, and improves the stability of the SECC cooperative operation. For example: 1. the electric vehicle with the electric quantity lower than 60% has a charging requirement, and needs to be charged to at least 80% of the rated capacity when the electric vehicle leaves; the electric vehicle with the electric quantity not lower than 60% has no need of charging, and the capacity at the leaving moment can be the same as the electric quantity at the entering of the system. 2. The total scheduling time is T, N electric vehicles can participate in scheduling, the electric vehicles are mainly used for meeting the travel requirements of users, the actual conditions of different vehicles are different, and the types of relevant parameters of any vehicle are consistent. 3. The charging and discharging power of the power supply battery of the electric automobile keeps constant in a single time period, and the power supply battery is a lithium battery. Setting the starting access time of a vehicle k as Tk,inA departure time of Tk,outThen, the expression of the running state of the electric vehicle is as follows:
Wk,EV(t)＝Wk,EV(t-1)+Pk,EV(t) (5)
in the formula, 1 represents that the electric vehicle k can participate in scheduling, and 0 represents that charging and discharging operations cannot be carried out; pk,EVin(t) and Pk,EVout(t) represents the charge and discharge power of the vehicle k at time t, respectively; etac、ηdRespectively represents the charge and discharge power, W, of the electric vehiclek,EV(t) is the electric quantity of the vehicle k at time t.
Further, the sub energy supply module comprises a photovoltaic power station; the obtaining of the output power models corresponding to the plurality of sub-energy supply modules in each SECC system comprises: and establishing a photovoltaic clustering model for the photovoltaic power station, and acquiring an output power model of the photovoltaic power station.
In an SECC system including a photovoltaic power plant, an output power model of the photovoltaic power plant is obtained in the following manner. Assuming that the temperature of the photovoltaic cell is equal to the ambient temperature, the storage capacity of the single photovoltaic cell is as follows:
in the formula (I), the compound is shown in the specification,
representing the electric energy power of the kth photovoltaic cell in a t period;
representing the rated power of the kth photovoltaic cell under the standard test condition;
representing the illumination intensity of the kth photovoltaic cell in the t period; k is a radical ofTRepresents the power temperature coefficient;
represents the kth photovoltaic cell temperature; t isrRepresents a reference temperature; gSTCIndicating the intensity of light under standard test conditions.
A photovoltaic power generation system (PV) component includes a photovoltaic cell array and a DC/AC alternating current module corresponding to the photovoltaic array. Each photovoltaic cell array is composed of a plurality of cell panels. If the area of each photovoltaic cell panel is AiTotal of s blocks, and conversion efficiency of photoelectric conversion is etaiTotal photovoltaic reserve of Ppv。
PPV＝Arη (9)
In the formula, A is the total area of the photovoltaic cell; eta is the total conversion efficiency of the photovoltaic power station.
The process of establishing the photovoltaic clustering model for the photovoltaic power station comprises the following steps: and obtaining an output power model of the photovoltaic power station by adopting a model C mean value clustering method. In order to make the photovoltaic cell model as close to reality as possible and increase the calculation speed, the model C-means clustering (FCM) output is used to simulate the actual photovoltaic output. The FCM is decomposed into C fuzzy groups, and the clustering center (i ═ 1, 2, …, C) of each group is solved so that the cost function of the non-similarity index is minimized. FCM adopts fuzzy division, and the degree of the data points belonging to each group is determined by using the membership degree of each data point, wherein the membership degree is 0-1. By normalization, the sum of the membership levels of one dataset is equal to 1. The objective function is the minimum cumulative distance to the cluster center, and the actual cluster function is:
in the above formula, M represents a Distance function, and a conventional Distance function covers Euclidean Distance (ED), Manhattan Distance (MD), and the like; and m is a fuzzy C-means clustering flexibility parameter.
Further, the output power model includes: and acquiring output power models corresponding to different supply modules according to the different supply modules.
It should be noted that, in the distribution network interconnection system with multiple SECCs, each SECC distribution network energy supply module may be multiple, for example, a substation, a distributed photovoltaic, a gas turbine energy center, etc., that is, correspond to multiple sub-energy supply modules, at this time, different energy supply module characteristics may be provided to obtain a power output model corresponding to each supply module so as to obtain energy storage capacity of each supply module in the SECC distribution network, and meanwhile, in consideration of the substation, an outdoor heat source of a substation building is mainly formed by heat transfer of a building enclosure (an outer wall and a roof) and solar radiation penetrating through a glass window, and an indoor load is caused by heat dissipation of a human body and heat dissipation of electric equipment. And the heat storage property of the building is related to the cold load, and the building has the property similar to energy storage, so that the economy of the system can be further improved.
Further, the building temperature control model comprises: and acquiring the approximate energy storage effect of the building temperature control model, and supplementing the output power model in each SECC system.
It should be noted that after the building temperature control model of the substation is established, the approximate energy storage effect is obtained to supplement the output power model of the SECC.
Further, the cost function includes: the maintenance cost, the natural gas cost, the main network interaction cost, the power transmission cost and the temperature transmission cost of each sub-distribution network.
It should be noted that the cost function of the consumption cost of each subunit includes: the maintenance cost, the natural gas cost and the main network interaction cost of each sub-distribution network, and the power output cost and the temperature penalty cost between the distribution networks; the total operation cost of the distribution network in the operation period of the multi-SECC power distribution network optimization model is the lowest, and the constructed objective function meets the following formula:
wherein s represents a preset distribution network, t is a preset moment, Cs,op(t) maintenance cost of distribution network, Cs,gas(t) Natural gas cost, C for distribution networks,grid(t) major network interaction cost for distribution network, Cs,eg(t) cost of inter-distribution network Power Transmission for distribution network, Cs,TAnd (t) punishing the cost of the temperature of the distribution network. And a cost function is established based on each cost in the distribution network, and the maintenance cost of the distribution network takes the power output of each subunit into consideration, so that the final maintenance cost can be selected according to the power output, and the objective function of the distribution network optimization can meet the requirement.
The establishment of the maintenance cost function of the distribution network needs to satisfy the following formula:
Cs,op(t)＝CWTPs,WT(t)+CPVPs,PV(t)+CMTPs,MT(t)+CGBQs,GB(t)+CACPs,AC(t)+CARPs,AR(t)+CHEQs,HE(t)+CSB|Ps,SBc(t)-Ps,SBd(t)
(15)
wherein, Ps,WT(t)、Ps,PV(t)、Ps,MT(t)、Qs,GB(t)、Ps,AC(t)、Ps,AR(t) and Qs,HE(t) output powers of a fan, a photovoltaic power station, a gas turbine, a gas boiler, an air conditioner, a refrigerator and a heat exchanger of a distribution network at a preset moment are respectively set; cWT、CPV、CMT、CGB、CAC、CARAnd CHEUnit maintenance costs of a fan, a photovoltaic power station, a gas turbine, a gas boiler, an air conditioner, a refrigerator and a heat exchanger of a distribution network are respectively set; ps,SBc(t)、Ps,SBd(t) and CSBThe storage power and the unit maintenance cost of the storage battery at the preset moment are respectively.
Natural gas cost of distribution network Cs,gas(t) may satisfy the following equation:
Cgasis the unit natural gas cost; ps,gas(t) gas power provided to a gas source point; LHVG is the specific heat value of natural gas; etaGBIs the ratio of the heat production efficiency of the gas boiler.
Major network interaction cost C of distribution networks,grid(t) satisfies the following formula:
Cs,grid(t)＝CBEPs,BE(t)-CSEPs,SE(t) (17)
wherein, CBE、CSE、Ps,BEAnd Ps,SEThe electricity purchasing and selling cost and the electricity purchasing and selling power interacted with the main network are respectively;
distribution network power transmission cost C of distribution networks,eg(t) satisfies the following formula:
Cs,eg(t)＝CbuyPs,bf(t)-CsellPs,sf(t) (18)
wherein, CbuyAnd CsellRepresenting cost of power transfer between distribution networks, Ps,bf(t) and Ps,sf(t) power purchased by other systems and power delivered by these other systems for time t s substation-energy center system;
temperature penalty cost C of distribution networks,T(t) satisfies the following formula:
Cs,T(t)＝γT|Ts,in(t)-Ts,set| (19)
wherein, γTIs the temperature penalty in units of units/deg.C; t iss,in(T) and Ts,setThe actual room temperature and the expected user value of the substation building are respectively.
Further, the constraint condition comprises an electric heating cold load and a first preset constraint condition which is met when the electric vehicle participates in the distribution network.
It should be noted that, when the optimization model corresponding to the SECC system is established based on the constraint condition and the objective function, the constraint condition includes a first preset constraint condition that is satisfied when the electric vehicle participates in the distribution network and the electric heating cold load, and the first preset constraint condition includes:
Qs,MT(t)+Qs,GB(t)＝Qs,ARin(t)+Qs,HEin(t) (21)
Qs,AR(t)+Qs,AC(t)＝Qs,cl(t) (22)
Qs,HE(t)＝Qs,hl(t) (23)
Ps,EVout(t)＝Ps,EVon(t)+Ps,EVnb(t) (24)
wherein, Ps,load(t) is the conventional electrical load, P, at the preset moment of the distribution networks,EVin(t) and Ps,EVout(t) total charging and discharging power Q of the electric vehicle at a preset moment respectivelys,MT(t)、Qs,GB(t)、Qs,ARin(t) and Qs,HEin(t) is the output power of the gas turbine and the gas boiler, the input power of the refrigerator and the heat exchanger, Qs,cl(t)、Qs,AR(t) and Qs,AC(t) Cold load, Q, at a preset moment of distribution networks,hl(t) Heat load at distribution network Preset time, Qs,HE(t) input power of heat exchanger, Ps,EVout(t)、Ps,EVon(t) and Ps,EVnb(t) the power of the remaining and used electric vehicles, respectively.
Further, the constraint condition comprises a second preset constraint condition which is met by the power interaction of the transformer substation.
It should be noted that, when the optimization model corresponding to the SECC system is established based on the constraint condition and the objective function, the constraint condition may further include a second preset constraint condition that is satisfied by power interaction of the substation, where the second preset constraint condition includes:
Ps,gmin≤|Ps,BE(t)-Ps,SE(t)|≤Ps,gmax (26)
Ps,egmin≤|Ps,bf(t)-Ps,sf(t)|≤Ps,egmax (27)
wherein, Ps,gminAnd Ps,gmaxRespectively is the lower limit and the upper limit of the interaction power between the transformer substation and the main network in the distribution network, Ps,o,minAnd Ps,o,maxThe lower limit and the upper limit of power interaction between the transformer substation in the distribution network and the transformer substations in other distribution networks are respectively set.
Further, the constraint condition comprises a third preset constraint condition met by the controllable unit gas turbine.
It should be noted that the gas turbine may include a controllable unit gas turbine, and when the optimization model corresponding to the SECC system is established based on the constraint condition and the objective function, the constraint condition may further include a third preset constraint condition that is satisfied by the controllable unit gas turbine, where the third preset constraint condition includes:
Ps,Cmin≤Ps,C(t)≤Ps,Cmax (28)
-RsC,downΔt≤Ps,C(t)-Ps,C(t-1)≤RsC,upΔt (29)
wherein, Ps,C(t) the output of the gas turbine of the controllable unit of the distribution network at a preset moment; ps,CminAnd Ps,CmaxRespectively is the lower limit value and the upper limit value of the output of the gas turbine of the controllable unit; -Rs,CdownAnd Rs,CupThe lower climbing speed and the upper climbing speed of the gas turbine of the controllable unit are respectively controlled; Δ t is a unit scheduling time.
Further, the constraint condition includes a fourth constraint condition that the electric vehicle satisfies.
It should be noted that, when the optimization model corresponding to the SECC system is established based on the constraint condition and the objective function, the constraint condition may further include a fourth preset constraint condition that is satisfied by the electric vehicle, where the fourth preset constraint condition includes:
wherein the content of the first and second substances,
rated capacity of electric vehicle, Wk,EV(Tk,o) And Wk,EV(Tk,i) The power of charging and discharging of the electric automobile is respectively.
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 (8)
1. A method for optimizing the operating efficiency of a power distribution network is characterized by comprising the following steps:
acquiring output power models corresponding to a plurality of sub energy supply modules in each SECC system;
acquiring a building temperature control model of a transformer substation in each SECC system and an energy model of an electric automobile in each SECC system; obtaining the approximate energy storage effect of the building temperature control model, and supplementing an output power model in each SECC system;
obtaining a cost function of the consumption cost of each subunit of each SECC system at a preset moment, and establishing a target function of each SECC system according to the cost function, wherein the cost function comprises a temperature punishment cost function, and the temperature punishment cost function is as follows:
Cs,T(t)＝γT|Ts,in(t)-Ts,set|
wherein, γTIs the temperature penalty in units of units/deg.C; t iss,in(T) and Ts,setRespectively representing the actual room temperature and the expected value of a user of the transformer substation building;
and acquiring the constraint conditions of each subunit of each SECC system, and establishing an optimization model of each SECC system according to the constraint conditions and the objective function.
2. The method of optimizing operational efficiency of a power distribution network of claim 1, wherein the sub-energy supply modules comprise photovoltaic power plants; the obtaining of the output power models corresponding to the plurality of sub-energy supply modules in each SECC system includes: and establishing a photovoltaic clustering model for the photovoltaic power station, and acquiring an output power model of the photovoltaic power station.
3. The method of optimizing operational efficiency of a power distribution network of claim 1, wherein the output power model comprises: and acquiring output power models corresponding to different supply modules according to the different supply modules.
4. The method of optimizing operational efficiency of a power distribution network of claim 1, wherein the cost function comprises: the maintenance cost, the natural gas cost, the main network interaction cost, the power transmission cost and the temperature transmission cost of each sub-distribution network.
5. The method for optimizing the operation efficiency of the power distribution network according to claim 1, wherein the constraint conditions comprise electric heating and cooling loads and a first preset constraint condition which is met when the electric vehicle participates in the power distribution network.
6. The method for optimizing the operating efficiency of the power distribution network according to claim 1, wherein the constraint condition comprises a second preset constraint condition that the power interaction of the substation satisfies.
7. The method of claim 1, wherein the constraint condition comprises a third predetermined constraint condition that is satisfied by a controllable group gas turbine.
8. The method of claim 1, wherein the constraint condition comprises a fourth constraint condition satisfied by the electric vehicle.
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