CN116187019A - Virtual power plant economic dispatching method with wind-solar storage considering step carbon price - Google Patents

Virtual power plant economic dispatching method with wind-solar storage considering step carbon price Download PDF

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CN116187019A
CN116187019A CN202211730432.9A CN202211730432A CN116187019A CN 116187019 A CN116187019 A CN 116187019A CN 202211730432 A CN202211730432 A CN 202211730432A CN 116187019 A CN116187019 A CN 116187019A
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乐鹰
周保中
李忆
许皓文
张继广
郭超
孙诗杰
谢康
吕若佳
孙宇航
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Shaoxing Research Institute Of Zhejiang University
Huadian Electric Power Research Institute Co Ltd
Hangzhou City University
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Huadian Electric Power Research Institute Co Ltd
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Abstract

The invention discloses an economic dispatching method of a virtual power plant containing wind and light storage considering ladder carbon price, which comprises the following steps: collecting wind-solar historical data containing the geographical position of the virtual power plant, modeling the output of a wind power photovoltaic unit in the system, expressing uncertainty of the wind power photovoltaic unit by using Weibull distribution and beta distribution respectively, and expressing the influence of the uncertainty on the virtual power plant through cost; establishing an energy storage operation model by using the state of charge, and determining the operation cost of energy storage; establishing a carbon transaction system based on the step carbon price, and determining the carbon transaction cost of the virtual power plant in the carbon transaction system; obtaining a model objective function based on the cost function of the previous step, and establishing an economic dispatch model of the whole virtual power plant containing the wind and solar energy storage by combining with the determination of the safe operation constraint of the system; and finally, carrying out optimization calculation to obtain data information such as output, line tide, carbon emission, cost and the like of each unit in the virtual power plant.

Description

Virtual power plant economic dispatching method with wind-solar storage considering step carbon price
Technical Field
The invention relates to an economic dispatching method of a virtual power plant, in particular to an economic dispatching method of a virtual power plant containing wind and solar energy storage, which considers ladder carbon price.
Background
Under the new carbon tax constraint, the requirements of the power system reach a new constraint balance. Meanwhile, the power system shows new forms after year development, and the characteristics of the power system such as safety, structural characteristics and the like are changed, wherein the most prominent is new energy power with the highest proportion year by year. Compared with traditional energy sources such as thermal power, the power system with high wind-solar-light-distributed new energy ratio has more outstanding new energy characteristics. However, due to these new changes, new theories and practices are required for economic dispatch of the entire power system.
The virtual power plant is a power coordination management system which realizes the aggregation and coordination optimization of DERs (distributed devices) such as DGs, energy storage systems, controllable loads, electric vehicles and the like through advanced information communication technology and software systems, and is used as a special power plant to participate in the operation of an electric power market and a power grid. The addition of carbon tax will also have a corresponding effect on the virtual power plant, and consideration is required to study the characteristics of the effect.
Disclosure of Invention
In order to solve the problems in the background art, the invention provides an economic dispatching method for a virtual power plant which is adaptive to wind-solar energy storage distributed energy, and meanwhile, the certainty of a conventional unit and the probability of distributed resources are considered, and the influence of stepped carbon price and carbon emission is considered.
The invention solves the problems by adopting the following technical scheme: the virtual power plant economic dispatching method containing wind and light storage considering the step carbon price is characterized by comprising the following steps of:
step 1, building a wind-electricity photovoltaic output model, collecting local wind-light historical data of a system, and predicting wind-light output by utilizing the historical data; and fitting uncertainty of wind speed by using Weibull distribution, fitting uncertainty of illumination by using beta distribution, converting influence of uncertainty on VPP into cost, and establishing a cost model of wind power photovoltaic.
And 2, establishing an energy storage model, describing the running state of the energy storage by using the state of charge so as to obtain the running model, and expressing the running cost of the energy storage.
And 3, establishing a carbon quota, carbon emission and a carbon transaction model based on the stepped carbon price of the virtual power plant, and determining the carbon transaction cost of the virtual power plant.
And 4, determining an objective function, and obtaining a total cost function based on the wind power photovoltaic cost, the energy storage cost and the carbon transaction cost obtained in the step 1, the step 2 and the step 3, and the power generation cost, the load shedding cost and the electricity selling cost of other traditional units.
And 5, modeling a tide system, and establishing an optimal tide economic dispatch model of the virtual power plant by taking the minimum cost as a target and combining unit line data of an actual system and determining constraint conditions according to system safe operation constraint based on the total cost function obtained in the step 4.
And 6, calculating to obtain basic data by using a linear programming optimization algorithm, modifying the carbon transaction price level based on the models in the step 3 and the step 5, and performing simulation calculation by using MATLAB to obtain data information such as the output, the line trend, the carbon emission, the cost and the like of each unit in the virtual power plant, which are obtained by the system under each carbon tax level.
The step 1 specifically comprises the following steps:
firstly, obtaining data of the historical weather (illumination intensity and wind speed) of the land, carrying out frequency distribution statistics to obtain a light intensity and wind speed frequency distribution histogram, and respectively fitting by using Beta (Beta) distribution and Weibull (Weibull) distribution.
Beta distribution:
Figure BDA0004031358840000021
wherein:
Figure BDA0004031358840000022
L max 、μ Beta 、σ Beta the maximum deviation value, the average deviation value and the standard deviation value of solar irradiance are respectively represented.
Two-parameter weibull distribution:
Figure BDA0004031358840000023
wherein: c is a scale parameter, k is a shape parameter, and v is a wind speed.
The probability density functions of the output of the classical wind power and the output of the photovoltaic unit are respectively as follows:
Figure BDA0004031358840000024
Figure BDA0004031358840000025
wherein: w is the fan output, ρ is the air density, A is the wind turbine blade area; q (Q) PV Output of photovoltaic unit, Q PV(max) Is the upper limit of the photovoltaic output.
The wind power and photovoltaic prediction overestimation cost and the carbon tax penalty cost are actually the rotating spare capacity cost of the system; when the actual wind speed or the radiation intensity does not reach the predicted value, the output force of the wind power plant and the photovoltaic power station is smaller than the planned value, and the increase of the generation of the thermal power unit is needed to meet the power balance of the power system, namely the positive standby cost; in contrast, the thermal power generating unit needs to be reduced to meet the requirement, namely the negative standby cost is expressed as punishment caused by uncertainty.
According to the wind power photovoltaic probability density distribution function obtained in the step 1, the expected value of the positive and negative standby capacity can be obtained:
Figure BDA0004031358840000031
Figure BDA0004031358840000032
C id ＝K id E(F cd )
C iu ＝K iu E(F cu )
Figure BDA0004031358840000033
wherein: f (F) cd 、F cu Respectively positive standby capacity and negative standby capacity, E is the expected value of the positive standby capacity and the negative standby capacity, w i Predicting force for wind power/photovoltaic, w r For rated wind/photovoltaic output, f w (w) is a wind power/photovoltaic probability density distribution function; c (C) id 、C iu Respectively the positive and negative standby cost of the i units, C Pwind 、C Psun C is the cost of wind power and photovoltaic id(Pwind) 、C iu(Pwind) Respectively the positive and negative spare capacity cost of the fan unit i, C jd(Psun) 、C ju(Psun) Respectively positive and negative standby capacity costs, K of the photovoltaic unit j id 、K iu The positive standby cost coefficient and the negative standby cost coefficient under the output of new energy are respectively shown, and the NW and the NP are respectively the number of units of a fan and a photovoltaic.
In addition, positive and negative standby cost coefficients under the output of new energy are determined by adopting a method for simulating market auction, namely, the cost coefficients of the virtual power plant trading from the standby market. Dividing the cost of maximum output power by the maximum output power to obtain the unit power generation cost coefficient K of the thermal power generating unit i ＝C i (pi ,max )/p i,max From K i Starting a unit with the minimum value, sequentially inputting a load with the maximum power of the unit until the sum of the powers of the units is greater than the load, and adding K of the marginal units i Namely, positive standby cost coefficients under a certain load are determined; then from K i The largest unit starts, the load is put into the unit by the maximum power in turn until the sum of the power of the units is larger than the load, and the K of the marginal unit i I.e. as a negative backup cost factor under a certain load.
The step 2 specifically comprises the following steps:
the amount of power stored by an energy storage system can generally be measured by a State of Charge (SOC) and calculated by the following equation:
SOC(t)＝SOC(t-1)-E BESS (t-1)/R BESS
wherein SOC (t) is the state of charge of the t period, and SOC (t-1) is the state of charge of the t-1 period; e (E) BESS (t-1) is a time t-1Total discharge capacity of battery, R BESS The rated capacity of the storage battery is kW.h.
The cost can be expressed by the following formula:
Figure BDA0004031358840000041
wherein:
Figure BDA0004031358840000042
is the running cost of the ith battery energy storage, pi BESS Is a cost-consuming factor for battery energy storage systems,
Figure BDA0004031358840000043
Figure BDA0004031358840000044
the discharge power and the charge power of the battery, respectively.
The step 3 specifically comprises the following steps:
adopting a free initial carbon emission right distribution mode based on the generated energy, and distributing carbon emission limits to the virtual power plant as follows:
Figure BDA0004031358840000045
wherein: e (E) q Gratuitous carbon emission quota established for the virtual power plant operators for the regulatory authorities; η is a carbon emission allowance for producing unit electric power; p (P) b,t 、P gt,t The outsourcing electric quantity at the time t and the output value of the traditional machine set are respectively.
The actual carbon emissions produced by the virtual power plant during the dispatch process are determined by the following equation:
Figure BDA0004031358840000046
wherein: e (E) p The carbon emission amount generated by the unit in the VPP is actually; a. b and c are fire respectivelyActual carbon emission coefficient of the motor unit;
the cost of carbon market trading based on step carbon prices is determined by the following formula:
Figure BDA0004031358840000047
/>
wherein: c (C) carbon Carbon transaction fees to be paid for the VPP operator; mu is the reference price of the carbon emission market; l is the interval length of each carbon emission; alpha is the price increasing rate of the step carbon trade, and every time the carbon emission amount increases by one interval, the price of the carbon trade increases by alpha mu.
The step 4 specifically comprises the following steps:
the system objective function is the total cost of the virtual power plant, and comprises the power generation cost of a common unit (coal power, gas power and nuclear power), the use cost of energy storage, the wind photovoltaic pre-estimation underestimation cost, the controllable load reduction cost and the carbon transaction cost.
1) The power generation cost of the traditional unit is as follows:
Figure BDA0004031358840000048
Figure BDA0004031358840000051
wherein: c (C) gt Is the total cost of the conventional unit; NG represents the number of conventional units;
Figure BDA0004031358840000052
as a cost function of the generator set, the generator set output is related; p (P) gt,i,t The generating capacity of the conventional unit i in the t period is represented by MW; a, a i 、b i 、c i Representing the cost coefficients of the generators, respectively.
2) The energy storage cost can be expressed by the following formula:
Figure BDA0004031358840000053
3) The corresponding costs incurred while adjusting the controllable load can be expressed by the following equation:
Figure BDA0004031358840000054
wherein: c (C) loadcurt Representing the operating cost of the controllable load; lambda (lambda) curt,t The compensation price at the time t; p is p curt,t The load interruption amount at time t.
4) The virtual power plant can buy and sell electric power with the outside through the connecting wire, and the generated cost is C market The method comprises the steps of carrying out a first treatment on the surface of the In addition, the virtual power plant supplies internal load demands to obtain revenues S load 。
Figure BDA0004031358840000055
Figure BDA0004031358840000056
C vppprice ＝λC elecprice
Wherein: c (C) elecprice For market electricity price, C vppprice The internal electricity price of VPP; p (P) purchase For electric power purchased from outside, P load Load requirements inside the virtual power plant; the internal electricity price is considered to be proportional to the market electricity price, with a coefficient of λ.
5) Total cost:
minC objective ＝C gt +C Pwind +C Psun +C BESS +C carbon +C loadcurt +C market -S load
wherein C is gt C is the power generation cost of the traditional unit Pwind And C Psun Respectively, high and low estimated cost of wind power photovoltaics, C BESS For energy storage cost, C carbon Cost for carbon trade，C loadcurt For load shedding cost.
The step 5 specifically comprises the following steps:
considering safety operation and other factors, the economic dispatch model operation requires the following constraints:
1) Direct current power flow constraint:
establishing an optimal power flow model for the system, in particular a direct current power flow model considering network loss; the specific direct current power flow formula is as follows:
P＝Bθ
wherein P is the active vector of the inflow node, θ is the node phase angle vector, and B is the negative number of the admittance matrix of the grid node during normal continuous operation.
2) Power balance constraint:
P ij ＝P i -P j
P i ＝P i load +P i BESS -P i wind -P i sun -P i gt -P i market -P i loadcurt
wherein P is ij For active power flow of transmission line from node i to node j, P i For the active power of node i, P i load For the active load at node i, P i BESS For the active load of the stored energy output at node i, P i wind 、P i sun 、P i gt Respectively the active power of wind power, photovoltaic and power generation output by a conventional unit at a node i, and P i market Active load purchased from outside for node i, P i loadcurt The amount of active load cut at node i.
3) Node cut load size constraint: the tangential load of the node is not greater than the load of the node, and P is not less than 0 i loadcurt ≤P i load 。
4) Capacity constraint of the transmission line:
Figure BDA0004031358840000061
in the method, in the process of the invention,
Figure BDA0004031358840000062
i Fthe upper and lower limits of the transmission capacity of the branches from node i to node j, respectively.
5) Unit output constraint:
Figure BDA0004031358840000063
wherein:
Figure BDA00040313588400000611
and->
Figure BDA0004031358840000064
The lower limit value and the upper limit value of the output force of the g-th distributed gas turbine are respectively.
6) Energy storage charge-discharge output constraint:
Figure BDA0004031358840000065
Figure BDA0004031358840000066
in the method, in the process of the invention,
Figure BDA0004031358840000067
and->
Figure BDA0004031358840000068
Charging power and discharging power of distributed energy storage respectively, +.>
Figure BDA00040313588400000612
And->
Figure BDA0004031358840000069
The upper limit of charging power and the upper limit of discharging power of the distributed energy storage are respectively.
7) Working range of the battery SOC:
Figure BDA00040313588400000610
in the method, in the process of the invention,SOC、
Figure BDA0004031358840000071
respectively the upper and lower limits of the stored charge state.
The step 6 specifically comprises the following steps:
and MATLAB, yalmip is used for calling the gurobi, a linear programming algorithm is used for calculation to obtain basic data, step carbon price parameters are set for simulation calculation based on the models of the step 2 and the step 3, and data information such as output, energy storage electric quantity, line tide, carbon emission, cost and the like of each unit in the virtual power plant is obtained based on step carbon price carbon transaction.
Compared with the prior art, the invention has the following advantages and effects:
1. the economic dispatching method considers the certainty of the conventional unit in the virtual power plant and the uncertainty of the new energy unit such as wind and light, and the like, and can adapt to the development of a novel power system.
2. The method takes the influence of carbon emission on the virtual power plant into consideration in the form of a carbon trading mechanism of carbon quota and stepped carbon price, and can adapt to low-carbon reform of an electric power market electric power system.
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FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a schematic diagram of a 39 test node in an embodiment of the invention;
FIG. 3 is a basic operation result diagram in an embodiment of the present invention;
FIG. 4 is a graph showing the comparison of wind speed, light, load demand and electricity price fluctuation in the embodiment of the invention.
Detailed Description
The present invention will be described in further detail by way of examples with reference to the accompanying drawings, which are illustrative of the present invention and not limited to the following examples.
Examples
As shown in FIG. 1, the virtual power plant economic dispatching method with wind and light storage considering the ladder carbon price comprises the following steps:
step 1: output prediction data of renewable energy sources such as wind power and photovoltaic and prediction data of the load of the virtual power plant for 24 hours are input, positive and negative rotation standby cost coefficients in the virtual power plant are determined by using a simulated market auction method, and rotation standby cost expressions of the wind power and the photovoltaic are respectively determined by Weibull distribution and Beta distribution. The operation cost of wind power photovoltaic consideration uncertainty is comprehensively obtained.
Step 2: and inputting the stepped carbon price parameters of the carbon trade market, and determining the carbon emission quota of the virtual power plant 24h by using a datum line method. And combining the two to obtain the cost of the virtual power plant in the carbon transaction process.
Step 3: based on the cost functions obtained in step 1 and step 2, in combination with other costs: such as conventional unit operating costs, battery operating costs, electricity buying and selling costs, electricity selling incomes for internal, etc., resulting in a total cost function for the virtual power plant.
Step 4: and establishing an economic dispatch model considering the ladder carbon price by combining the safe operation constraint, expressing the economic dispatch model by using MATLAB, yalmip language, and calling a solver gurobi to calculate so as to obtain a final economic dispatch result.
The present embodiment uses an IEEE 39 node distribution network system for testing, as shown in fig. 2. 9 distributed generator sets, 1 wind turbine set, 1 photovoltaic set, 2 distributed energy storage and 1 external interconnecting line. In this embodiment, simulation is performed on a MATLAB platform, and solution is performed by using a gurobi toolbox, so as to obtain a basic operation result of a specified carbon trade market, as shown in fig. 3. Fig. 4 is a view showing the wind speed, light, load demand and electricity price fluctuation in the present embodiment.
What is not described in detail in this specification is all that is known to those skilled in the art.
Although the present invention has been described with reference to the above embodiments, it should be understood that the invention is not limited to the embodiments described above, but is capable of modification and variation without departing from the spirit and scope of the present invention.

Claims (2)

1. The virtual power plant economic dispatching method containing wind and light storage considering the step carbon price is characterized by comprising the following steps of:
step 1, building a wind-electricity photovoltaic output model, collecting local wind-light historical data of a system, and predicting wind-light output by utilizing the historical data; fitting uncertainty of wind speed by using Weibull distribution, fitting uncertainty of illumination by using beta distribution, converting influence of uncertainty on VPP into cost, and establishing a cost model of wind-driven photovoltaic;
step 2, an energy storage model is built, the running state of energy storage is described by using the state of charge, so that the running model is obtained, and the running cost of the energy storage is expressed;
step 3, establishing a carbon quota, carbon emission and a carbon transaction model based on the stepped carbon price of the virtual power plant, and determining the carbon transaction cost of the virtual power plant;
step 4, determining an objective function, and obtaining a total cost function based on the wind power photovoltaic cost, the energy storage cost and the carbon transaction cost obtained in the step 1, the step 2 and the step 3, and the power generation cost load shedding cost and the power selling cost of other traditional units;
step 5, modeling a tide system, namely, based on the total cost function obtained in the step 4, taking the minimum cost as a target, combining unit line data of an actual system, determining constraint conditions according to system safe operation constraint, and establishing an optimal tide economic dispatch model of the virtual power plant;
and 6, calculating to obtain basic data by using a linear programming optimization algorithm, modifying the carbon transaction price level based on the models in the step 3 and the step 5, and performing simulation calculation by using MATLAB to obtain the output, line load flow, carbon emission and cost data information of each unit in the virtual power plant, which are obtained by the system under each carbon tax level.
2. The virtual power plant economic dispatch method with wind and light storage considering step carbon price according to claim 1, wherein the method is characterized in that:
the step 1 specifically comprises the following steps:
firstly, obtaining historical weather data of the land, carrying out frequency distribution statistics to obtain a light intensity and wind speed frequency distribution histogram, and respectively fitting by using beta distribution and Weibull distribution;
beta distribution:
Figure FDA0004031358830000011
wherein:
Figure FDA0004031358830000012
L max 、μ Beta 、σ Beta respectively representing the maximum deviation value, the average deviation value and the standard deviation value of solar irradiance;
two-parameter weibull distribution:
Figure FDA0004031358830000013
wherein: c is a scale parameter, k is a shape parameter, and v is a wind speed;
combining the output functions of the classical wind power and the photovoltaic unit to obtain probability density functions of the output of the classical wind power and the photovoltaic unit, wherein the probability density functions are respectively as follows:
Figure FDA0004031358830000021
Figure FDA0004031358830000022
wherein: w is the fan output, ρ is the air density, A is the wind turbine blade area; q (Q) PV Output of photovoltaic unit, Q PV(max) Is a photovoltaic deviceAn upper limit of the force;
the wind power and photovoltaic prediction overestimation cost and the carbon tax penalty cost are actually the rotating spare capacity cost of the system; when the actual wind speed or the radiation intensity does not reach the predicted value, the output force of the wind power plant and the photovoltaic power station is smaller than the planned value, and the increase of the generation of the thermal power unit is needed to meet the power balance of the power system, namely the positive standby cost; on the contrary, the thermal power unit is required to be reduced to meet the requirement, namely the negative standby cost is expressed as punishment caused by uncertainty;
according to the wind power photovoltaic probability density distribution function obtained in the step 1, the expected values of the positive standby capacity and the negative standby capacity are obtained:
Figure FDA0004031358830000023
Figure FDA0004031358830000024
C id ＝K id E(F cd )
C iu ＝K iu E(F cu )
Figure FDA0004031358830000025
wherein: f (F) cd 、F cu Respectively positive standby capacity and negative standby capacity, E is the expected value of the positive standby capacity and the negative standby capacity, w i Predicting force for wind power/photovoltaic, w r For rated wind/photovoltaic output, f w (w) is a wind power/photovoltaic probability density distribution function; c (C) id 、C iu Respectively the positive standby cost and the negative standby cost of the i units, C Pwind 、C Psun C is the cost of wind power and photovoltaic id(Pwind) 、C iu(Pwind) The positive standby capacity cost and the negative standby capacity cost of the fan unit i are respectively C jd(Psun) 、C ju(Psun) Respectively, photovoltaicPositive and negative spare capacity costs, K, of unit j id 、K iu Respectively positive standby cost coefficient and negative standby cost coefficient under the output of new energy, wherein NW and NP are respectively the number of units of a fan and a photovoltaic;
in addition, a method for simulating market auction is adopted to determine a positive standby cost coefficient and a negative standby cost coefficient under the output of new energy, namely the cost coefficient of the virtual power plant trading from the standby market; dividing the cost of maximum output power by the maximum output power to obtain the unit power generation cost coefficient K of the thermal power generating unit i ＝C i (p i,max )/p i,max From K i Starting a unit with the minimum value, sequentially inputting a load with the maximum power of the unit until the sum of the powers of the units is greater than the load, and adding K of the marginal units i Namely, positive standby cost coefficients under a certain load are determined; then from K i The largest unit starts, the load is put into the unit by the maximum power in turn until the sum of the power of the units is larger than the load, and the K of the marginal unit i Namely, determining the negative standby cost coefficient under a certain load;
the step 2 specifically comprises the following steps:
the amount of power stored by the energy storage system is measured by the state of charge and calculated using the following equation:
SOC(t)＝SOC(t-1)-E BESS (t-1)/R BESS
wherein SOC (t) is the state of charge of the t period, and SOC (t-1) is the state of charge of the t-1 period; e (E) BESS (t-1) is the total discharge amount of the storage battery at time t-1, R BESS The unit is kW.h for rated capacity of the storage battery;
the cost is expressed by the following formula:
Figure FDA0004031358830000031
wherein:
Figure FDA0004031358830000032
is the running cost of the ith battery energy storage, pi BESS Is a battery energy storage systemCost coefficient of consumption of system->
Figure FDA0004031358830000033
Figure FDA0004031358830000034
The discharging power and the charging power of the battery respectively;
the step 3 specifically comprises the following steps:
adopting a free initial carbon emission right distribution mode based on the generated energy, and distributing carbon emission limits to the virtual power plant as follows:
Figure FDA0004031358830000035
wherein: e (E) q Gratuitous carbon emission quota established for the virtual power plant operators for the regulatory authorities; η is a carbon emission allowance for producing unit electric power; p (P) b,t 、P gt,t The outsourcing electric quantity at the time t and the output value of the traditional unit are respectively;
the actual carbon emissions produced by the virtual power plant during the dispatch process are determined by the following equation:
Figure FDA0004031358830000036
wherein: e (E) p The carbon emission amount generated by the unit in the VPP is actually; a. b and c are actual carbon emission coefficients of the thermal power generating unit respectively;
the cost of carbon market trading based on step carbon prices is determined by the following formula:
Figure FDA0004031358830000041
wherein: c (C) carbon Carbon transaction fees to be paid for the VPP operator; mu is the reference price of the carbon emission market; l is the interval length of each carbon emission; alpha is the price increase of the ladder carbon tradeThe carbon trade price increases by alpha mu every time the carbon emission increases by one interval;
the step 4 specifically comprises the following steps:
the system objective function is the total cost of the virtual power plant, and comprises the power generation cost of a common unit, the use cost of energy storage, the wind photovoltaic pre-estimation underestimation cost, the controllable load reduction cost and the carbon transaction cost;
1) The power generation cost of the traditional unit is as follows:
Figure FDA0004031358830000042
Figure FDA0004031358830000043
wherein: c (C) gt Is the total cost of the conventional unit; NG represents the number of conventional units;
Figure FDA0004031358830000044
as a cost function of the generator set, the generator set output is related; p (P) gt,i,t The generating capacity of the conventional unit i in the t period is represented by MW; a, a i 、b i 、c i Respectively representing cost coefficients of the generators;
2) The energy storage cost is expressed by the following formula:
Figure FDA0004031358830000045
3) A corresponding cost is incurred while adjusting the controllable load, as represented by the following formula:
Figure FDA0004031358830000046
wherein: c (C) loadcurt Representing the operating cost of the controllable load; lambda (lambda) curt,t At tA compensation price for the engraving; p is p curt,t The load interruption quantity at the moment t;
4) The virtual power plant buys and sells electric power with the outside through the connecting line, and the generated cost is C market The method comprises the steps of carrying out a first treatment on the surface of the In addition, the virtual power plant supplies internal load demands to be able to obtain revenues S load ；
Figure FDA0004031358830000047
Figure FDA0004031358830000051
C vppprice ＝λC elecprice
Wherein: c (C) elecprice For market electricity price, C vppprice The internal electricity price of VPP; p (P) purchase For electric power purchased from outside, P load Load requirements inside the virtual power plant; the internal electricity price is considered to be in direct proportion to the market electricity price, and the coefficient is lambda;
5) Total cost:
minC objective ＝C gt +C Pwind +C Psun +C BESS +C carbon +C loadcurt +C market -S load
wherein C is gt C is the power generation cost of the traditional unit Pwind And C Psun Respectively overestimating cost and underestimating cost of wind power photovoltaics, C BESS For energy storage cost, C carbon For carbon trade cost, C loadcurt The load cutting cost is;
the step 5 specifically comprises the following steps:
considering the safety operation factor, the economic dispatch model operation requires the following constraints:
1) Direct current power flow constraint:
establishing an optimal power flow model for the system, in particular a direct current power flow model considering network loss; the specific direct current power flow formula is as follows:
P＝Bθ
wherein P is an active vector flowing into a node, theta is a node phase angle vector, and B is a negative number of an admittance matrix of a power grid node during normal continuous operation;
2) Power balance constraint:
P ij ＝P i -P j
P i ＝P i load +P i BESS -P i wind -P i sun -P i gt -P i market -P i loadcurt
wherein P is ij For active power flow of transmission line from node i to node j, P i For the active power of node i, P i load For the active load at node i, P i BESS For the active load of the stored energy output at node i, P i wind 、P i sun 、P i gt Respectively the active power of wind power, photovoltaic and power generation output by a conventional unit at a node i, and P i market Active load purchased from outside for node i, P i loadcurt The amount of active load cut down at node i;
3) Node cut load size constraint: the tangential load of the node is not greater than the load of the node, and P is not less than 0 i loadcurt ≤P i load ；
4) Capacity constraint of the transmission line:
Figure FDA0004031358830000052
in the method, in the process of the invention,
Figure FDA0004031358830000061
F i the upper limit and the lower limit of the transmission capacity of the branch from the node i to the node j are respectively;
5) Unit output constraint:
Figure FDA0004031358830000062
wherein:
Figure FDA0004031358830000063
and->
Figure FDA0004031358830000064
Respectively the lower limit value and the upper limit value of the output force of the g-th distributed gas turbine;
6) Energy storage charge-discharge output constraint:
Figure FDA0004031358830000065
Figure FDA0004031358830000066
/>
in the method, in the process of the invention,
Figure FDA0004031358830000067
and->
Figure FDA0004031358830000068
Charging power and discharging power of distributed energy storage respectively, +.>
Figure FDA0004031358830000069
And->
Figure FDA00040313588300000610
The upper limit of charging power and the upper limit of discharging power of the distributed energy storage are respectively;
7) Working range of the battery SOC:
Figure FDA00040313588300000611
in the method, in the process of the invention,SOCand
Figure FDA00040313588300000612
respectively an upper limit and a lower limit of the energy storage charge state;
the step 6 specifically comprises the following steps:
and MATLAB, yalmip is used for calling the gurobi to calculate and obtain basic data by using a linear programming algorithm, and step-by-step carbon price parameters are set to perform simulation calculation based on the models in the step 2 and the step 3 to obtain the data information of the output, the energy storage electric quantity, the line tide, the carbon emission and the cost of each unit in the virtual power plant based on step-by-step carbon price carbon transaction.
CN202211730432.9A 2022-12-30 2022-12-30 Virtual power plant economic dispatching method with wind-solar storage considering step carbon price Pending CN116187019A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117040027A (en) * 2023-09-28 2023-11-10 华北电力大学 Coordination optimization method and device for rural virtual power plant
CN118199181A (en) * 2024-05-10 2024-06-14 深圳市超业电力科技有限公司 Power distribution resource optimal configuration system based on intelligent power grid

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117040027A (en) * 2023-09-28 2023-11-10 华北电力大学 Coordination optimization method and device for rural virtual power plant
CN117040027B (en) * 2023-09-28 2024-01-16 华北电力大学 Coordination optimization method and device for rural virtual power plant
CN118199181A (en) * 2024-05-10 2024-06-14 深圳市超业电力科技有限公司 Power distribution resource optimal configuration system based on intelligent power grid

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