CN108037667B - Base station electric energy optimal scheduling method based on virtual power plant - Google Patents

Base station electric energy optimal scheduling method based on virtual power plant Download PDF

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CN108037667B
CN108037667B CN201711347288.XA CN201711347288A CN108037667B CN 108037667 B CN108037667 B CN 108037667B CN 201711347288 A CN201711347288 A CN 201711347288A CN 108037667 B CN108037667 B CN 108037667B
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唐平
岳东
陈剑波
梁勋
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Jiangsu Xin Yunchang Science & Technology Co ltd
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Abstract

The invention discloses a base station electric energy optimal scheduling method based on a virtual power plant, which comprises the following steps: (1) firstly, establishing an interaction model between a micro-grid containing a base station and a virtual power plant and a traditional large power grid; (2) establishing an optimized scheduling model with the maximum scheduling profit of a virtual power plant containing a base station as a purpose; (3) the method comprises the steps of establishing a model by predicting wind and light and aiming at minimizing energy abandon cost caused by uncertainty of clean energy; (4) processing the output uncertainty of the clean energy by adopting a robust optimization method for the constraint condition, and setting a prediction coefficient and a robust coefficient; (5) and finally, solving the optimal scheduling result by using computer software. The coordination among a plurality of micro-grids in the virtual power plant is considered, so that the operation scheduling cost of the virtual power plant is minimized, the consumption rate of clean energy is effectively improved, and the loss caused by uncertain output of the clean energy is reduced.

Description

Base station electric energy optimal scheduling method based on virtual power plant
Technical Field
The invention belongs to the field of optimized scheduling of power systems, and particularly relates to a base station electric energy optimized scheduling method based on a virtual power plant.
Background
A communication base station, as an important component of a wireless communication system, is a radio transceiver station that performs information transfer with a mobile phone terminal. The construction of mobile communication base stations is an important part of the investment of mobile communication operators in China, and is widely constructed in cities and villages. Meanwhile, with the increasing of energy pressure, the environmental problems are highlighted, and the rapid development of distributed energy sources mainly in the forms of wind power and photovoltaic is promoted. Communication base stations comprising equipment such as fans, photovoltaics and energy storage are also subject to sustainable development. The access of a large-scale communication base station to a large power grid inevitably increases the control difficulty of the operation of the power grid. Moreover, the distributed power sources are distributed in scattered positions in the ground, are greatly influenced by natural factors, and cause adverse effects on the power balance of the power system due to the fact that output uncertainty and intermittence are serious.
A Virtual Power Plant (VPP) can centralize a plurality of distributed power sources, energy storage devices, and loads, and implement optimized operation of energy sources through advanced coordination control technology, communication technology, and software system, thereby improving the utilization rate of clean energy and the overall economic benefit. For a plurality of micro-grids, coordinated operation among the micro-grids needs to be considered during optimal scheduling, and the total scheduling of a virtual power plant and local target benefits of each micro-grid need to be considered. The VPP aggregation communication base station and the wind-solar power plant participate in the operation of the power grid, and the negative influence on the power grid caused by the output of clean energy and the uncertainty of charging and discharging of the base station energy storage equipment can be effectively relieved by applying a proper optimal scheduling strategy; and the economic benefit and the environmental benefit are realized by peak clipping and valley filling on the premise of ensuring the stable operation of the power grid.
Disclosure of Invention
The purpose of the invention is as follows: aiming at the problems, the invention provides a base station electric energy optimal scheduling method based on a virtual power plant.
The technical scheme is as follows: in order to realize the purpose of the invention, the technical scheme adopted by the invention is as follows: a base station electric energy optimal scheduling method based on a virtual power plant comprises the following steps:
(1) establishing an interaction model between a micro-grid containing a base station and a virtual power plant and a traditional large power grid;
(2) establishing a first optimized scheduling model with the maximum scheduling profit of a virtual power plant containing a base station as a target;
(3) establishing a second optimized scheduling model aiming at minimizing the energy abandoning cost caused by uncertainty of the output of the clean energy;
(4) processing the output uncertainty of the clean energy by adopting a robust optimization method, and setting a prediction coefficient and a robust coefficient;
(5) and synthesizing the objective functions of the two models, and solving the optimal scheduling result by using computer software.
In the step (2), the objective function of the first optimized scheduling model is as follows:
Figure BDA0001509557770000021
Figure BDA0001509557770000022
Figure BDA0001509557770000023
in the formula, H represents a virtual power plant operation profit function in a time period t, nsRepresenting the total scheme number of the electricity price, T is the total time period number, pi(s) represents the probability of the electricity price of the s-th group of schemes, KtRepresenting the benefit of the period t, WtRepresenting the cost of the t period, cnUnit price of electricity, denoted as the electricity sold by the virtual power plant to the large grid, clRepresenting a unit price of electricity, P, for a virtual power plant selling electricity to a useri，n，t＝pi，w，t+pi，pv，tRepresents the power generation plan submitted by the base station microgrid i to the large power grid during the period t, wherein pi，w，t、pi，pv，tRespectively represents the planned active power output, P, of the ith base station microgrid fan unit and the photovoltaic unit in the t periodi，l，tRepresenting the demand of the base station microgrid i to supply power for the internal use of the base station during the time period t,
Figure BDA0001509557770000024
and (4) representing the power generation cost of the ith base station microgrid in a period t, wherein M represents the number of the microgrids.
In the step (3), the objective function of the second optimized scheduling model is:
Figure BDA0001509557770000025
where ρ isw，ρpvRespectively representing the economic losses of unit wind abandoning amount and light abandoning amount of the wind turbine generator and the photovoltaic generator,
Figure BDA0001509557770000026
and the actual output of the microgrid fan unit and the actual output of the photovoltaic unit of the ith base station are respectively represented.
In the step (4), the robust optimization auxiliary constraint conditions are as follows:
Figure BDA0001509557770000027
Figure BDA0001509557770000028
Figure BDA0001509557770000029
in the formula, mut,xtFor error coefficients, Γ is a robust coefficient, vtAnd representing the predicted value of the photovoltaic output of the fan.
Has the advantages that: the method adopts a stochastic programming method for the uncertainty of the electricity price, adopts a robust optimization method for the uncertainty of the wind-solar output of the prediction processing of the clean energy, and improves the calculation efficiency; meanwhile, environmental benefits and economic benefits are considered, the consumption rate of clean energy can be effectively improved on the basis of considering profit maximization, and losses caused by light and wind abandoning are reduced.
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FIG. 1 is a schematic diagram of a virtual power plant and grid interaction model.
Detailed Description
The technical solution of the present invention is further described below with reference to the accompanying drawings and examples.
Fig. 1 shows an interactive model of a virtual power plant with a base station and a traditional power grid, wherein the virtual power plant aggregates the traditional power grid, distributed energy sources, energy storage facilities and communication base stations together through a control center of the virtual power plant to participate in the operation of a power system.
In the time period of low electricity price and small load, the power grid charges the energy storage battery of the communication base station and the centralized energy storage unit through the charging and discharging device, and the charging electric quantity is provided by the residual electric quantity obtained by subtracting the electricity consumption of the load from the generated energy quantity of the power grid. If the residual electric quantity meets the total charging quantity required by the two batteries simultaneously, the two batteries are charged simultaneously; if the residual electric quantity can not meet the total charging quantity required by the communication base station and the base station simultaneously, the communication base station battery is charged preferentially, the concentrated energy storage unit is charged after the communication base station energy storage battery is fully charged, and the communication base station energy storage battery is used for base station signal processing.
In the time period of high electricity price and large power grid load, the centralized energy storage unit checks whether the base station energy storage battery stored in the power station is enough, if not, the base station energy storage battery is charged preferentially, and the power grid is discharged through the charging and discharging device under the condition of meeting the power demand of the communication base station.
When the virtual power plant is connected to the power grid, the control center can comprehensively compare the power grid and the micro power grid of the base station to carry out power interaction, and a scheduling scheme is established to sell or purchase electric energy to the power grid with the aim of maximizing the scheduling profit of the virtual power plant of the base station. The output of the fan unit and the photovoltaic unit is greatly influenced by nature, and randomness and uncertainty are caused, so that the phenomena of wind abandoning, light abandoning and the like of clean energy are caused, and energy waste and economic loss are brought. Therefore, it is necessary to predict the wind and light to participate in the virtual power plant operation scheduling with the aim of minimizing the energy rejection cost caused by uncertainty of the clean energy output.
The invention discloses a base station electric energy optimal scheduling method based on a virtual power plant, which comprises the following steps:
(1) establishing an interaction model between a micro-grid containing a base station and a virtual power plant and a traditional large power grid;
(2) establishing a first optimized scheduling model with the maximum scheduling profit of a virtual power plant containing a base station as a target;
the objective function of the model is:
Figure BDA0001509557770000031
Figure BDA0001509557770000032
Figure BDA0001509557770000033
in the formula (I), the compound is shown in the specification,h represents a virtual power plant operation profit function in the t period, nsRepresenting the total scheme number of the electricity price, T is the total time period number, pi(s) represents the probability of the electricity price of the s-th group of schemes, KtRepresenting the benefit of the period t, WtRepresenting the cost of the t period, cnUnit price of electricity, denoted as the electricity sold by the virtual power plant to the large grid, clRepresenting a unit price of electricity, P, for a virtual power plant selling electricity to a useri，n，t＝pi，w，t+pi，pv，tRepresents the power generation plan submitted by the base station microgrid i to the large power grid during the period t, wherein pi，w，t、pi，pv，tRespectively represents the planned active power output, P, of the ith base station microgrid fan unit and the photovoltaic unit in the t periodi，l，tRepresenting the demand of the base station microgrid i to supply power for the internal use of the base station during the time period t,
Figure BDA0001509557770000034
and (4) representing the power generation cost of the ith base station microgrid in a period t, wherein M represents the number of the microgrids.
The microgrid generation cost may be expressed as:
Figure BDA0001509557770000041
in the formula, NiN-th micro-power supply, k, representing the ith micro-gridi，jRepresents the jth micro-power supply of the ith base station micro-grid, ckijIs the k-thi，jCost of individual micro-power source, pkij，tIs the kth time period of ti，jThe output of each micro power supply.
Establishing a constraint condition to enable the power system to meet the constraint of the power generation plan of the virtual power plant, wherein the internal constraint condition is that the total output of all micro power sources of the base station micro power grid i in the virtual power plant is as follows:
Figure BDA0001509557770000042
Figure BDA0001509557770000043
wherein p isi，ch，t、pi，dch，tRespectively representing the charging and discharging power of a storage battery in the micro-grid i of the base station,
Figure BDA0001509557770000044
and charging and discharging efficiency of a storage battery in the base station microgrid i.
Establishing constraint conditions of each base station microgrid:
(a) and (3) power balance constraint of the ith base station microgrid:
Figure BDA0001509557770000045
(b) the output constraint of the controllable micro power supply of the ith base station sub-micro power grid is as follows:
Figure BDA0001509557770000046
(c) the ith sub-microgrid and the large power grid are subjected to transmission power constraint:
Figure BDA0001509557770000047
(d) and (3) charge and discharge restraint of the storage battery:
Figure BDA0001509557770000048
Figure BDA0001509557770000049
Et＝Et-1+xchpi,ch,tηch-xdchpi,dch,t
xch+xdch≤1
Figure BDA00015095577700000410
in the formula (I), the compound is shown in the specification,
Figure BDA00015095577700000411
respectively representing the lower limit and the upper limit of the transmission power of the ith base station sub-micro-grid and the large power grid,
Figure BDA00015095577700000412
represents the lower and upper limits of the storage battery charging power of the base station microgrid i,
Figure BDA00015095577700000413
represents the lower and upper limits of the discharge power of the storage batteries of the base station microgrid i, EtRepresenting the battery capacity, x, during a period tch、xdchRespectively representing the charging and discharging state variable quantity of a storage battery in the base station microgrid i to be 0 or 1, and soci representing the charging state of the storage battery of the base station microgrid i
Figure BDA0001509557770000051
And (4) representing the charging state of a storage battery of the base station microgrid i to be on and off.
(3) Forecasting wind and light, and establishing a second optimized scheduling model with the aim of minimizing energy abandon cost caused by uncertainty of output of clean energy;
the model objective function is:
Figure BDA0001509557770000052
where ρ isw，ρpvRespectively representing the economic losses of unit wind abandoning amount and light abandoning amount of the wind turbine generator and the photovoltaic generator,
Figure BDA0001509557770000053
and the actual output of the microgrid fan unit and the actual output of the photovoltaic unit of the ith base station are respectively represented.
(4) Processing the output uncertainty of the clean energy by adopting a robust optimization method, and setting a prediction coefficient and a robust coefficient;
the output of each base station microgrid photovoltaic unit fan unit of the virtual power plant is greatly influenced by natural conditions, the actual transmission capacity and the required electric quantity are required to have certain deviation, and constraint conditions are established for ensuring that the total transmission capacity of one day is the same.
(a) Constraint conditions for the same total amount of power transmission:
Figure BDA0001509557770000054
Figure BDA0001509557770000055
in the formula (I), the compound is shown in the specification,
Figure BDA0001509557770000056
representing the actual electric quantity meeting the requirement of the base station microgrid at the time t, wherein omega is an allowable deviation coefficient omega epsilon [0,1]。
(b) Overall power constraint of the virtual power plant:
Figure BDA0001509557770000057
in the formula, vtRepresenting the predicted value of the photovoltaic output of the fan, ctExpressed as power purchase, outt、intIndicating the amount of discharge and the amount of charge, lambda1、λ2Indicating the power generation efficiency and the energy storage efficiency.
(c) Robust optimization auxiliary constraint conditions and coefficient setting:
Figure BDA0001509557770000058
Figure BDA0001509557770000059
Figure BDA00015095577700000510
in the formula, mut,xtAuxiliary variables are all error coefficients, gamma is a robust coefficient, and gamma belongs to [0,1 ]]And the predicted value of the wind-solar output is [ (1-mu)t)vt,(1+μt)vt]The internal wave motion is carried out by the internal wave motion,
Figure BDA0001509557770000061
for an arbitrary period of t, FtIs composed of
Figure BDA0001509557770000062
Wherein the error coefficient xtAnd the robust coefficient Γ is set as follows:
Figure BDA0001509557770000063
(5) and synthesizing the two objective functions and solving the optimal scheduling result by using computer software.
And solving a base station electric energy optimization scheduling scheme based on a virtual power plant, so that the optimal output of the clean energy generator set, the charge and discharge power of the energy storage device and the optimal interaction power with a power grid can be obtained, and economic benefits and environmental benefits are considered.
The above examples are merely illustrative of the present invention, and it is within the scope of the present invention that modifications to the specific embodiments of the invention or equivalent substitutions in various forms will occur to those skilled in the art.

Claims (6)

1. A base station electric energy optimal scheduling method based on a virtual power plant is characterized by comprising the following steps: the method comprises the following steps:
(1) establishing an interaction model between a micro-grid containing a base station and a virtual power plant and a traditional large power grid;
(2) establishing a first optimized scheduling model with the maximum scheduling profit of a virtual power plant containing a base station as a target;
the objective function of the first optimized scheduling model is:
Figure FDA0002928992020000011
Figure FDA0002928992020000012
Figure FDA0002928992020000013
in the formula, H represents a virtual power plant operation profit function in a time period t, nsRepresenting the total scheme number of the electricity price, T is the total time period number, pi(s) represents the probability of the electricity price of the s-th group of schemes, KtRepresenting the benefit of the period t, WtRepresenting the cost of the t period, cnUnit price of electricity, denoted as the electricity sold by the virtual power plant to the large grid, clRepresenting a unit price of electricity, P, for a virtual power plant selling electricity to a useri，n，t＝pi，w，t+pi，pv，tRepresents the power generation plan submitted by the base station microgrid i to the large power grid during the period t, wherein pi，w，t、pi，pv，tRespectively represents the planned active power output, P, of the ith base station microgrid fan unit and the photovoltaic unit in the t periodi，l，tRepresenting the demand of the base station microgrid i to supply power for the internal use of the base station during the time period t,
Figure FDA0002928992020000014
representing the power generation cost of the ith base station microgrid in a time period t, wherein M represents the number of the microgrids;
(3) establishing a second optimized scheduling model aiming at minimizing the energy abandoning cost caused by uncertainty of the output of the clean energy;
(4) processing the output uncertainty of the clean energy by adopting a robust optimization method, and setting a prediction coefficient and a robust coefficient;
(5) and synthesizing the objective functions of the two models, and solving the optimal scheduling result by using computer software.
2. The base station electric energy optimization scheduling method based on the virtual power plant as claimed in claim 1, characterized in that: the power generation cost of the ith base station microgrid in the period t is as follows:
Figure FDA0002928992020000015
in the formula, NiN-th micro-power supply, k, representing the i-th base station micro-gridi，jRepresents the jth micro-power supply of the ith base station micro-grid, ckijIs the k-thi，jCost of individual micro-power source, pkij，tIs the kth time period of ti，jThe output of each micro power supply.
3. The base station electric energy optimization scheduling method based on the virtual power plant as claimed in claim 2, characterized in that: the total output of all micro power sources of the ith base station micro grid in the virtual power plant is as follows:
Figure FDA0002928992020000016
Figure FDA0002928992020000017
wherein p isi，ch，t、pi，dch，tRespectively representing the charging and discharging power of a storage battery in the micro-grid i of the base station,
Figure FDA0002928992020000021
and charging and discharging efficiency of a storage battery in the base station microgrid i.
4. The base station electric energy optimization scheduling method based on the virtual power plant as claimed in claim 2 or 3, characterized in that: in the step (3), the objective function of the second optimized scheduling model is:
Figure FDA0002928992020000022
where ρ isw，ρpvRespectively representing the economic losses of unit wind abandoning amount and light abandoning amount of the wind turbine generator and the photovoltaic generator,
Figure FDA0002928992020000023
and the actual output of the microgrid fan unit and the actual output of the photovoltaic unit of the ith base station are respectively represented.
5. The base station electric energy optimization scheduling method based on the virtual power plant as claimed in claim 4, characterized in that: in the step (4), the robust optimization auxiliary constraint conditions are as follows:
Figure FDA0002928992020000024
Figure FDA0002928992020000025
Figure FDA0002928992020000026
in the formula, mut,xtFor error coefficients, Γ is a robust coefficient, vtThe predicted value of the photovoltaic output of the fan is shown,
Figure FDA0002928992020000028
for an arbitrary period of t.
6. The base of claim 5The method for optimizing and scheduling the base station electric energy of the virtual power plant is characterized by comprising the following steps: error coefficient utAnd the robust coefficient Γ is set as follows:
Figure FDA0002928992020000027
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