CN113792953B - Virtual power plant optimal scheduling method and system - Google Patents
Virtual power plant optimal scheduling method and system Download PDFInfo
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Abstract
The invention discloses a virtual power plant optimal scheduling method and a system, comprising the following steps: predicting the power scenes and the probability of wind power and photovoltaic power generation of the next day according to the wind speed, temperature and illumination radiation intensity historical information of the region where the renewable distributed energy source is located in the virtual power plant; counting the number of electric vehicles in the virtual power plant, the capacity of an energy storage system, the number of cogeneration units and the number of flexible loads which participate in excitation type demand response; inputting the data into a pre-constructed optimal scheduling model which meets the electric load requirement and the thermal load requirement of a user and aims at maximizing the benefit of the virtual power plant; and solving the optimal scheduling model to obtain an optimal scheduling scheme of the virtual power plant. The advantages are that: the flexible load is participated in the excitation type demand response of the virtual power plant, so that the peak clipping and valley filling effects of distributed energy sources, energy storage systems, electric vehicles and flexible loads in the virtual power plant are fully exerted, and the emission of pollutants of the thermoelectric interconnected unit is reduced.
Description
Technical Field
The invention relates to a virtual power plant optimal scheduling method and a virtual power plant optimal scheduling system, and belongs to the technical field of power system optimal scheduling.
Background
In face of environmental problems caused by conventional power generation, distributed power generation using solar energy, wind energy and fuel cells as energy sources has been gradually recognized as a reliable and clean power generation way meeting the future energy demands. Along with the gradual increase of the permeability of the new energy power generation in the power grid, the uncertainty and the fluctuation of the new energy power generation bring great interference to the power system, and the phenomenon of wind and light abandoning is serious due to the insufficient internal digestion capacity of the area. In 2019, the ubiquitous power internet of things construction concept is proposed by the national grid limited company, and the friendly grid connection level of the distributed new energy is improved by means of measures such as virtual power plant construction and multi-energy complementation, so that clean energy consumption is promoted.
The virtual power plant is characterized in that a generator set, an energy storage system, a distributed energy source and a controllable flexible load in a certain area are organically combined together, and the generator set, the energy storage system, the distributed energy source and the controllable flexible load are combined into a whole to participate in the operation of a power grid on the premise of meeting the power load in the area through advanced control, metering and communication technologies.
The virtual power plant comprises various distributed energy sources, energy storage systems, cogeneration units, electric vehicles and flexible loads in the region, and obtains profits by regulating and controlling the virtual power plant and performs economic compensation on the response loads meeting the excitation type requirements. Therefore, the method and the system balance unbalance between the distributed energy output and the load demand in the virtual power plant, integrate the distributed energy output, and actively participate in the excitation type demand response by the flexible load at the user side, so that the economic benefit of the virtual power plant is improved, the pollutant discharge amount of the virtual power plant is reduced, and the method and the system are important problems to be solved in the optimal scheduling of the virtual power plant.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a virtual power plant optimal scheduling method and a virtual power plant optimal scheduling system.
In order to solve the technical problems, the invention provides a virtual power plant optimal scheduling method, which comprises the following steps:
predicting the power scenes and the probability of wind power and photovoltaic power generation of the next day according to the wind speed, temperature and illumination radiation intensity historical information of the region where the renewable distributed energy source is located in the virtual power plant;
counting the number of electric vehicles in the virtual power plant, the capacity of an energy storage system, the number of cogeneration units and the number of flexible loads which participate in excitation type demand response;
inputting the next-day wind power, the power scenes and the probability generated by photovoltaic power generation, the number of electric vehicles in the virtual power plant, the capacity of an energy storage system, the number of cogeneration units and the number of flexible loads participating in excitation type demand response to a pre-constructed optimal scheduling model which meets the electric load demand and the heat load demand of a user and aims at maximizing the benefit of the virtual power plant;
and solving the optimal scheduling model to obtain the output condition of each power generation asset in the virtual power plant per hour, and determining an optimal scheduling scheme of the virtual power plant according to the output condition.
Further, the process for predicting the power scenes and the probability of the next day wind power and photovoltaic power generation according to the wind speed, the temperature and the illumination radiation intensity historical information of the area where the renewable distributed energy source in the virtual power plant is located comprises the following steps:
the log-likelihood function of the edge distribution function is established by,
wherein: l (delta) is a log-likelihood function, f (x) t Delta) is an edge probability density function, x t The wind speed/illumination intensity at the moment T is represented by delta as an edge probability density function parameter, and T is represented by total hours of a day;
obtaining f (x) by maximizing formula (1) t Delta) edge distribution functionAnd utilize->Solving the estimated parameter of the edge probability density function>
Obtaining Copula function estimation parameters by using a maximization formula (2)
Wherein: c is a Copula probability density function, θ represents Copula function parameters, and the estimated parameters are substituted into a Student-T Copula to generate a wind speed and illumination intensity scene set according to the obtained Copula function estimation parameters;
reducing the number of wind speed and illumination intensity scenes generated by a Student-T Copula by using a synchronous back generation technology to obtain a reduced wind speed and illumination intensity scene set;
obtaining predicted wind power and photovoltaic power output scenes of the next day according to the reduced wind speed and illumination scene set by using the following formula;
P PV,t ＝η PV S PV ε t
wherein: g WPP,t The power generated by the wind turbine at the time t is used as the power generated by the wind turbine; v t Predicted wind speed for time t; v in And v out The cut-in wind speed and the cut-out wind speed are; v r Is the rated wind speed; g r Is rated output power; p (P) PV,t The output of the photovoltaic generator set at the time t; η (eta) PV Is photovoltaic conversion efficiency; s is S PV Is a photovoltaic area; epsilon t To predict the intensity of photovoltaic radiation.
Further, the process of reducing the number of scenes by using the synchronous back-substitution technology comprises the following steps:
s1, setting a reduced wind speed and illumination scene set as C, setting the iteration times k=1, and setting the scene set of the iteration process as C k ＝C；
S2, determining probability distances of any two scenes in the reduced wind speed and illumination scene set, wherein a probability distance formula is as follows
Wherein: ║. ║ 2 Is a norm expression; i, j E C k And i not equal to j,corresponding to any two scenes, the corresponding probabilities are pi respectively i 、π j ；
S3, finding a scene closest to the scene i, and determining the minimum distance between the scene i and other scenes asThen find a scene matching this minimum distance J;
s4, multiplying the minimum distance of each scene by the probability corresponding to the minimum distance to obtain a scene matched with the minimum value determined in the last step, and then according toDetermining scene C 1 ,Z C1 ＝minZ i ,i∈C k ；
S5, eliminating scene C 1 At the same time scene C 1 The probability of transition to the closest probability, the remaining scenes are set to C k+1 ＝C k -C 1 ；
S6, recording the number of the residual scenes, if the number of the residual scenes meets the calculation requirement, continuing the next step, and if the number of the residual scenes does not meet the calculation requirement, returning to the step S2;
s7, reserving set C k+1 Is a function of the probability of each scene.
Further, the incentive type demand response flexible load comprises a transferable load and an interruptible load;
wherein the load transferable model is
Wherein:the load before and after the load transfer of the d-level load at the moment t is respectively; p (t, t ') is the load amount transferred from time t to time t';
when the transferable load at the user side participates in the demand response of the virtual power plant, the virtual power plant performs economic compensation on the transferable load, and the economic compensation cost expression of the transferable load is as follows
Wherein: sigma (sigma) 1 、σ 2 Basic compensation cost and user comfort compensation cost for transferable load compensation cost; t (T) max Is the maximum transition time interval; c (C) DR1 Total compensation cost for transferable loads;
interruptible load for direct reduction of total load, mathematical model of interruptible load and compensation cost are expressed as follows
Wherein:P′ load,d,t the total load before and after the d-level load is interrupted; c (C) DR2,d Compensating costs for interrupt loads; p (P) DR,d,t The load interruption quantity is the d-th level; />Compensating electricity price for the interruption quantity of the d-th level load; n is n d Representing the power load level.
Further, the expression of the optimized scheduling model is:
wherein: pi (ω) is the probability that scene ω occurs; w is the total field Jing Geshu; f is the net benefit of the virtual power plant;the method comprises the following steps of providing benefits for a virtual power plant to supply power for total load in a scene omega; />The method is the selling heat benefit of the virtual power plant in scene omega; />The power generation and heating cost of the cogeneration unit is reduced; />The virtual power plant is operated at the cost of environmental protection; />The running cost of the electric automobile is; />The operation cost of the energy storage system is; />The output cost of the electric boiler; />Penalty costs for deviating from the output plan;the load compensation cost is transferred and the load compensation cost is interrupted respectively;
constraints of the optimal scheduling model include: electric power balance constraint, thermal power balance constraint, distributed power supply output constraint, cogeneration system output and climbing constraint, energy storage system constraint, electric vehicle state of charge constraint and excitation type demand response constraint.
A virtual power plant optimization scheduling system, comprising:
the prediction module is used for predicting the power scenes and the probability of the next day wind power and photovoltaic power generation according to the wind speed, the temperature and the illumination radiation intensity historical information of the area where the renewable distributed energy source in the virtual power plant is located;
the statistics module is used for counting the number of electric vehicles in the virtual power plant, the capacity of the energy storage system, the number of cogeneration units and the number of flexible loads which participate in excitation type demand response;
the input module is used for inputting the power scenes and the probability generated by the next-day wind power and the photovoltaic power generation and the quantity of electric vehicles in the virtual power plant, the capacity of the energy storage system, the quantity of the cogeneration units and the quantity of the flexible loads which participate in excitation type demand response to a pre-constructed optimal scheduling model which meets the electric load demand and the heat load demand of a user and aims at maximizing the benefit of the virtual power plant;
and the solving module is used for solving the optimal scheduling model to obtain the output condition of each power generation asset per hour in the virtual power plant, and determining the optimal scheduling scheme of the virtual power plant according to the output condition.
Further, the prediction module includes:
a first calculation module for establishing a log-likelihood function of the edge distribution function by the following equation,
wherein: l (delta) is a log-likelihood function, f (x) t Delta) is an edge probability density function, x t The wind speed/illumination intensity at the moment T is represented by delta as an edge probability density function parameter, and T is represented by total hours of a day;
a second calculation module for obtaining f (x) by maximizing formula (1) t Delta) edge distribution functionAnd utilize->Solving the estimated parameter of the edge probability density function>
A third calculation module for obtaining Copula function estimation parameters by using the maximization type (2)
Wherein: c is a Copula probability density function, θ represents Copula function parameters, and the estimated parameters are substituted into a Student-T Copula to generate a wind speed and illumination intensity scene set according to the obtained Copula function estimation parameters;
the reduction module is used for reducing the number of wind speed and illumination intensity scenes generated by the Student-T Copula by utilizing a synchronous back generation technology to obtain a reduced wind speed and illumination intensity scene set;
the fourth calculation module is used for obtaining predicted wind power and photovoltaic power output scenes of the next day according to the reduced wind speed and illumination scene set by using the following formula;
P PV,t ＝η PV S PV ε t
wherein: g WPP,t The power generated by the wind turbine at the time t is used as the power generated by the wind turbine; v t Predicted wind speed for time t; v in And v out The cut-in wind speed and the cut-out wind speed are; v r Is the rated wind speed; g r Is rated output power; p (P) PV,t The output of the photovoltaic generator set at the time t; η (eta) PV Is photovoltaic conversion efficiency; s is S PV Is a photovoltaic area; epsilon t To predict the intensity of photovoltaic radiation.
Further, the reduction module includes:
the setting module is used for setting the reduced wind speed and illumination scene set as C, the iteration times k=1, and setting the scene set of the iteration process as C k ＝C；
The first determining module is used for determining the probability distance between any two scenes in the reduced wind speed and illumination scene set, and the probability distance formula is as follows
Wherein: ║. ║ 2 Is a norm expression; i, j E C k And i not equal to j,corresponding to any two scenes, the corresponding probabilities are pi respectively i 、π j ；
A second determining module for finding the scene closest to the scene i, and determining the minimum distance between the scene and other scenes asThen find a scene matching this minimum distance J;
a third determining module for multiplying the minimum distance of each scene by the probability corresponding to the minimum distance to obtain a scene matching the minimum value determined in the previous step, and then according toDetermining scene C 1 ,
An elimination module for eliminating scene C 1 At the same time scene C 1 The probability of transition to the closest probability, the remaining scenes are set to C k+1 ＝C k -C 1 ；
The recording module is used for recording the number of the residual scenes, if the number of the residual scenes meets the calculation requirement, continuing the next step, and if the number of the residual scenes does not meet the calculation requirement, re-determining the residual scenes through the first determining module, the second determining module, the third determining module and the eliminating module in sequence;
a reservation module for reserving the set C k+1 Is a function of the probability of each scene.
Further, the statistics module includes:
a transferable load calculation module for calculating a transferable load:
wherein:the load before and after the load transfer of the d-level load at the moment t is respectively; p (t, t ') is the load amount transferred from time t to time t';
when the transferable load at the user side participates in the demand response of the virtual power plant, the virtual power plant performs economic compensation on the transferable load, and the economic compensation cost expression of the transferable load is as follows
Wherein: sigma (sigma) 1 、σ 2 Basic compensation cost and user comfort compensation cost for transferable load compensation cost; t (T) max Is the maximum transition time interval; c (C) DR1 Total compensation cost for transferable loads;
a transferable load calculation module for calculating an interruptible load and a compensation cost for the interruptible load:
wherein:P′ load,d,t the total load before and after the d-level load is interrupted; c (C) DR2,d Compensating costs for interrupt loads; p (P) DR,d,t The load interruption quantity is the d-th level; />Compensating electricity price for the interruption quantity of the d-th level load; n is n d Representing the power load level.
Further, the expression of the optimized scheduling model is:
wherein: pi (ω) is the probability that scene ω occurs; w is the total field Jing Geshu; f is the net benefit of the virtual power plant;the method comprises the following steps of providing benefits for a virtual power plant to supply power for total load in a scene omega; />The method is the selling heat benefit of the virtual power plant in scene omega; />The power generation and heating cost of the cogeneration unit is reduced; />The virtual power plant is operated at the cost of environmental protection; />The running cost of the electric automobile is;the operation cost of the energy storage system is; />The output cost of the electric boiler; />Penalty costs for deviating from the output plan;the load compensation cost is transferred and the load compensation cost is interrupted respectively;
constraints of the optimal scheduling model include: electric power balance constraint, thermal power balance constraint, distributed power supply output constraint, cogeneration system output and climbing constraint, energy storage system constraint, electric vehicle state of charge constraint and excitation type demand response constraint.
The invention has the beneficial effects that:
according to the invention, the flexible load is participated in the excitation type demand response of the virtual power plant, so that the peak clipping and valley filling functions of the distributed energy sources, the energy storage system, the electric automobile and the flexible load in the virtual power plant are fully exerted, the load curve is gentle, the overall operation economy of the virtual power plant is improved, and the pollutant emission of the thermoelectric interconnected unit is reduced.
Drawings
FIG. 1 is a flow chart of a virtual power plant optimization scheduling method in an example of the invention;
FIG. 2 is an overall framework of a virtual power plant in accordance with an embodiment of the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for more clearly illustrating the technical aspects of the present invention, and are not intended to limit the scope of the present invention.
As shown in fig. 1-2, the invention provides a virtual power plant optimal scheduling method, and the implementation process comprises the following detailed steps:
step 1, predicting the power scenes and the probability of wind power and photovoltaic power generation of the next day according to the historical information of wind speed, temperature and illumination radiation intensity of a region where renewable distributed energy sources in a virtual power plant are located;
on the premise of knowing the historical wind speed and the illumination intensity, judging that the historical wind speed and the illumination intensity have the dependency of the upper end and the lower end at different moments;
the log-likelihood function of the edge distribution function is established by,
wherein: l (delta) is a log-likelihood function, f (x) t Delta) is an edge probability density function, x t For wind speed/illumination intensity at time T, δ is the edge probability density function parameter, and T is expressed as the total hours of the day.
By maximizing the above formula, f (x t ) Is a function of the edge distribution F (x t ) And can utilizeSolving the estimated parameter of the edge probability density function>
Obtaining Copula function estimated parameters by using the maximization formula
Wherein: c is a Copula probability density function, and θ represents a Copula function parameter. Substituting the obtained Copula function parameters into a Student-T Copula to generate a wind speed and illumination intensity scene set.
Obtaining wind power and photovoltaic power output scenes according to the wind speed and illumination scenes generated in the previous step by using the following formula;
P PV,t ＝η PV S PV ε t
wherein: g WPP,t Generating power for the wind generating set at a time t; v t Predicted wind speed for time t; v in And v out The cut-in wind speed and the cut-out wind speed are; v r Is the rated wind speed; g r Is rated output power; p (P) PV,t The output of the photovoltaic generator set at the time t; η (eta) PV Is photovoltaic conversion efficiency; s is S PV Is a photovoltaic area; epsilon is the predicted photovoltaic radiation intensity;
because the scene data volume is huge through the Student-T Copula function, the similarity among the scenes is high, and the calculation speed is influenced, so that the calculation speed is increased for effectively combining the near-field scenes, and the scene quantity is reduced by utilizing a synchronous back substitution technology.
Step 2, counting the number of electric vehicles, the capacity of an energy storage system, the number of cogeneration units and the number of flexible loads which participate in excitation type demand response in the virtual power plant;
in the virtual power plant, the electric automobile is used as a movable energy storage, not only can acquire electric energy from a power Grid by means of a charging pile, but also can transmit electric energy to the power Grid under an excitation mechanism, so that V2G (Vehicle-to-Grid) interaction with the power Grid is realized, and the effect of stabilizing the uncertainty and fluctuation of distributed power generation is achieved. The state of charge model of the electric automobile is expressed as
Wherein: SOC (State of Charge) t The state of charge of the electric automobile at the moment t; p (P) EV,t An equivalent output value of the electric automobile is larger than 0, and is smaller than 0, and is discharged, wherein 0 is equal to an off-grid state; c (C) EV The battery capacity of the electric automobile; lambda is the unit mileage power consumption of the electric automobile; l is the driving mileage of the electric automobile.
The flexible load comprises a transferable load and an interruptible load, both of which can participate in demand response scheduling, and transfer and interruption are carried out according to supply and demand conditions, so that the controllable degree is high.
The transferable load is a load whose usage period can be changed under the condition that the total load amount is ensured to be unchanged in the whole scheduling period. When the transferable load on the user side participates in the demand response, the virtual power plant needs to make economic compensation for the transferred load. The compensation cost is not only related to the size of the transferred load quantity, but also related to the time interval of load transfer, because the larger the load scheduling interval which can be transferred before and after optimization is, the more the electricity utilization comfort of a user is seriously affected. The transferable load model and compensation cost are expressed as follows:
in the method, in the process of the invention,the load before and after the load transfer of the d-level load at the moment t is respectively; p (t, t ') is the load amount transferred from time t to time t'; sigma (sigma) 1 、σ 2 Basic compensation cost and user comfort compensation cost for transferable load compensation cost; t (T) max Is the maximum transition time interval; c (C) DR1 The total compensation cost for the transferable load.
Unlike transferable loads, which do not change the total load, interruptible loads can directly reduce the total load. Mathematical model of interruptible load and compensation cost are respectively
In the method, in the process of the invention,P′ load，d，t respectively d-stage load interrupt negativeThe total load before and after loading; c (C) DR2,d Compensating costs for interrupt loads; p (P) DR,d,t The load interruption quantity is the d-th level; />Compensating electricity price for interruption of d-th stage load, n d Representing the power load level.
Step 3, on the premise of meeting the electric load demand and the thermal load demand of the user, establishing an optimization model with the aim of maximizing the benefits of the virtual power plant, and carrying out economic optimization scheduling of the virtual power plant;
3.1 virtual Power plant Integrated framework
The virtual power plant is provided with a cogeneration unit, wind power, photovoltaic, an energy storage system, an electric automobile and a bilateral transaction protocol is signed with electric automobile users, primary users and secondary users on the power generation side, as shown in figure 2, wherein the primary users consist of industrial and commercial loads with larger electric quantity demands, the electricity consumption peaks are relatively distributed and the electricity price is high, and the secondary users consist of resident loads with smaller electric quantity demands, and the electricity consumption peaks are concentrated and the electricity price is low. The primary users and the secondary users are composed of traditional loads and flexible loads, the traditional loads do not participate in virtual power plant scheduling and must be met preferentially, the flexible loads can participate in excitation type demand response, and the self power consumption demand is adjusted.
3.2 virtual Power plant scheduling objective function
The virtual power plant dispatching objective function is the maximum profit of the whole, the objective function is mainly divided into total income and operation cost, and the objective function is expressed as follows:
wherein: f is the net benefit of the virtual power plant; pi (ω) is the probability that scene ω occurs; w is the total field Jing Geshu;the method comprises the following steps of providing benefits for a virtual power plant to supply power for total load in a scene omega;/>the method is the selling heat benefit of the virtual power plant in scene omega; />The power generation and heating cost of the cogeneration unit is reduced; />The virtual power plant is operated at the cost of environmental protection; />The running cost of the electric automobile is;the operation cost of the energy storage system is; />The output cost of the electric boiler; />Penalty costs for deviating from the output plan; c (C) DR1 Total compensation cost for transferable loads; c (C) DR2 The cost is compensated for the interrupt load.
3.3 virtual Power plant scheduling constraints
(1) Electric power balance constraint
Wherein: p (P) CHP The electric power output of the cogeneration unit is obtained; p'. WPP 、P′ PV Wind power and photovoltaic power output are respectively; discharging and charging power of the electric automobile respectively; p (P) E1，dis 、P E1，chr Discharging and charging power of the energy storage system respectively; p'. load1 、P′ load2 The requirements of the primary and secondary user electric loads are respectively met; p (P) eb Power is consumed for the electric heat transfer equipment;
(2) Thermal equilibrium constraint
H CHP +H eb +H E2，dis ＝H load +H E2，chr
Wherein: h CHP The heat power output of the cogeneration unit is obtained; h eb Outputting thermal power for the electric heat transfer device; h E2，chr 、H E2，dis The heat storage tank is respectively charged and discharged with heat; h load Is a user thermal load requirement.
(3) Constraint of electric automobile
P EV,t |≤P EV,max
SOC min ≤SOC t ≤SOC max
SOC out,t ≥SOC demand
Wherein: p (P) EV,max The maximum charge and discharge power of the electric automobile; SOC (State of Charge) min 、SOC max The minimum and maximum charge states of the electric automobile are set; SOC (State of Charge) demand The state of charge is expected for the owner of the electric vehicle.
(4) Transferable load constraints
Transferable load constraint
Wherein:the maximum allowable load quantity is the t moment; />The maximum allowable load amount at time t.
Transferable load transfer time constraints
p(t,t′)＝0 t′∈T 1
p(t′,t)＝0 t∈T 2
Wherein: t (T) 1 、T 2 The transfer-out time is not allowed and the transfer-in time is not allowed, respectively.
The total load is invariable to restrain before and after transfer
(5) Interruptible load constraint
P DR,d,min,t ≤P DR,d,t ≤P DR,d,max,t
P DR,d,t-1 +P DR,d,t ≤P DR,d,max
Wherein: p (P) DR,d,min,t Is the minimum value of the interrupt load power; p (P) DR,d,max,t Maximum value of interruption load power; p (P) DR,d,max And the maximum response power of the controllable load in the d-th stage load continuous time is obtained.
And 4, solving the optimal scheduling model of the virtual power plant to obtain an optimal scheduling scheme of the virtual power plant.
The invention also provides a virtual power plant optimizing and scheduling system, which comprises the following steps:
the prediction module is used for predicting the power scenes and the probability of the next day wind power and photovoltaic power generation according to the wind speed, the temperature and the illumination radiation intensity historical information of the area where the renewable distributed energy source in the virtual power plant is located;
the statistics module is used for counting the number of electric vehicles in the virtual power plant, the capacity of the energy storage system, the number of cogeneration units and the number of flexible loads which participate in excitation type demand response;
the input module is used for inputting the power scenes and the probability generated by the next-day wind power and the photovoltaic power generation and the quantity of electric vehicles in the virtual power plant, the capacity of the energy storage system, the quantity of the cogeneration units and the quantity of the flexible loads which participate in excitation type demand response to a pre-constructed optimal scheduling model which meets the electric load demand and the heat load demand of a user and aims at maximizing the benefit of the virtual power plant;
and the solving module is used for solving the optimal scheduling model to obtain the output condition of each power generation asset per hour in the virtual power plant, and determining the optimal scheduling scheme of the virtual power plant according to the output condition.
Further, the prediction module includes:
a first calculation module for establishing a log-likelihood function of the edge distribution function by the following equation,
wherein: l (delta) is a log-likelihood function, f (x) t Delta) is an edge probability density function, x t The wind speed/illumination intensity at the moment T is represented by delta as an edge probability density function parameter, and T is represented by total hours of a day;
a second calculation module for obtaining f (x) by maximizing formula (1) t Delta) edge distribution functionAnd utilize->Solving the estimated parameter of the edge probability density function>
A third calculation module for obtaining Copula function estimation parameters by using the maximization type (2)
Wherein: c is a Copula probability density function, θ represents Copula function parameters, and the estimated parameters are substituted into a Student-T Copula to generate a wind speed and illumination intensity scene set according to the obtained Copula function estimation parameters;
the reduction module is used for reducing the number of wind speed and illumination intensity scenes generated by the Student-T Copula by utilizing a synchronous back generation technology to obtain a reduced wind speed and illumination intensity scene set;
the fourth calculation module is used for obtaining predicted wind power and photovoltaic power output scenes of the next day according to the reduced wind speed and illumination scene set by using the following formula;
P PV,t ＝η PV S PV ε t
wherein: g WPP,t The power generated by the wind turbine at the time t is used as the power generated by the wind turbine; v t Predicted wind speed for time t; v in And v out The cut-in wind speed and the cut-out wind speed are; v r Is the rated wind speed; g r Is rated output power; p (P) PV,t The output of the photovoltaic generator set at the time t; η (eta) PV Is photovoltaic conversion efficiency; s is S PV Is a photovoltaic area; epsilon t To predict the intensity of photovoltaic radiation.
Further, the reduction module includes:
the setting module is used for setting the reduced wind speed and illumination scene set as C, the iteration times k=1, and setting the scene set of the iteration process as C k ＝C；
The first determining module is used for determining the probability distance between any two scenes in the reduced wind speed and illumination scene set, and the probability distance formula is as follows
/>
Wherein: ║. ║ 2 Is a norm expression; i, j E C k And i not equal to j,corresponding to any two scenes, the corresponding probabilities are pi respectively i 、π j ；
A second determining module for finding the scene closest to the scene i, and determining the minimum distance between the scene and other scenes asThen find a scene matching this minimum distance J;
a third determining module for multiplying the minimum distance of each scene by the probability corresponding to the minimum distance to obtain a scene matching the minimum value determined in the previous step, and then according toDetermining scene C 1 ,
An elimination module for eliminating scene C 1 At the same time scene C 1 The probability of transition to the closest probability, the remaining scenes are set to C k+1 ＝C k -C 1 ；
The recording module is used for recording the number of the residual scenes, if the number of the residual scenes meets the calculation requirement, continuing the next step, and if the number of the residual scenes does not meet the calculation requirement, re-determining the residual scenes through the first determining module, the second determining module, the third determining module and the eliminating module in sequence;
a reservation module for reserving the set C k+1 Is a function of the probability of each scene.
Further, the statistics module includes:
a transferable load calculation module for calculating a transferable load:
wherein:the load before and after the load transfer of the d-level load at the moment t is respectively; p (t, t ') is the load amount transferred from time t to time t';
when the transferable load at the user side participates in the demand response of the virtual power plant, the virtual power plant performs economic compensation on the transferable load, and the economic compensation cost expression of the transferable load is as follows
Wherein: sigma (sigma) 1 、σ 2 Basic compensation cost and user comfort compensation cost for transferable load compensation cost; t (T) max Is the maximum transition time interval; c (C) DR1 Total compensation cost for transferable loads;
a transferable load calculation module for calculating an interruptible load and a compensation cost for the interruptible load:
wherein:P′ load,d,t the total load before and after the d-level load is interrupted; c (C) DR2,d Compensating costs for interrupt loads; p (P) DR,d,t The load interruption quantity is the d-th level; />Compensating electricity price for the interruption quantity of the d-th level load; n is n d Representing the power load level.
Further, the expression of the optimized scheduling model is:
/>
wherein: pi (ω) is the probability that scene ω occurs; w is the total field Jing Geshu; f is the net benefit of the virtual power plant;the method comprises the following steps of providing benefits for a virtual power plant to supply power for total load in a scene omega; />The method is the selling heat benefit of the virtual power plant in scene omega; />The power generation and heating cost of the cogeneration unit is reduced; />The virtual power plant is operated at the cost of environmental protection; />The running cost of the electric automobile is;the operation cost of the energy storage system is; />The output cost of the electric boiler; />Penalty costs for deviating from the output plan;respectively is turned toA moving load compensation cost and an interrupting load compensation cost;
constraints of the optimal scheduling model include: electric power balance constraint, thermal power balance constraint, distributed power supply output constraint, cogeneration system output and climbing constraint, energy storage system constraint, electric vehicle state of charge constraint and excitation type demand response constraint.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing is merely a preferred embodiment of the present invention, and it should be noted that modifications and variations could be made by those skilled in the art without departing from the technical principles of the present invention, and such modifications and variations should also be regarded as being within the scope of the invention.
Claims (2)
1. The virtual power plant optimal scheduling method is characterized by comprising the following steps of:
predicting the power scenes and the probability of wind power and photovoltaic power generation of the next day according to the wind speed, temperature and illumination radiation intensity historical information of the region where the renewable distributed energy source is located in the virtual power plant;
counting the number of electric vehicles in the virtual power plant, the capacity of an energy storage system, the number of cogeneration units and the number of flexible loads which participate in excitation type demand response;
inputting the next-day wind power, the power scenes and the probability generated by photovoltaic power generation, the number of electric vehicles in the virtual power plant, the capacity of an energy storage system, the number of cogeneration units and the number of flexible loads participating in excitation type demand response to a pre-constructed optimal scheduling model which meets the electric load demand and the heat load demand of a user and aims at maximizing the benefit of the virtual power plant;
solving the optimal scheduling model to obtain the output condition of each power generation asset in the virtual power plant in each hour, and determining an optimal scheduling scheme of the virtual power plant according to the output condition;
the process for predicting the power scenes and the probability of the next day wind power and photovoltaic power generation according to the wind speed, the temperature and the illumination radiation intensity historical information of the area where the renewable distributed energy source is located in the virtual power plant comprises the following steps:
the log-likelihood function of the edge distribution function is established by,
wherein: l (delta) is a log-likelihood function, f (x) t Delta) is an edge probability density function, x t The wind speed/illumination intensity at the moment T is represented by delta as an edge probability density function parameter, and T is represented by total hours of a day;
obtaining f (x) by maximizing formula (1) t Delta) edge distribution functionAnd utilize->Solving the estimated parameter of the edge probability density function>
Obtaining Copula function estimation parameters by using a maximization formula (2)
Wherein: c is a Copula probability density function, θ represents Copula function parameters, and the estimated parameters are substituted into a Student-T Copula to generate a wind speed and illumination intensity scene set according to the obtained Copula function estimation parameters;
reducing the number of wind speed and illumination intensity scenes generated by a Student-T Copula by using a synchronous back generation technology to obtain a reduced wind speed and illumination intensity scene set;
obtaining predicted wind power and photovoltaic power output scenes of the next day according to the reduced wind speed and illumination scene set by using the following formula;
P PV,t ＝η PV S PV ε t
wherein: g WPP,t The power generated by the wind turbine at the time t is used as the power generated by the wind turbine; v t Predicted wind speed for time t; v in And v out The cut-in wind speed and the cut-out wind speed are; v r Is the rated wind speed; g r Is rated output power; p (P) PV,t The output of the photovoltaic generator set at the time t; η (eta) PV Is photovoltaic conversion efficiency; s is S PV Is a photovoltaic area; epsilon t To predict the intensity of photovoltaic radiation;
the process for reducing the number of scenes by using the synchronous back-substitution technology comprises the following steps:
s1, setting a reduced wind speed and illumination scene set as C, setting the iteration times k=1, and setting the scene set of the iteration process as C k ＝C；
S2, determining probability distances of any two scenes in the reduced wind speed and illumination scene set, wherein a probability distance formula is as follows
Wherein: I.I 2 Is a norm expression; i, j E C k And i not equal to j,corresponding to any two scenes, the corresponding probabilities are pi respectively i 、π j ；
S3, finding a scene closest to the scene i, and determining the minimum distance between the scene i and other scenes asThen find a field matching this minimum distance JA scene;
s4, multiplying the minimum distance of each scene by the probability corresponding to the minimum distance to obtain a scene matched with the minimum value determined in the last step, and then according toDetermining scene C 1 ,/>
S5, eliminating scene C 1 At the same time scene C 1 The probability of transition to the closest probability, the remaining scenes are set to C k+1 ＝C k -C 1 ；
S6, recording the number of the residual scenes, if the number of the residual scenes meets the calculation requirement, continuing the next step, and if the number of the residual scenes does not meet the calculation requirement, returning to the step S2;
s7, reserving set C k+1 The corresponding probabilities of each scene of (a);
the incentive type demand response flexible load comprises a transferable load and an interruptible load;
wherein the load transferable model is
Wherein:the load before and after the load transfer of the d-level load at the moment t is respectively; p (t, t ') is the load amount transferred from time t to time t';
when the transferable load at the user side participates in the demand response of the virtual power plant, the virtual power plant performs economic compensation on the transferable load, and the economic compensation cost expression of the transferable load is as follows
Wherein: sigma (sigma) 1 、σ 2 Basic compensation cost and user comfort compensation cost for transferable load compensation cost; t (T) max Is the maximum transition time interval; c (C) DR1 Total compensation cost for transferable loads;
interruptible load for direct reduction of total load, mathematical model of interruptible load and compensation cost are expressed as follows
Wherein:P′ load,d,t the total load before and after the d-level load is interrupted; c (C) DR2,d Compensating costs for interrupt loads; p (P) DR,d,t The load interruption quantity is the d-th level; />Compensating electricity price for the interruption quantity of the d-th level load; n is n d Representing a power load level;
the expression of the optimized scheduling model is as follows:
wherein: pi (ω) is the probability that scene ω occurs; w is the total field Jing Geshu; f is the net benefit of the virtual power plant;in scene omega for virtual power plantThe return to power the total load; />The method is the selling heat benefit of the virtual power plant in scene omega; />The power generation and heating cost of the cogeneration unit is reduced; />The virtual power plant is operated at the cost of environmental protection; />The running cost of the electric automobile is; />The operation cost of the energy storage system is; />The output cost of the electric boiler; />Penalty costs for deviating from the output plan; />The load compensation cost is transferred and the load compensation cost is interrupted respectively;
constraints of the optimal scheduling model include: electric power balance constraint, thermal power balance constraint, distributed power supply output constraint, cogeneration system output and climbing constraint, energy storage system constraint, electric vehicle state of charge constraint and excitation type demand response constraint.
2. A virtual power plant optimization scheduling system, comprising:
the prediction module is used for predicting the power scenes and the probability of the next day wind power and photovoltaic power generation according to the wind speed, the temperature and the illumination radiation intensity historical information of the area where the renewable distributed energy source in the virtual power plant is located;
the statistics module is used for counting the number of electric vehicles in the virtual power plant, the capacity of the energy storage system, the number of cogeneration units and the number of flexible loads which participate in excitation type demand response;
the input module is used for inputting the power scenes and the probability generated by the next-day wind power and the photovoltaic power generation and the quantity of electric vehicles in the virtual power plant, the capacity of the energy storage system, the quantity of the cogeneration units and the quantity of the flexible loads which participate in excitation type demand response to a pre-constructed optimal scheduling model which meets the electric load demand and the heat load demand of a user and aims at maximizing the benefit of the virtual power plant;
the solving module is used for solving the optimal scheduling model to obtain the output condition of each power generation asset per hour in the virtual power plant, and determining an optimal scheduling scheme of the virtual power plant according to the output condition;
the prediction module includes:
a first calculation module for establishing a log-likelihood function of the edge distribution function by the following equation,
wherein: l (delta) is a log-likelihood function, f (x) t Delta) is an edge probability density function, x t The wind speed/illumination intensity at the moment T is represented by delta as an edge probability density function parameter, and T is represented by total hours of a day;
a second calculation module for obtaining f (x) by maximizing formula (1) t Delta) edge distribution functionAnd utilize->Finding edge probability densityFunction estimation parameter->
A third calculation module for obtaining Copula function estimation parameters by using the maximization type (2)
Wherein: c is a Copula probability density function, θ represents Copula function parameters, and the estimated parameters are substituted into a Student-T Copula to generate a wind speed and illumination intensity scene set according to the obtained Copula function estimation parameters;
the reduction module is used for reducing the number of wind speed and illumination intensity scenes generated by the Student-T Copula by utilizing a synchronous back generation technology to obtain a reduced wind speed and illumination intensity scene set;
the fourth calculation module is used for obtaining predicted wind power and photovoltaic power output scenes of the next day according to the reduced wind speed and illumination scene set by using the following formula;
P PV,t ＝η PV S PV ε t
wherein: g WPP,t The power generated by the wind turbine at the time t is used as the power generated by the wind turbine; v t Predicted wind speed for time t; v in And v out The cut-in wind speed and the cut-out wind speed are; v r Is the rated wind speed; g r Is rated output power; p (P) PV,t The output of the photovoltaic generator set at the time t; η (eta) PV Is photovoltaic conversion efficiency; s is S PV Is a photovoltaic area; epsilon t To predict the intensity of photovoltaic radiation;
the reduction module includes:
the setting module is used for setting the reduced wind speed and illumination scene set as C, the iteration times k=1, and setting the scene set of the iteration process as C k ＝C；
The first determining module is used for determining the probability distance between any two scenes in the reduced wind speed and illumination scene set, and the probability distance formula is as follows
Wherein: I.I 2 Is a norm expression; i, j E C k And i not equal to j,corresponding to any two scenes, the corresponding probabilities are pi respectively i 、π j ；
A second determining module for finding the scene closest to the scene i, and determining the minimum distance between the scene and other scenes asThen find a scene matching this minimum distance J;
a third determining module for multiplying the minimum distance of each scene by the probability corresponding to the minimum distance to obtain a scene matching the minimum value determined in the previous step, and then according toDetermining scene C 1 ,
An elimination module for eliminating scene C 1 At the same time scene C 1 The probability of transition to the closest probability, the remaining scenes are set to C k+1 ＝C k -C 1 ；
The recording module is used for recording the number of the residual scenes, if the number of the residual scenes meets the calculation requirement, continuing the next step, and if the number of the residual scenes does not meet the calculation requirement, re-determining the residual scenes through the first determining module, the second determining module, the third determining module and the eliminating module in sequence;
a reservation module for reserving the set C k+1 The corresponding probabilities of each scene of (a);
the statistics module comprises:
a transferable load calculation module for calculating a transferable load:
wherein:the load before and after the load transfer of the d-level load at the moment t is respectively; p (t, t ') is the load amount transferred from time t to time t';
when the transferable load at the user side participates in the demand response of the virtual power plant, the virtual power plant performs economic compensation on the transferable load, and the economic compensation cost expression of the transferable load is as follows
Wherein: sigma (sigma) 1 、σ 2 Basic compensation cost and user comfort compensation cost for transferable load compensation cost; t (T) max Is the maximum transition time interval; c (C) DR1 Total compensation cost for transferable loads;
a transferable load calculation module for calculating an interruptible load and a compensation cost for the interruptible load:
wherein:P′ load,d,t the total load before and after the d-level load is interrupted; c (C) DR2,d Compensating costs for interrupt loads; p (P) DR,d,t The load interruption quantity is the d-th level; />Compensating electricity price for the interruption quantity of the d-th level load; n is n d Representing a power load level;
the expression of the optimized scheduling model is as follows:
wherein: pi (ω) is the probability that scene ω occurs; w is the total field Jing Geshu; f is the net benefit of the virtual power plant;the method comprises the following steps of providing benefits for a virtual power plant to supply power for total load in a scene omega; />The method is the selling heat benefit of the virtual power plant in scene omega; />The power generation and heating cost of the cogeneration unit is reduced; />The virtual power plant is operated at the cost of environmental protection; />The running cost of the electric automobile is; />The operation cost of the energy storage system is; />The output cost of the electric boiler; />Penalty costs for deviating from the output plan; />The load compensation cost is transferred and the load compensation cost is interrupted respectively;
constraints of the optimal scheduling model include: electric power balance constraint, thermal power balance constraint, distributed power supply output constraint, cogeneration system output and climbing constraint, energy storage system constraint, electric vehicle state of charge constraint and excitation type demand response constraint.
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