CN117526454A - Virtual power plant operation management method, device and storage medium - Google Patents
Virtual power plant operation management method, device and storage medium Download PDFInfo
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Abstract
The invention provides a virtual power plant operation management method, equipment and a storage medium, which comprise the steps of obtaining power production information of flexible resources in each virtual power plant, constraint conditions corresponding to the flexible resources and attribute information of the virtual power plant, and respectively determining first adjustable power corresponding to the flexible resources and second adjustable power corresponding to the virtual power plant; based on the electricity purchase price of each virtual power plant from the power distribution network, the load demand predicted value corresponding to each virtual power plant and the unit output variable in each virtual power plant, constructing an independent operation model of each virtual power plant with the maximum income of each virtual power plant as a target; and according to the first adjustable power and the second adjustable power, determining an operation management optimization strategy corresponding to each virtual power plant by combining the independent operation model of each virtual power plant through a multi-objective two-stage optimization algorithm. The method can optimize the operation management strategy and realize the maximization of the operation benefit.
Description
Technical Field
The present invention relates to the field of power grid technologies, and in particular, to a virtual power plant operation management method, device, and storage medium.
Background
With the increase of the capacity of the non-water renewable energy source machine set represented by wind and light, the grid connection mode is changed from local grid connection to centralized and distributed grid connection in multiple areas, so that the uncertainty of a power source side and a load side is greatly increased, higher requirements are put forward on flexible resources in different time scales, and the power system is gradually changed into a renewable energy source power dominant multi-energy complementary power system. Considering that the power system directly manages the heterogeneous, scattered and diversified random power sources and flexible resource scheduling, the power system cannot bring higher economic benefits to both parties, and can generate a plurality of technical difficulties in the aspect of stable operation.
The patent with the application number of CN201610090840.0 discloses an intelligent management system for virtual power plant energy management and power transaction and an optimal operation method thereof, wherein a distributed energy unit is used as a terminal energy and information source device of the intelligent management system for the virtual power plant to form a distributed resource system, and the intelligent management system for the virtual power plant consists of an operation data acquisition and management module, an enterprise resource management module, a prediction module, a resource configuration module, an electricity selling plan optimization module, a transaction platform module, a real-time dispatching optimization module, a control dispatching execution module and a synchronous simulation function module. The invention adopts distributed energy information management mainly comprising distributed power generation and energy storage devices and flexible load equipment to realize the overall system architecture of all necessary functional modules such as prediction, resource combination management and configuration optimization, power supply and scheduling optimization, market transaction, real-time monitoring and scheduling and the like.
However, the method cannot bring higher economic benefits to the random power supply and the flexible resource, and can also generate a plurality of technical problems in the aspect of stable operation.
Disclosure of Invention
The embodiment of the invention provides a virtual power plant operation management method, equipment and a storage medium, which can at least solve part of problems in the prior art, namely that higher economic benefits cannot be brought to a random power supply and flexible resources, and a plurality of technical problems in the aspect of stable operation can be generated.
In a first aspect of an embodiment of the present invention,
provided is a virtual power plant operation management method, comprising:
acquiring power production information of each flexible resource in each virtual power plant, constraint conditions corresponding to each flexible resource and attribute information of the virtual power plant, and respectively determining first adjustable power corresponding to the flexible resource and second adjustable power corresponding to the virtual power plant;
based on the electricity purchase price of each virtual power plant from the power distribution network, the load demand predicted value corresponding to each virtual power plant and the unit output variable in each virtual power plant, constructing an independent operation model of each virtual power plant by taking the maximum income of each virtual power plant as a target;
and according to the first adjustable power and the second adjustable power, determining an operation management optimization strategy corresponding to each virtual power plant by combining the independent operation model of each virtual power plant through a multi-objective two-stage optimization algorithm.
In an alternative embodiment of the present invention,
determining a first adjustable power corresponding to the flexible resource includes:
determining a first adjustable power corresponding to the flexible resource according to the power constraint of the flexible resource, a power variable meeting the power constraint and a power utility function corresponding to the flexible resource;
determining a second adjustable power corresponding to the virtual power plant includes:
distributing a power constraint matrix and a power scaling factor for the first adjustable power, aggregating the first adjustable power, filtering power variables of the flexible resources after aggregation, and determining aggregation power information corresponding to the virtual power plant;
and determining second adjustable power corresponding to the virtual power plant based on the upper and lower power limits, the upper and lower climbing limits, the upper and lower electric energy limits, the first weight coefficient set corresponding to the upper and lower power limits, the second weight coefficient set corresponding to the upper and lower climbing limits and the third weight coefficient set corresponding to the upper and lower electric energy limits of the virtual power plant in combination with the aggregated power information.
In an alternative embodiment of the present invention,
determining a first adjustable power corresponding to the flexible resource as shown in the following formula:
；
；
wherein,representing a first adjustable power corresponding to the flexible resource set,R(X)representing flexible resource setsR (X)=[x 0 ,…,x i ]，NRepresenting the number of flexible resources that are available,H(R(x i ))represent the firstiThe power utility function corresponding to the flexible resource,P R( x i) (t)represent the firstiThe flexible resources are integrated intThe power variation of the moment in time,u d representing preference coefficients, < >>The time difference of the regulation and control is represented,Tindicates the regulation period>、/>Respectively represent the firstiThe flexible resources are integrated intMinimum power and maximum power at a moment;
and determining the aggregate power information corresponding to the virtual power plant as shown in the following formula:
；
wherein,P J which represents the aggregate power information,Arepresenting a power constraint matrix of the power class,s f representing a power scaling factor;
determining a second adjustable power corresponding to the virtual power plant as shown in the following formula:
；
wherein,representing a second level of adjustable power that is to be used,Tthe regulation period is represented by the number of the regulation periods,Nrepresenting the number of flexible resources that are available,P J representing aggregate power information, ">、/>Respectively represent the upper and lower power limits of the virtual power plant>、/>Respectively represent a first weight coefficient corresponding to the upper power limit and a first weight coefficient corresponding to the lower power limit in the first weight coefficient group,、/>respectively represent the upper limit and the lower limit of climbing corresponding to the virtual power plant, < ->、/>Respectively representing a second weight coefficient corresponding to the upper climbing limit and a second weight coefficient corresponding to the lower climbing limit in the second weight coefficient group, +.>、/>Respectively representing the upper limit and the lower limit of the electric energy corresponding to the virtual power plant>、/>And respectively representing a third weight coefficient corresponding to the upper power limit and a third weight coefficient corresponding to the lower power limit in the third weight coefficient group.
In an alternative embodiment of the present invention,
the constructing an independent operation model of each virtual power plant with the minimum scheduling cost of each virtual power plant as a target based on the electricity purchase price of each virtual power plant from the power distribution network, the load demand predicted value corresponding to each virtual power plant and the unit output variable in each virtual power plant comprises:
determining an independent operation model of each virtual power plant according to a method shown by the following formula:
；
wherein,Frepresenting the scheduling costs of each virtual power plant,I max representing the total number of operating time periods for each virtual power plant,representation oftThe time the purchase price of each virtual power plant from the distribution network,u p representing an electricity selling identifier->Representing a load demand forecast value corresponding to each virtual power plant,nILindicating the number of units involved in the demand response,pil j representation ofjAnd at the moment, the output variable of the unit in each virtual power plant, delta represents the electricity selling weight, and theta represents the load weight.
In an alternative embodiment of the present invention,
the method further includes determining a risk factor corresponding to the flexible resource:
determining risk factors of the flexible resources according to the risk adaptation function of the flexible resources, the risk prediction value of the flexible resources, standard deviation of the distribution obeyed by the risk prediction value and standard error value of a preset confidence interval by combining utility functions corresponding to the flexible resources;
the method for determining the risk factors of the flexible resources is shown as the following formula:
；
wherein,RISK X1 representing flexible resourcesX1Is a risk factor of (a) in the (c),F X1 representing flexible resourcesX1Is a function of the risk utility of (a),erepresenting the risk preference coefficient(s),P X1 representing the predicted value corresponding to the flexible resource,z X1 represents the standard deviation of the distribution to which the risk prediction value is subject,O X1 a standard error value representing a preset confidence interval,f(X1)representing risk adaptation functions for flexible resources.
In an alternative embodiment of the present invention,
the determining the operation management optimization strategy corresponding to each virtual power plant by combining the independent operation model of each virtual power plant through a multi-target two-stage optimization algorithm comprises the following steps:
constructing a first objective function according to the first adjustable power, the second adjustable power, the operation benefit of the virtual power plant and the operation cost of the virtual power plant, and taking the maximum operation benefit of the virtual power plant market as a target;
constructing a second objective function based on the user participation demand response, the first response quantity of the interruptible load and the second response quantity of the transferable load, and aiming at the highest satisfaction degree of the dispatching user of the virtual power plant;
and solving the first objective function and the second objective function through a robust iterative optimization algorithm in combination with a first constraint condition corresponding to the first objective function and a second constraint condition corresponding to the second objective function, and determining an operation management optimization strategy corresponding to the virtual power plant.
In an alternative embodiment of the present invention,
the constructing the first objective function includes:
；
；
；
wherein maxf1Indicating that the virtual power plant market operation revenue is maximized,R VPP representing the operating revenue of the virtual power plant,C VPP representing the running cost of the system;
representing revenue of a virtual power plant through electricity selling of the grid,/->Representing revenue generated by a virtual power plant through hot-net sales,/->Representing revenue obtained from the virtual power plant supplying power and heat to the user;
Tthe regulation period is represented by the number of the regulation periods,r gas,t representation oftThe price of the natural gas is changed at the moment,L CHP,t representation oftThe natural gas consumption of the machine set is measured at the moment,H gas represents the heat value of the natural gas,NJthe number of the machine sets is represented,represent the firsthThe operation and maintenance cost coefficient of the individual units,P h,t represents the h unittOutput power at time.
In an alternative embodiment of the present invention,
said constructing a second objective function comprises:
；
；
；
wherein,minf2indicating that the satisfaction of the scheduled users of the virtual power plant is highest,deviation penalty cost representing day-ahead scheduling curve and actual running curve, +.>Representing the wind abandoning punishment cost;
representing a positive bias penalty parameter, ">Representing a negative bias penalty parameter->Representing the deviation amount of a daily declaration curve and an actual operation curve;
represents the wind abandon penalty coefficient, < ->And represents the waste wind power.
In a second aspect of an embodiment of the present invention,
provided is a virtual power plant operation management apparatus including:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to invoke the instructions stored in the memory to perform the method described previously.
In a third aspect of an embodiment of the present invention,
there is provided a computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the method as described above.
The virtual power plant operation management method of the embodiment of the invention,
acquiring power production information of each flexible resource in each virtual power plant, constraint conditions corresponding to each flexible resource and attribute information of the virtual power plant, and respectively determining first adjustable power corresponding to the flexible resource and second adjustable power corresponding to the virtual power plant;
and according to the first adjustable power and the second adjustable power, determining an operation management optimization strategy corresponding to each virtual power plant by combining the independent operation model of each virtual power plant through a multi-objective two-stage optimization algorithm.
In the period of higher electricity price, the virtual power plant operators actively reduce the electricity purchasing power from the power grid and increase the output of the unit to sell electricity to the power grid. Meanwhile, operators increase electricity purchasing power in a period of low electricity prices. The virtual power plant plays a role in peak clipping and valley filling, and the running safety of the power grid is improved. In terms of thermal transactions, the virtual power plant operators choose to sell all the excess heat after meeting the internal heat load requirements and the upper limit of the capacity of the heat storage tank to increase their own market operating benefits.
In the real-time deviation optimization stage, a robust optimization model is adopted to describe uncertainty of wind power output, robustness of decision making of a virtual power plant operator is improved, a scheduling plan has certain risk resistance, when a wind power output deviation predicted value is large, output deviation and wind abandoning conditions are further reduced, and wind power consumption level is improved.
Drawings
FIG. 1 is a flow chart of a virtual power plant operation management method according to an embodiment of the invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The technical scheme of the invention is described in detail below by specific examples. The following embodiments may be combined with each other, and some embodiments may not be repeated for the same or similar concepts or processes.
FIG. 1 is a schematic flow chart of a virtual power plant operation management method according to an embodiment of the present invention, as shown in FIG. 1, the method includes:
s101, acquiring power production information of each flexible resource in each virtual power plant, constraint conditions corresponding to each flexible resource and attribute information of the virtual power plant, and respectively determining first adjustable power corresponding to the flexible resource and second adjustable power corresponding to the virtual power plant;
the virtual power plant (virtual power plant, VPP) has no specific constraints on the geographical location and operating characteristics of the distributed energy source, and provides a highly flexible and adaptable distributed energy management approach for the power system. Virtual power plants serve as bridges between a power grid management system and distributed energy sources, and the operating characteristics such as the operating state, the operating cost, the operating power and the like of the distributed energy sources need to be aggregated, and then participate in the power market or power grid dispatching together with the traditional power plants.
For example, the virtual power plant may be connected to a distribution grid dispatch control center via a distribution grid power network and a communication network. The virtual power plant mainly comprises a distributed photovoltaic power generation system, a storage battery system for energy storage, a local load and a virtual power plant control dispatching center. When the virtual power plant operates, interaction occurs with the power distribution network, electric energy generated by the distributed photovoltaic power generation system can be used for meeting local load, charging storage batteries and selling the storage batteries to obtain benefits for the power distribution network, meanwhile, the storage batteries are used for meeting the local load, and besides the storage batteries can be used for storing and releasing the electric energy through interaction with the power distribution network, and the local load can purchase electricity from the power distribution network in a peak period so as to meet requirements.
Various flexible resources of the present application may include storable energy sources, such as wind and solar energy; adjustable electric loads such as electric vehicles and energy recovery systems, energy storage systems, and the like; the power generation information of the present application may include at least one of power load, callable output, power balance, load demand response; the constraint conditions of each flexible resource in the application can comprise power balance constraint, energy storage system operation constraint, unit operation constraint, virtual power plant outsourcing power related constraint, load demand response and the like.
Virtual power plants serve as bridges between a power grid management system and distributed energy sources, and the operating characteristics such as the operating state, the operating cost, the operating power and the like of the distributed energy sources need to be aggregated, and then participate in the power market or power grid dispatching together with the traditional power plants.
The virtual power plant comprises more flexible resources with adjustable power, and the adjustable power of the virtual power plant is formed by comprehensively considering the information such as the running state, the running parameters, the marginal cost and the like of the flexible resources and the network information (such as a network topological structure, node voltage constraint, line tide about-to-about and the like) provided by the dispatching center.
In an alternative embodiment of the present invention,
determining a first adjustable power corresponding to the flexible resource includes:
determining a first adjustable power corresponding to the flexible resource according to the power constraint of the flexible resource, a power variable meeting the power constraint and a power utility function corresponding to the flexible resource;
in an alternative embodiment of the present invention,
determining a first adjustable power corresponding to the flexible resource as shown in the following formula:
；
；
wherein,representing a first adjustable power corresponding to the flexible resource set,R(X)representing flexible resource setsR (X)=[x 0 ,…,x i ]，NRepresenting the number of flexible resources that are available,H(R(x i ))represent the firstiThe power utility function corresponding to the flexible resource,P R( x i) (t)represent the firstiThe flexible resources are integrated intThe power variation of the moment in time,u d representing preference coefficients, < >>The time difference of the regulation and control is represented,Tindicates the regulation period>、/>Respectively represent the firstiThe flexible resources are integrated intMinimum power and maximum power at a moment;
the power utility function corresponding to the flexible resource is used for expressing the function of the quantity relation between the income obtained by each flexible resource from the virtual power plant and the output power of each flexible resource.
Determining a second adjustable power corresponding to the virtual power plant includes:
distributing a power constraint matrix and a power scaling factor for the first adjustable power, aggregating the first adjustable power, filtering power variables of the flexible resources after aggregation, and determining aggregation power information corresponding to the virtual power plant;
in an alternative embodiment of the present invention,
and determining the aggregate power information corresponding to the virtual power plant as shown in the following formula:
；
wherein,P J which represents the aggregate power information,Arepresenting a power constraint matrix of the power class,s f representing a power scaling factor;
if the power inequality constraint of the distributed energy source contains discrete variables, the calculation complexity of the adjustable power of the virtual power plant can be increased, and in order to simplify the subsequent solution of the second adjustable power information corresponding to the virtual power plant, the first adjustable power information can be aggregated, the power variables of the distributed energy source after aggregation can be filtered, and the aggregated power information corresponding to the virtual power plant can be determined.
And determining second adjustable power corresponding to the virtual power plant based on the upper and lower power limits, the upper and lower climbing limits, the upper and lower electric energy limits, the first weight coefficient set corresponding to the upper and lower power limits, the second weight coefficient set corresponding to the upper and lower climbing limits and the third weight coefficient set corresponding to the upper and lower electric energy limits of the virtual power plant in combination with the aggregated power information.
The distributed energy sources in the virtual power plant mainly comprise equivalent generators only comprising power constraint or climbing constraint or equivalent energy storage devices only comprising power constraint or electric energy constraint, and in order to simplify the calculation difficulty, the adjustable power information of the virtual power plant can be solved together through the equivalent generators and the equivalent energy storage devices.
Determining a second adjustable power corresponding to the virtual power plant as shown in the following formula:
；
wherein,representing a second level of adjustable power that is to be used,Tthe regulation period is represented by the number of the regulation periods,Nrepresentation flexibilityThe number of resources to be allocated to a resource,P J representing aggregate power information, ">、/>Respectively represent the upper and lower power limits of the virtual power plant>、/>Respectively represent a first weight coefficient corresponding to the upper power limit and a first weight coefficient corresponding to the lower power limit in the first weight coefficient group,、/>respectively represent the upper limit and the lower limit of climbing corresponding to the virtual power plant, < ->、/>Respectively representing a second weight coefficient corresponding to the upper climbing limit and a second weight coefficient corresponding to the lower climbing limit in the second weight coefficient group, +.>、/>Respectively representing the upper limit and the lower limit of the electric energy corresponding to the virtual power plant>、/>Respectively representing a third weight coefficient corresponding to the upper power limit and a third weight corresponding to the lower power limit in the third weight coefficient groupAnd (5) a weight coefficient.
By setting the corresponding weight coefficient sets for the upper and lower power limits, the upper and lower climbing limits and the upper and lower electric energy limits corresponding to the virtual power plant, the duty ratio of different constraint conditions of the virtual power plant in the power adjustment can be highlighted, and finally the accuracy of the operation management optimization strategy can be improved.
S102, constructing an independent operation model of each virtual power plant by taking the maximum income of each virtual power plant as a target based on the electricity purchase price of each virtual power plant from the power distribution network, the load demand predicted value corresponding to each virtual power plant and the unit output variable in each virtual power plant;
for example, the virtual power plant optimization may include various targets, for example, the targets of lowest electricity purchasing cost from the distribution network, lowest running risk of the virtual power plant, highest market benefit of the virtual power plant, and the like, and the selection of a specific optimization target may be selected according to actual use requirements, which is not limited in the embodiment of the present application. According to the embodiment of the application, the lowest electricity purchasing cost from the power distribution network is taken as a target, the lowest overall cost of operation management of the virtual power plant can be ensured, and the maximum market income is realized.
Respectively determining the output power of a power generation unit, the charge state of an energy storage unit and the power balance information inside the virtual power plant in the flexible resources according to the power production information of each flexible resource in each virtual power plant;
and solving the independent operation model by combining the independent operation model of each virtual power plant and taking the lowest electricity purchasing cost of each virtual power plant from the power distribution network as a target, and determining an operation management optimization strategy corresponding to each virtual power plant by combining constraint conditions corresponding to each flexible resource.
The virtual power plant can integrate and optimize the use of various resources under the comprehensive cooperation of a plurality of technologies, and the essence of the virtual power plant is a system for the coordinated cooperation and the decentralized use of resources and energy. The operation mode of the virtual power plant is quite similar to the mode of the power station, and the integrated utilization of the power resources relates to the operation effect of the virtual power plant. For example, a power generation schedule is formulated, an upper power generation limit is defined, the operation cost is controlled, and the like, and under the support of the functions, an independent virtual power plant can be contacted with a participant of electric power operation at any time, and can contribute to the operation of a power grid through direct contact with a communication center. On the basis of comprehensively integrating resource information, the whole virtual power plant can integrate the information together in a larger range, and comprehensively plan the utilization of resources, so that important support is provided for the stable operation of the whole virtual power plant. Thus, it is necessary to build a corresponding independent operational model for each virtual power plant.
In an alternative embodiment of the present invention,
the constructing an independent operation model of each virtual power plant with the minimum scheduling cost of each virtual power plant as a target based on the electricity purchase price of each virtual power plant from the power distribution network, the load demand predicted value corresponding to each virtual power plant and the unit output variable in each virtual power plant comprises:
determining an independent operation model of each virtual power plant according to a method shown by the following formula:
；
wherein,Frepresenting the scheduling costs of each virtual power plant,I max representing the total number of operating time periods for each virtual power plant,representation oftThe time the purchase price of each virtual power plant from the distribution network,u p representing an electricity selling identifier->Representing a load demand forecast value corresponding to each virtual power plant,nILindicating the number of units involved in the demand response,pil j representation ofjAnd at the moment, the output variable of the unit in each virtual power plant, delta represents the electricity selling weight, and theta represents the load weight.
In an alternative embodiment of the present invention,
the method further includes determining a risk factor corresponding to the flexible resource:
determining risk factors of the flexible resources according to the risk adaptation function of the flexible resources, the risk prediction value of the flexible resources, standard deviation of the distribution obeyed by the risk prediction value and standard error value of a preset confidence interval by combining utility functions corresponding to the flexible resources;
the method for determining the risk factors of the flexible resources is shown as the following formula:
；
wherein,RISK X1 representing flexible resourcesX1Is a risk factor of (a) in the (c),F X1 representing flexible resourcesX1Is a function of the risk utility of (a),erepresenting the risk preference coefficient(s),P X1 representing the predicted value corresponding to the flexible resource,z X1 represents the standard deviation of the distribution to which the risk prediction value is subject,O X1 a standard error value representing a preset confidence interval,f(X1)representing risk adaptation functions for flexible resources.
The risk adaptation function is used for indicating the tolerance of each flexible resource to faults; the risk utility function is used for indicating the relation function between the benefits obtained by each flexible resource from the virtual power plant and the virtual power plant fault risk caused by each flexible resource;
the virtual power plant is faced with various uncertain factors in the operation process, and the confidence level of the loss caused by the uncertain factors should not be uniformly regulated to be the same value, but should be selected in a self-adaptive manner according to the characteristics of each flexible resource. The wind power generation output and the light power generation output are greatly affected by environmental factors, and the error between the predicted value and the actual value is large, so that the risk factors of various flexible resources can be comprehensively considered, and the risk resistance of a virtual power plant operation management optimization strategy determined later can be improved by determining the risk factors of the flexible resources.
S103, according to the first adjustable power and the second adjustable power, determining an operation management optimization strategy corresponding to each virtual power plant through a multi-objective two-stage optimization algorithm and combining the independent operation model of each virtual power plant.
In the embodiment of the application, the power distribution network system comprises N virtual power plants, and each virtual power plant is connected with a power distribution network dispatching control center through a power distribution network and a communication network. Each virtual power plant mainly comprises a distributed photovoltaic power generation system, a storage battery system for energy storage, a local load and a virtual power plant control dispatching center. When the power distribution system is independently operated, each virtual power plant only interacts with the power distribution network, electric energy generated by the distributed photovoltaic power generation system can be used for meeting local load, charging the storage battery and selling the storage battery to obtain benefits for the power distribution network, meanwhile, the storage battery can be used for meeting the local load and also can be used for storing and releasing the electric energy by interacting with the power distribution network, and the local load can purchase electricity from the power distribution network in a peak period so as to meet requirements. When the virtual power plants operate cooperatively, the virtual power plants can interact with the power distribution network, and can also interact with each other by electric power, and the virtual power plants can utilize the photovoltaic power and the energy storage devices of other virtual power plants besides calling the distributed photovoltaic power and the energy storage system of the virtual power plants. Compared with the independent running state, each virtual power plant has more new energy photovoltaic power supply during cooperative running, and meanwhile, the storage of power can be realized by using the energy storage battery with larger capacity, so that the whole power distribution network system has greater flexibility during cooperative running, and the distributed photovoltaic power generation system and the energy storage equipment can be utilized more efficiently.
In an alternative embodiment of the present invention,
the determining the operation management optimization strategy corresponding to each virtual power plant by combining the independent operation model of each virtual power plant through a multi-target two-stage optimization algorithm comprises the following steps:
constructing a first objective function according to the first adjustable power, the second adjustable power, the operation benefit of the virtual power plant and the operation cost of the virtual power plant, and taking the maximum operation benefit of the virtual power plant market as a target;
in an alternative embodiment of the present invention,
the constructing the first objective function includes:
；
；
；
wherein maxf1Indicating that the virtual power plant market operation revenue is maximized,R VPP representing the operating revenue of the virtual power plant,C VPP representing the running cost of the system;
representing revenue of a virtual power plant through electricity selling of the grid,/->Representing revenue generated by a virtual power plant through hot-net sales,/->Representing revenue obtained from the virtual power plant supplying power and heat to the user;
Tthe regulation period is represented by the number of the regulation periods,r gas,t representation oftThe price of the natural gas is changed at the moment,L CHP,t representation oftThe natural gas consumption of the machine set is measured at the moment,H gas represents the heat value of the natural gas,NJthe number of the machine sets is represented,represent the firsthThe operation and maintenance cost coefficient of the individual units,P h,t represents the h unittOutput power at time.
Constructing a second objective function based on the user participation demand response, the first response quantity of the interruptible load and the second response quantity of the transferable load, and aiming at the highest satisfaction degree of the dispatching user of the virtual power plant;
in an alternative embodiment of the present invention,
said constructing a second objective function comprises:
；
；
；
wherein,minf2indicating that the satisfaction of the scheduled users of the virtual power plant is highest,deviation penalty cost representing day-ahead scheduling curve and actual running curve, +.>Representing the wind abandoning punishment cost;
representing a positive bias penalty parameter, ">Representing a negative bias penalty parameter->Representing the deviation amount of a daily declaration curve and an actual operation curve;
represents the wind abandon penalty coefficient, < ->And represents the waste wind power.
And solving the first objective function and the second objective function through a robust iterative optimization algorithm in combination with a first constraint condition corresponding to the first objective function and a second constraint condition corresponding to the second objective function, and determining an operation management optimization strategy corresponding to the virtual power plant.
Illustratively, the robust iterative optimization algorithm of the disclosed embodiments may include a non-linear constrained stochastic programming algorithm such as a scene tree algorithm. In practical applications, the operation management optimization strategy of the virtual power plant is a typical random programming problem containing nonlinear constraint, wherein the random variable is output power and electricity load of each flexible resource, and the nonlinear constraint condition can comprise independent operation condition of each virtual power plant. The robust iterative optimization algorithm of the present application may refer to an existing algorithm, and is not described herein in detail.
In a second aspect of an embodiment of the present invention,
provided is a virtual power plant operation management apparatus including:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to invoke the instructions stored in the memory to perform the method described previously.
In a third aspect of an embodiment of the present invention,
there is provided a computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the method as described above.
The present invention may be a method, apparatus, system, and/or computer program product. The computer program product may include a computer readable storage medium having computer readable program instructions embodied thereon for performing various aspects of the present invention.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.
Claims (10)
1. A virtual power plant operation management method, comprising:
acquiring power production information of each flexible resource in each virtual power plant, constraint conditions corresponding to each flexible resource and attribute information of the virtual power plant, and respectively determining first adjustable power corresponding to the flexible resource and second adjustable power corresponding to the virtual power plant;
based on the electricity purchase price of each virtual power plant from the power distribution network, the load demand predicted value corresponding to each virtual power plant and the unit output variable in each virtual power plant, constructing an independent operation model of each virtual power plant by taking the maximum income of each virtual power plant as a target;
and according to the first adjustable power and the second adjustable power, determining an operation management optimization strategy corresponding to each virtual power plant by combining the independent operation model of each virtual power plant through a multi-objective two-stage optimization algorithm.
2. The method of claim 1, wherein determining the first adjustable power for the flexible resource comprises:
determining a first adjustable power corresponding to the flexible resource according to the power constraint of the flexible resource, a power variable meeting the power constraint and a power utility function corresponding to the flexible resource;
determining a second adjustable power corresponding to the virtual power plant includes:
distributing a power constraint matrix and a power scaling factor for the first adjustable power, aggregating the first adjustable power, filtering power variables of the flexible resources after aggregation, and determining aggregation power information corresponding to the virtual power plant;
and determining second adjustable power corresponding to the virtual power plant based on the upper and lower power limits, the upper and lower climbing limits, the upper and lower electric energy limits, the first weight coefficient set corresponding to the upper and lower power limits, the second weight coefficient set corresponding to the upper and lower climbing limits and the third weight coefficient set corresponding to the upper and lower electric energy limits of the virtual power plant in combination with the aggregated power information.
3. The method of claim 2, wherein the step of determining the position of the substrate comprises,
determining a first adjustable power corresponding to the flexible resource as shown in the following formula:
；
；
wherein,representing a first adjustable power corresponding to the flexible resource set,R(X)representing flexible resource setsR(X)= [x 0 ,…,x i ]，NRepresenting the number of flexible resources that are available,H(R(x i ))represent the firstiThe power utility function corresponding to the flexible resource,P R ( x i) (t)represent the firstiThe flexible resources are integrated intThe power variation of the moment in time,u d representing preference coefficients, < >>The time difference of the regulation and control is represented,Tindicates the regulation period>、/>Respectively represent the firstiThe flexible resources are integrated intMinimum power and maximum time of dayA power;
and determining the aggregate power information corresponding to the virtual power plant as shown in the following formula:
；
wherein,P J which represents the aggregate power information,Arepresenting a power constraint matrix of the power class,s f representing a power scaling factor;
determining a second adjustable power corresponding to the virtual power plant as shown in the following formula:
；
wherein,representing a second level of adjustable power that is to be used,Tthe regulation period is represented by the number of the regulation periods,Nrepresenting the number of flexible resources that are available,P J representing aggregate power information, ">、/>Respectively represent the upper and lower power limits of the virtual power plant>、/>Respectively represent a first weight coefficient corresponding to the upper power limit and a first weight coefficient corresponding to the lower power limit in the first weight coefficient group,、/>respectively represent the upper limit and the lower limit of climbing corresponding to the virtual power plant, < ->、/>Respectively representing a second weight coefficient corresponding to the upper climbing limit and a second weight coefficient corresponding to the lower climbing limit in the second weight coefficient group, +.>、/>Respectively representing the upper limit and the lower limit of the electric energy corresponding to the virtual power plant>、/>And respectively representing a third weight coefficient corresponding to the upper power limit and a third weight coefficient corresponding to the lower power limit in the third weight coefficient group.
4. The method of claim 1, wherein constructing the independent operation model of each virtual power plant with the minimum scheduling cost of each virtual power plant as a target based on the purchase price of each virtual power plant from the power distribution network, the load demand predicted value corresponding to each virtual power plant, and the unit output variable in each virtual power plant comprises:
determining an independent operation model of each virtual power plant according to a method shown by the following formula:
；
wherein,Frepresenting the scheduling costs of each virtual power plant,I max representing the total number of operating time periods for each virtual power plant,representation oftThe time the purchase price of each virtual power plant from the distribution network,u p representing an electricity selling identifier->Representing a load demand forecast value corresponding to each virtual power plant,nILindicating the number of units involved in the demand response,pil j representation ofjAnd at the moment, the output variable of the unit in each virtual power plant, delta represents the electricity selling weight, and theta represents the load weight.
5. The method of claim 1, further comprising determining a risk factor for the flexible resource correspondence:
determining a risk factor of the flexible resource according to a risk adaptation function of the flexible resource, a risk prediction value of the flexible resource, a standard deviation of the distribution obeyed by the risk prediction value and a standard error value of a preset confidence interval, and combining a risk utility function corresponding to the flexible resource;
the method for determining the risk factors of the flexible resources is shown as the following formula:
；
wherein,RISK X1 representing flexible resourcesX1Is a risk factor of (a) in the (c),F X1 representing flexible resourcesX1Is a function of the risk utility of (a),erepresenting the risk preference coefficient(s),P X1 representing the predicted value corresponding to the flexible resource,z X1 represents the standard deviation of the distribution to which the risk prediction value is subject,O X1 a standard error value representing a preset confidence interval,f(X1)representing risk adaptation functions for flexible resources.
6. The method of claim 1, wherein the determining, by means of a multi-objective two-stage optimization algorithm, the operation management optimization strategy corresponding to each virtual power plant in combination with the independent operation model of the virtual power plant comprises:
constructing a first objective function according to the first adjustable power, the second adjustable power, the operation benefit of the virtual power plant and the operation cost of the virtual power plant, and taking the maximum operation benefit of the virtual power plant market as a target;
constructing a second objective function based on the user participation demand response, the first response quantity of the interruptible load and the second response quantity of the transferable load, and aiming at the highest satisfaction degree of the dispatching user of the virtual power plant;
and solving the first objective function and the second objective function through a robust iterative optimization algorithm in combination with a first constraint condition corresponding to the first objective function and a second constraint condition corresponding to the second objective function, and determining an operation management optimization strategy corresponding to the virtual power plant.
7. The method of claim 6, wherein constructing the first objective function comprises:
；
；
；
wherein maxf1Indicating that the virtual power plant market operation revenue is maximized,R VPP representing the operating revenue of the virtual power plant,C VPP representing the running cost of the system;
representing revenue of a virtual power plant through electricity selling of the grid,/->Representing revenue generated by a virtual power plant through hot-net sales,/->Representing revenue obtained from the virtual power plant supplying power and heat to the user;
Tthe regulation period is represented by the number of the regulation periods,r gas,t representation oftThe price of the natural gas is changed at the moment,L CHP,t representation oftThe natural gas consumption of the machine set is measured at the moment,H gas represents the heat value of the natural gas,NJthe number of the machine sets is represented,represent the firsthThe operation and maintenance cost coefficient of the individual units,P h,t represents the h unittOutput power at time.
8. The method of claim 6, wherein constructing the second objective function comprises:
；
；
；
wherein,minf2indicating that the satisfaction of the scheduled users of the virtual power plant is highest,representing the day-ahead scheduling curve and actual operationBias penalty cost of curve,/->Representing the wind abandoning punishment cost;
representing a positive bias penalty parameter, ">Representing a negative bias penalty parameter->Representing the deviation amount of a daily declaration curve and an actual operation curve;
represents the wind abandon penalty coefficient, < ->And represents the waste wind power.
9. A virtual power plant operation management apparatus, comprising:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to invoke the instructions stored in the memory to perform the method of any of claims 1 to 8.
10. A computer readable storage medium having stored thereon computer program instructions, which when executed by a processor, implement the method of any of claims 1 to 8.
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