CN111915125A - Multi-type resource optimal combination method and system for virtual power plant - Google Patents
Multi-type resource optimal combination method and system for virtual power plant Download PDFInfo
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Abstract
The invention provides a virtual power plant multi-type resource optimal combination method and a system, comprising the steps of constructing an interactive framework of a multi-party main body in the operation of a virtual power plant; establishing a resource regulation characteristic model of virtual power plant interaction resources, and establishing a resource optimization combination model of the virtual power plant considering power distribution network blocking management; the branch power flow constraint linearization is realized, sensitivity parameters are introduced, and the relation between the branch transmission power change and the virtual power plant regulation power is described in a linearization manner; according to the regulation and control authority of the virtual power plant to the resources, firstly, the combination mode of directly controlling the resources by the virtual power plant is determined according to the resource optimization combination model, and then the combination mode of the resources controlled by the distributed energy users is determined. The resource optimization combination method and the resource optimization combination system provide blocking management service for the power distribution network and can overcome distributed energy heterogeneity.
Description
Technical Field
The invention relates to the technical field of power distribution network operation, in particular to a virtual power plant multi-type resource optimal combination method and system.
Background
With the continuous increase of the occupation ratio of energy storage, renewable energy and controllable load in a distribution network, the utilization of the dispersed and flexible resources to provide services for a power grid and the full play of the regulation and control potential of the resources become an important direction for the construction of the current smart power grid. The resources which are distributed on the user side and can be interacted and managed are collectively called as distributed energy resources, the distributed energy resources have great difference in all subjects, interaction willingness and technical characteristics, and the direct management of the power grid on the distributed energy resources is difficult. At present, the management of distributed energy sources mainly depends on an active power distribution network technology and a micro-grid technology. However, the active power distribution system has certain mandatory performance on the management of DER, so that the active power distribution system is more suitable for providing short-term and temporary services for the power distribution network by utilizing the flexibility of distributed energy; the micro-grid has certain requirements on the composition and functions of internal distributed energy, the source, load and storage need to meet the balance constraint of the system and the off-grid stable operation requirement, and the distributed energy with dispersed geography and heterogeneous structure is difficult to self-organize to form the micro-grid.
In a virtual power plant with multiple energy sources participating in interaction, the first problem involved is the problem of resource combination. The virtual power plant and the distributed energy are mutually selected: the distributed energy sources are independently selected whether to participate in the interaction of the virtual power plant, and the virtual power plant selects which distributed energy sources can participate in the interaction. Because the state (generated energy, load demand or operation cost) of the distributed energy is in a change, the composition of the distributed energy in the virtual power plant is dynamic, the virtual power plant selects proper distributed energy to participate in interaction to form an alliance before a scheduling period begins, the virtual power plant has certain regulation authority on members in the alliance in the scheduling period, and the alliance is released after the scheduling period is finished. The improper distributed energy combination mode can not satisfy the constraint of the power distribution network, and meanwhile, the income can not be obtained to satisfy the benefit demand of the members of the alliance.
Disclosure of Invention
In view of the above problems, the present invention provides a method and a system for optimally combining multiple types of resources of a virtual power plant, which provide blocking management services to a power distribution network and can overcome the heterogeneity of distributed energy resources.
According to one aspect of the invention, a virtual power plant multi-type resource optimal combination method is provided, which comprises the following steps
Constructing an interactive framework of a multi-party main body in the operation of a virtual power plant, wherein in the interactive framework, tasks born by each main body in the operation process are determined according to the regulating capacity and the requirement of each main body involved in the operation process, and the main bodies are distributed energy owners, power markets and power distribution system operators;
establishing a resource regulation characteristic model of the interactive resources of the virtual power plant, and establishing a resource regulation characteristic model which can participate in the regulation of the virtual power plant according to the agreement of the virtual power plant and a distributed energy source owner, so as to preliminarily determine the regulation capacity of the virtual power plant;
establishing a resource optimization combination model of a virtual power plant considering power distribution network blocking management, combining the regulation capacity of the virtual power plant with a regulation target, and establishing a resource optimization combination model meeting the requirements of each main body;
the branch power flow constraint linearization is realized, sensitivity parameters are introduced, and the relation between the branch transmission power change and the virtual power plant regulation power is described in a linearization manner;
according to the regulation and control authority of the virtual power plant on the resources, firstly, the combination mode of directly controlling the resources by the virtual power plant is determined according to the resource optimization combination model, and then the combination mode of the resources controlled by the distributed energy resource owner is determined.
Preferably, the step of constructing an interactive framework of the multi-party agent in the operation of the virtual power plant comprises:
the virtual power plant interacts with distributed energy, and resources are divided into three types according to regulation authority: the first kind of resources are observable and uncontrollable, and distributed energy owners submit power demand predictions of the second day to the virtual power plant in the day ahead; the second type of resources can be observed, the running state of the equipment is directly controlled by the virtual power plant, and distributed energy resource owners submit power demand prediction of the second day to the virtual power plant in the day ahead and agree with the virtual power plant to participate in regulation time, regulation mode and regulation power; the third type of resources can be observed, the running state of the equipment is controlled by a distributed energy resource owner, and the distributed energy resource owner provides the power demand prediction and the power adjustable range of the second day for the virtual power plant; the virtual power plant directly controls the equipment running state of the second type of resources according to the regulation demand, and issues the regulation power required by the second day resources to the third type of resources;
the virtual power plant interacts with the power market, the virtual power plant submits a bidding power curve to the power market before, and the power market clears and issues bidding results to the virtual power plant;
the virtual power plant interacts with a power distribution system operator, the virtual power plant predicts and aggregates distributed resources according to the output of the distributed resources to form baseline power, the virtual power plant submits the baseline power to the power distribution system operator, and the power distribution system operator is assisted to predict load distribution in the whole distribution network area; and the distribution system operator issues a distribution network load distribution prediction result to the virtual power plant.
Further, preferably, the step of constructing the resource adjustment characteristic model of the virtual power plant interaction resource includes:
according to the regulation authority of the virtual power plant to the resources and the adjustable characteristic of the resources, the second type of resources change the running state of the equipment according to the regulation target through the following formula,
wherein, Δ PtValue of regulation power, P, provided for resources of the second type at time ttFor the output power value in actual operation after the second type of resource is adjusted at the time t,
the predicted value of the output power at the time t when the second type of resources are not regulated and controlled;
according to the regulation authority of the virtual power plant to the resources and the adjustable characteristic of the resources, the third class of resources provides the second class of resources with the regulation power within the following formula range,
Plb,t≤ΔPt≤Pub,t
wherein, Plb,t、Pub,tThe lower and upper limits of the power may be adjusted for the second type of resource, respectively.
In addition, preferably, the step of constructing the resource adjustment characteristic model of the virtual power plant interaction resource further includes:
the distributed energy comprises an electric automobile and an energy storage, and the distributed energy owner comprises an electric automobile user and an energy storage user;
for the electric automobile user, appointing an adjustable time interval of the electric automobile, changing the charging track of the electric automobile by slowing down the charging power of the electric automobile in an allowable charging power range by a virtual power plant on the premise of ensuring that the accumulated charging amount in the adjustment period before and after adjustment does not change,
et+1＝et+Pev,t
wherein e istIs the cumulative amount of charge at time t, Pev,tFor the charging power at the time t,
in order to allow the maximum charging power,
is a predicted value of charging power, delta P, at time t when not chargedev,tAdjustment value for charging power provided at time t, etbIs the accumulated charge amount at the end of the adjustment time;
for the energy storage users, the participation regulation time period and the adjustable power range are appointed, the charging power of the energy storage is regulated by keeping the charging state of the energy storage within the healthy range on the premise of ensuring that the energy storage electric quantity is not changed after a regulation and control period,
-PN≤Pess,t≤PN
et+1＝et+Pess,t
SOCt＝SOC0+et/CN
wherein, PNRated charging power for energy storage;
and Pess,tRespectively representing the charging power of the stored energy at t moments before and after adjustment; SOCtRepresenting the state of charge of energy storage at time t; SOCtRepresenting the state of charge of energy storage at time t; cNRepresenting the capacity of stored energy;
andSOCand the upper limit and the lower limit of the SOC allowed in the energy storage regulation process are shown.
In addition, preferably, the step of constructing the resource adjustment characteristic model of the virtual power plant interaction resource further includes:
the distributed energy resource includes controllable loads including industrially controllable loads and commercially controllable loads, the distributed energy resource owner including industrially controllable load users and commercially controllable load users;
the method comprises the following steps that load requirements of an industrial controllable load user and a commercial controllable load user on the second day are reported to a virtual power plant operator every day and are reported as an uncontrollable load and a controllable load, the virtual power plant issues an expected regulation target to the industrial controllable load user and the commercial controllable load user according to controllable load regulation characteristics and an expected regulation requirement, and the regulation power of the controllable load when the controllable load participates in regulation is as follows:
Plb,ind,t≤ΔPind,t≤Pub,ind,t
Plb,com,t≤ΔPcom,t≤Pub,com,t
wherein, Δ Pind,tRegulated power available for industrial controllable loads; plb,ind,tAnd Pub,ind,tThe lower limit and the upper limit of the adjustable power of the industrial controllable load are respectively; delta Pcom,tRegulated power available to a user of the commercially controllable load; plb,com,tAnd Pub,com,tThe lower and upper limits of the power can be adjusted for the commercially controllable load, respectively.
Preferably, the step of establishing a resource optimization combination model of the virtual power plant considering the blocking management of the power distribution network comprises: selecting a resource optimization combination mode participating in virtual power plant regulation with the aim of minimizing regulation cost,
wherein, i is the resource type, n is the distribution network node number, and represents the resource access distribution network position, CostiThe adjustment cost coefficient of the resource i is the electric power market punishment cost suffered by unit electric quantity deviation; Δ t denotes the adjustment interval period, λ is the access cost, P, required for a single resource to participate in the adjustmentVPP,tRepresenting the actual operating power, P, of the virtual power plant at the time t after regulationaim,tRepresents the adjusted expected operating power of the virtual power plant and the virtual power plant at the moment t of the power market,
and
and for decision variables, jointly indicate that the resource i at the node n participates in the regulation state,
is an integer variable from 0 to 1, and is,
indicating that the resource is selected to participate in the adjustment combination,
indicating the regulated power provided by the resource at time t.
Further, preferably, the step of establishing a resource optimization combination model of the virtual power plant considering the blocking management of the power distribution network further includes: analyzing the regulation capacity of the virtual power plant from two scales of space and time respectively, comprising:
on a spatial scale, the regulation capability of the virtual power plant at node n is shown as follows:
wherein, Δ Pn,tRepresenting the regulated power which can be provided by the virtual power plant after aggregation at the node n;
Pn,tadjusting active injection power of the front and rear distribution network nodes n for the virtual power plant respectively; pub,n,tAnd Plb,n,tRespectively representing an upper limit and a lower limit of the regulated power which can be provided by the node n;
on a time scale, the regulation capacity of the virtual power plant at time t is shown as follows:
wherein, Pub,tAnd Plb,tRespectively represents the upper limit and the lower limit of the available regulating power at the time t after the virtual power plant aggregation,
representing the actual operating power of the virtual power plant before regulation and control;
and
respectively representing the upper limit and the lower limit of the available regulating power of the resource i at the node n at the time t;
the regulatory requirements of the virtual power plant on a time scale meet the external performance constraints imposed on participating in the power market,
|PVPP,t-Paim,t|≤er
wherein er is the allowed power deviation agreed by the virtual power plant and the power market;
the auxiliary power distribution network of the virtual power plant carries out blocking management, and after the virtual power plant is regulated, the transmission power of each branch of the power distribution network is constrained as follows:
-Pmn,N≤Pmn,t≤Pmn,N
wherein the content of the first and second substances,
the transmission power of the branch m-n when not regulated is obtained, and m and n are respectively serial numbers of connecting nodes at two ends of the branch;
respectively representing the magnitude and phase of the voltage at node m when unregulated; gmn、bmnRespectively representing admittance values of the branches m-n; pmn,tAdjusting the m-n transmission power of the rear branch; pmn,,NThe maximum transmission power allowed for the branch; delta Pmn,tThe amount of change in the transmission power for the branch.
Preferably, the step of linearizing the branch flow constraint comprises:
the nonlinear constraint of the branch power flow is linearized by introducing sensitivity parameters,
wherein, Δ Pmn,tRepresenting the change in transmission power, Δ P, of branch mnn,tRepresents the regulated power, alpha, available after the virtual power plant aggregation at node nP,mn,n,tAnd alphaQ,mn,n,tRespectively representing the sensitivity of the change of the active injection power and the reactive injection power of the node N to the change of the transmission power of the branch mn, wherein N represents the number of nodes with interactive resource access of a virtual power plant capable of providing regulation capacity in the range of a distribution network, and delta Qn,tThere is an active injected power change for node n.
Further, preferably, the step of linearizing the branch flow constraint includes:
the nonlinear constraint of the branch power flow is linearized by introducing sensitivity parameters,
wherein the content of the first and second substances,
is the power factor angle of node n.
According to another aspect of the present invention, there is provided a virtual power plant multi-type resource optimization combination system, including:
the system comprises a first construction module, a second construction module and a third construction module, wherein the first construction module is used for constructing an interactive framework of a plurality of main bodies in the operation of the virtual power plant, in the interactive framework, tasks born by each main body in the operation process are determined according to the adjusting capacity and the requirements of each main body involved in the operation process, and the main bodies are distributed energy owners, power markets and power distribution systems;
the second construction module is used for constructing a resource regulation characteristic model of the interactive resources of the virtual power plant, establishing a resource regulation characteristic model which can participate in the regulation of the virtual power plant according to the agreement of the virtual power plant and a distributed energy source owner, and preliminarily determining the regulation capacity of the virtual power plant;
the third building module is used for building a resource optimization combination model of the virtual power plant considering the blocking management of the power distribution network, combining the adjusting capacity of the virtual power plant with an adjusting target and building a resource optimization combination model meeting the requirements of each main body;
the constraint linearization module is used for carrying out constraint linearization on branch power flow, introducing sensitivity parameters and describing the relationship between the change of the branch transmission power and the regulated power of the virtual power plant in a linearization manner;
and the resource optimization combination module determines a combination mode of directly controlling resources by the virtual power plant according to the regulation and control authority of the virtual power plant on the resources and determines a combination mode of the resources controlled by the distributed energy owner according to the resource optimization combination model.
The resource optimal combination method and the resource optimal combination system for the virtual power plant to provide blocking management service for the power distribution network can overcome the heterogeneity of distributed energy resources, and have strong applicability in the conventional power system. The distributed energy resources are managed by adopting a virtual power plant interaction framework, and under the premise of not changing a distributed energy grid-connected mode, various distributed energy resources can be aggregated by advanced technologies such as control, metering and communication, so that the distributed energy resources with dispersed geographic positions and different structures can be operated in a coordinated mode.
The method and the system for optimizing and combining the multiple types of resources of the virtual power plant can enable the virtual power plant to participate in the power market to obtain more profits when providing blocking management auxiliary service for the distribution network, and improve the stability of the alliance structure.
Drawings
FIG. 1 is a flow chart of a method for optimizing and combining multiple types of resources of a virtual power plant according to the present invention;
FIG. 2 is a schematic diagram illustrating the interaction process between multiple parties involved in the present invention;
FIG. 3 is a schematic diagram of a topological structure of a simulation reference distribution network system for verifying the validity of the proposed model; .
FIG. 4 is a flow chart of the combination and regulated power solution;
FIG. 5 is a schematic of the external characteristics of a virtual power plant aggregate;
fig. 6 shows the distribution of the transmission power of the distribution network branches before regulation;
FIG. 7 illustrates virtual plant turndown and turndown targets for selected combinations;
FIG. 8 is a virtual power plant resource adjustment power distribution;
fig. 9 shows the distribution network branch transmission power distribution after regulation and control;
FIG. 10 is a schematic diagram of a block diagram of a multi-type resource optimization and combination system of a virtual power plant according to the present invention.
Detailed Description
In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of one or more embodiments. It may be evident, however, that such embodiment(s) may be practiced without these specific details. In other instances, well-known structures and devices are shown in block diagram form in order to facilitate describing one or more embodiments.
Various embodiments according to the present invention will be described in detail below with reference to the accompanying drawings.
Fig. 1 is a flowchart of the optimal combination method for multiple types of resources in a virtual power plant according to the present invention, and as shown in fig. 1, the optimal combination method for multiple types of resources in a virtual power plant includes:
step S1, constructing an interactive framework of a multi-party main body in the operation of the virtual power plant, wherein in the interactive framework, tasks born by each main body in the operation process are determined according to the adjusting capability and the requirement of each main body involved in the operation process, and the main bodies are distributed energy resource owners, power markets and power distribution system operators;
s2, constructing a resource regulation characteristic model of the virtual power plant interaction resources, and establishing a resource regulation characteristic model capable of participating in virtual power plant regulation according to the agreement of the virtual power plant and a distributed energy source owner, so as to preliminarily determine the regulation capacity of the virtual power plant;
step S3, establishing a resource optimization combination model of the virtual power plant considering the blocking management of the power distribution network, combining the adjusting capacity of the virtual power plant with an adjusting target, and establishing a resource optimization combination model meeting the requirements of each main body;
step S4, carrying out branch flow constraint linearization, introducing sensitivity parameters, and describing the relationship between branch transmission power change and virtual power plant regulation power in a linearization manner;
and step S5, according to the regulation and control authority of the virtual power plant to the resources, determining a combination mode of directly controlling the resources by the virtual power plant according to the resource optimization combination model, and then determining a combination mode of the resources controlled by the distributed energy resource owner.
In step S1, the method includes:
the virtual power plant is interactive with the distributed energy, and the virtual power plant and a distributed energy owner sign an agreement to determine the regulation authority of the virtual power plant on interactive resources. Dividing the resources into three categories according to the regulation authority: the first kind of resources are observable and uncontrollable, and distributed energy owners submit power demand predictions of the second day to the virtual power plant in the day ahead; the second type of resources can be observed, the running state of equipment is directly controlled by the virtual power plant, and distributed energy resource owners submit power demand forecast of the second day to the virtual power plant in the day ahead and agree with the virtual power plant to participate in regulation time, regulation mode and regulation power/electric quantity requirements; the third type of resources can be observed, the running state of the equipment is controlled by a distributed energy resource owner, and the distributed energy resource owner provides the power demand prediction and the power adjustable range of the second day for the virtual power plant; the virtual power plant directly controls the equipment running state of the second type of resources according to the regulation demand, and issues the regulation power required by the second day resources to the third type of resources;
the virtual power plant interacts with the power market, and the virtual power plant aggregates distributed energy to form a main body with flexible and adjustable capacity to participate in bidding of the power market. In the interaction process, the virtual power plant submits a bidding power curve to a forward power market, and the power market clears and issues bidding results to the virtual power plant;
the virtual power plant interacts with a power distribution system operator, and the virtual power plant forms baseline power according to output prediction aggregation of distributed resources. In the interaction process, the virtual power plant submits baseline power to a power distribution system operator, and the power distribution system operator is assisted to predict load distribution in the whole distribution network area; and the distribution system operator issues a distribution network load distribution prediction result to the virtual power plant.
In step S2, a resource regulation characteristic model is established according to the agreement between the virtual power plant and the distributed energy users, including: according to the regulation authority of the virtual power plant to the resources and the adjustable characteristic of the resources, the second type of resources change the running state of the equipment according to the regulation target through the following formula, and the regulation power value provided by the second type of resources is shown as the following formula:
wherein, Δ PtValue of regulation power, P, provided for resources of the second type at time ttFor the output power value in actual operation after the second type of resource is adjusted at the time t,
the predicted value of the output power at the time t when the second type of resources are not regulated and controlled;
according to the regulation authority of the virtual power plant to the resources and the adjustable characteristic of the resources, the third class of resources provides the second class of resources with the regulation power within the following formula range,
Plb,t≤ΔPt≤Pub,t
wherein, Plb,t、Pub,tThe lower and upper limits of the power may be adjusted for the second type of resource, respectively.
In one embodiment, the distributed energy resource includes electric vehicles and energy storage, and the distributed energy resource users (one of distributed energy resource owners, distributed energy resource providing regulation capability) include electric vehicle users and energy storage users;
for the electric automobile user, appointing an adjustable time interval of the electric automobile, changing the charging track of the electric automobile by slowing down the charging power of the electric automobile in an allowable charging power range by a virtual power plant on the premise of ensuring that the accumulated charging amount in the adjustment period before and after adjustment does not change,
et+1＝et+Pev,t
wherein e istIs the cumulative amount of charge at time t, Pev,tFor the charging power at the time t,
in order to allow the maximum charging power,
is a predicted value of charging power, delta P, at time t when not chargedev,tAdjustment value for charging power provided at time t, etbIs the accumulated charge amount at the end of the adjustment time;
for the energy storage users, the participation regulation time period and the adjustable power range are appointed, the charging power of the energy storage is regulated by keeping the charging state of the energy storage within the healthy range on the premise of ensuring that the energy storage electric quantity is not changed after a regulation and control period,
-PN≤Pess,t≤PN
et+1＝et+Pess,t
SOCt＝SOC0+et/CN
wherein, PNRated charging power for energy storage;
and Pess,tRespectively representing the charging power of the stored energy at t moments before and after adjustment; SOCtRepresenting the state of charge of energy storage at time t; SOC0Is the state of charge for initial energy storage; cNRepresenting the capacity of stored energy;
andSOCand the upper limit and the lower limit of the SOC allowed in the energy storage regulation process are shown.
In one embodiment, the distributed energy resource includes controllable loads including industrially controllable loads and commercially controllable loads, and the distributed energy resource users include industrially controllable load users and commercially controllable load users;
the method comprises the following steps that load requirements of an industrial controllable load user and a commercial controllable load user on the second day are reported to a virtual power plant operator every day and are reported as an uncontrollable load and a controllable load, the virtual power plant issues an expected regulation target to the industrial controllable load user and the commercial controllable load user according to controllable load regulation characteristics and an expected regulation requirement, and the regulation power of the controllable load when the controllable load participates in regulation is as follows:
Plb,ind,t≤ΔPind,t≤Pub,ind,t
Plb,com,t≤ΔPcom,t≤Pub,com,t
wherein, Δ Pind,tRegulated power available for industrial controllable loads; plb,ind,tAnd Pub,ind,tThe lower limit and the upper limit of the adjustable power of the industrial controllable load are respectively; delta Pcom,tRegulated power available to a user of the commercially controllable load; plb,com,tAnd Pub,com,tThe lower and upper limits of the power can be adjusted for the commercially controllable load, respectively.
In step S3, a resource optimization combination model of the virtual power plant considering distribution network blocking management is established, and a resource optimization combination mode participating in virtual power plant regulation is selected with the objective of minimizing regulation cost to minimize the influence on the power demand of the user, where the objective function is shown as follows:
wherein, i is the resource type, n is the distribution network node number, and represents the resource access distribution network position, CostiThe adjustment cost coefficient of the resource i is the electric power market punishment cost suffered by unit electric quantity deviation; Δ t denotes the adjustment interval period, λ is the access cost, P, required for a single resource to participate in the adjustmentVPP,tRepresenting the actual operating power, P, of the virtual power plant at the time t after regulationaim,tRepresents the adjusted expected operating power of the virtual power plant and the virtual power plant at the moment t of the power market,
and
and for decision variables, jointly indicate that the resource i at the node n participates in the regulation state,
is an integer variable from 0 to 1, and is,
indicating that the resource is selected to participate in the adjustment combination,
indicating the regulated power provided by the resource at time t.
In one embodiment, analyzing the turndown capability of the virtual power plant from two dimensions, space and time, respectively, includes:
on a spatial scale, the regulation capability of the virtual power plant at node n is shown as follows:
wherein, Δ Pn,tRepresenting the regulated power which can be provided by the virtual power plant after aggregation at the node n;
Pn,tadjusting active injection power of the front and rear distribution network nodes n for the virtual power plant respectively; pub,n,tAnd Plb,n,tRespectively representing an upper limit and a lower limit of the regulated power which can be provided by the node n;
on a time scale, the regulation capacity of the virtual power plant at time t is shown as follows:
wherein, Pub,tAnd Plb,tRespectively represents the upper limit and the lower limit of the available regulating power at the time t after the virtual power plant aggregation,
representing the actual operating power of the virtual power plant before regulation and control;
and
respectively representing the upper limit and the lower limit of the available regulating power of the resource i at the node n at the time t;
the regulation requirement of the virtual power plant on the time scale meets the external characteristic constraint of participating in the power market, and the virtual power plant and the power market agree on Paim,tThen, P should be made at every time interval as much as possibleVPP,tAnd Paim,tThe virtual power plant has a certain regulation demand on a time scale to meet the external characteristic constraint on participating in the power market, and the power market allows the actual power of the virtual power plant and engagementHas a certain deviation, and is restricted as follows
|PVPP,t-Paim,t|≤er
Wherein er is the allowed power deviation agreed by the virtual power plant and the power market;
the virtual power plant assists the distribution network to block management, and after the virtual power plant is adjusted, each branch transmission power of the distribution network is constrained as follows, that is to say, after the virtual power plant is adjusted, each branch transmission power of the distribution network is not out of limit:
-Pmn,N≤Pmn,t≤Pmn,N
wherein the content of the first and second substances,
the transmission power of the branch m-n when not regulated is obtained, and m and n are respectively serial numbers of connecting nodes at two ends of the branch;
respectively representing the magnitude and phase of the voltage at node m when unregulated; gmn、bmnRespectively representing admittance values of the branches m-n; pmn,tAdjusting the m-n transmission power of the rear branch; pmn,,NThe maximum transmission power allowed for the branch; delta Pmn,tThe amount of change in the transmission power for the branch.
In step S4, the branch power flow constraint linearization is performed, and the essential of the virtual power plant auxiliary power distribution system operator for blocking management is to adjust the operating state of the controllable resource and change the active injection power of the distribution network node where the resource is located, so as to change the power flow distribution of the distribution network and alleviate the branch blocking, including:
introducing nonlinear constraint of sensitivity parameter linearization branch power flowNonlinear constraint, Δ P, of incoming sensitivity parameter, α, linearizing branch power flowmn,tBy Δ Pn,tAnd node reactive injection power change (Δ Q)n,t) Jointly determining, the relationship between the branch transmission power change amount and the virtual power plant regulation power is shown as the following formula:
wherein, Δ Pmn,tRepresenting the change in transmission power, Δ P, of branch mnn,tRepresents the regulated power, alpha, available after the virtual power plant aggregation at node nP,mn,n,tAnd alphaQ,mn,n,tThe sensitivity of the change of the active injection power and the reactive injection power of the node N to the change of the transmission power of the branch mn is respectively represented, and N represents the number of nodes which have virtual power plant interaction resources capable of providing adjusting capacity and are accessed in the distribution network range.
Preferably, due to Δ Pn,tAccount for
The proportion is small, and the power factor angle of the nodes before and after adjustment is considered
The active injection power change quantity and the reactive injection power change quantity of the node are governed as follows:
therefore, the relationship between the branch transmission power change amount and the virtual power plant regulation power is as follows:
wherein the content of the first and second substances,
is the power factor angle of node n.
In step S5, the resource optimization combination model is solved to obtain a resource optimization combination scheme, the resource optimization combination problem is divided into two parts, and according to the control authority of the virtual power plant on the resources, the combination mode in which the resources are directly controlled by the virtual power plant is determined according to the optimization model, and then the combination mode in which the resources are controlled by the distributed energy users is determined, so as to finally obtain the resource optimization combination scheme.
In a preferred embodiment of the present invention, as shown in fig. 2, the distributed energy sources interacting with the virtual power plant include photovoltaic, electric vehicle, energy storage and controllable load, wherein photovoltaic is a first kind of resource, which is not controllable; the electric automobile and the stored energy are second-class resources, the operation state of the equipment is directly controlled by the virtual power plant; the controllable load is a third resource, which is specifically divided into an industrial controllable load and a commercial controllable load, and the operation state of the equipment is controlled by a user (one of distributed energy owners). And the virtual power plant operator aggregates the distributed photovoltaic power prediction curve, the electric vehicle charging prediction track, the energy storage charging and discharging track and the power demand prediction value of the controllable load user to form a virtual power plant power baseline.
Obtaining the aggregate baseline power of the virtual power plant at each node by combining the position of the resource, submitting the aggregate baseline power to a distribution network system operator, and issuing a load distribution prediction result of each node of the distribution network to the virtual power plant by the distribution system operator
Aggregating the base line power of the virtual power plants of each node to form a total power base line of the virtual power plants on a time scale
And will be
Submitting the competitive bidding power to the electric power market in the day before to participate in competitive bidding, issuing competitive bidding results to the virtual power plant after the electric power market is cleared, and taking the competitive bidding results issued by the electric power market as target work of the virtual power plantRate Paim,t)。
According to the regulation authority of the virtual power plant on the resources and the adjustable characteristic of the resources, the virtual power plant has no regulation authority on the distributed photovoltaic users, and the distributed photovoltaic resources do not provide regulation power.
According to the regulation authority of the virtual power plant on the resources and the adjustable characteristic of the resources, the virtual power plant can directly control the running states of the electric automobile and the energy storage equipment according to the regulation requirement under the condition that the virtual power plant meets the agreement (the regulation time period, the regulation mode and the regulation power/electric quantity requirement) with the user.
For the user of the electric automobile, appointing the adjustable time interval t of the electric automobilea,tb]Maximum charging power is allowed to be
And the virtual power plant changes the charging track of the electric automobile by slowing down the charging power of the electric automobile within the allowable charging power range, but ensures that the accumulated charging amount (e) in the adjusting period before and after adjustment is not changed. At this time, the electric vehicle regulation characteristic may be established as:
et+1＝et+Pev,t
the energy storage is similar to the electric automobile, and is appointed to participate in the regulation time period and the adjustable power range. During adjustment, the power of the stored energy can be changed, and the power direction can be changed. The energy storage running state is also influenced by the state of charge (SOC) of the equipment, the SOC is related to the charging track of the energy storage and the capacity of the battery, and the performance of the energy storage equipment is influenced when the equipment continuously runs in an over-low/over-high SOC state, so that the SOC of the energy storage in the adjusting process is always kept in a healthy range. In order to ensure the flexible adjustment capability of the energy storage equipment, the energy storage electric quantity does not change after a complete adjustment and control period. The energy storage regulation characteristic may be established as:
-PN≤Pess,t≤PN
et+1＝et+Pess,t
SOCt＝SOC0+et/CN
in the formula, PNRated charging power for energy storage;
and Pess,tRespectively representing the charging power of the stored energy at t moments before and after adjustment; e.g. of the typetCumulative amount of charge, SOC, for storing energy at time ttRepresenting the state of charge of energy storage at time t; cNRepresenting the capacity of stored energy;
andSOCand the upper limit and the lower limit of the SOC allowed in the energy storage regulation process are shown.
According to the regulation authority of the virtual power plant to the resources and the regulation characteristic of the resources, the industrial controllable load users and the commercial controllable load users report the load requirements of the next day to the virtual power plant operators every day, the load requirements are specifically divided into two parts of uncontrollable loads and controllable loads to be reported, the virtual power plant issues expected regulation targets to the industrial and commercial load users according to the controllable load regulation characteristic and in combination with the expected regulation requirements, and the control mode of specific equipment is determined by the users. The regulation power that the controllable load can provide when participating in the regulation is as follows:
Plb,ind,t≤ΔPind,t≤Pub,ind,t
Plb,com,t≤ΔPcom,t≤Pub,com,t
in the formula,. DELTA.Pind,tRegulated power available to industrial loads; plb,ind,tAnd Pub,ind,tThe lower limit and the upper limit of the adjustable power of the industrial load are respectively; delta Pcom,tRegulated power available to commercial loads; plb,com,tAnd Pub,com,tThe lower and upper limits of the power can be adjusted for the commercial load, respectively.
In an embodiment of the present invention, as shown in fig. 3, the distribution network system is divided into town area Feeder1 and country area Feeder2, and the switches S1, S2 and S3 are closed. When the output of renewable energy (photovoltaic power station) is not considered, the maximum load of the distribution network is 11.57MW + j3.78Mvar. Electric vehicle charging stations and commercial loads are mostly and intensively distributed in urban central areas, and photovoltaic power stations and industrial loads are mostly distributed in rural areas. The controllable load is composed of an industrial controllable load and a commercial controllable load, the industrial controllable load provides adjusting capacity for a VPP (Virtual Power Plant) in a mode of load reduction, wherein the reducible amount of the industrial load accounts for about 20% of the total amount of the industrial load, and the reducible load of 400kW can be provided at maximum after aggregation; the commercial adjustable load accounts for 15% of the total commercial load and can provide-50-250 kW of adjustable power. The installed capacity of PV (photovoltaic) amounts to 5700kW, allowing a power reduction proportion of 5%. The maximum load of the electric vehicle charging station can reach 175 kW. The energy storage equipment participating in VPP regulation has 3 units, the maximum power is 400kW, and the total installed amount is 1000 kW. After polymerization, VPP can provide the maximum regulating capacity of-2.75-2.65 MW.
As shown in fig. 4, the multi-type resource optimization combination method of the virtual power plant includes:
step S10, according to the power agreed by the virtual power plant and the power market, the target power and the baseline powerDetermining the difference between the power supply capacity of the virtual power plant and the regulated power required by the virtual power plant at each moment on the time scale:
the external characteristics of the virtual power plant are shown in fig. 5, the aggregated external characteristics of the virtual power plant when the virtual power plant is not regulated are shown as a power baseline, and the virtual power plant has the regulation capability between an up regulation boundary and a down regulation boundary according to the regulation characteristics of the interactive resources of the virtual power plant.
Step S20, the transmission power distribution of each branch is calculated. According to load prediction and load flow calculation provided by a DSO (distribution system operator), and the relation between branch load flow and virtual power plant regulation power, determining the transmission power distribution and the allowable transmission power variation range of each branch. When the regulation and control are not carried out, the transmission power distribution of each branch is shown in fig. 6, the transmission power of the branches 1-2 and 2-3 is large, a large amount of photovoltaic output exists except for 7-18 time periods, the distribution of tide is improved, the blockage of the tide flow is relieved, and the phenomenon that the transmission power of the branches exceeds the limit exists in the rest time periods.
And step S30, determining a combination mode of directly controlling resources by the virtual power plant according to the regulated power. Firstly, under the condition that all the participatable adjusting resources participate in adjustment, the adjusting power required to be provided by the resources directly controlled by the virtual power plant at each moment is calculated according to the target adjusting power
If the resource provides the regulated power during the regulation period, the resource is considered to be involved in regulation (
i — Electric Vehicle (EV), energy storage (ESS)).
Step S40, combining the geographical position distribution, the adjusting capability and the adjusting requirement of the electric automobile and the energy storage equipment, the energy storage equipment participates in the adjustment, the electric automobiles positioned at the nodes 4,6 and 10 participate in the adjustment,
step S50, if yesThe virtual power plant can directly control resources to meet the regulation requirement (| P)VPP,t-Paim,tIs less than or equal to er and-Pmn,N≤Pmn,t≤Pmn,N) The VPP only directly controls the resources to participate in regulation; if the adjustment capability of the virtual power plant for directly controlling the resources is limited and the adjustment requirement cannot be met, the step S60 is executed.
Step S60, determining the combination mode of the resources controlled by the virtual power plant user according to the adjustable range
And updating the target regulated power by combining the combined state of the resources directly controlled by the virtual power plant in the step S40 and the regulated power and the target regulated power of the resources in each period, namely:
the resource type i is a resource EV, ESS directly controlled by VPP; according to the adjustable range of the resources controlled by the virtual power plant user, selecting a resource combination mode which meets the adjustment requirement and can realize the target adjustment power, namely:
i is the resource industry controllable load and the business controllable load controlled by the virtual power plant user. In the final resource combination mode of the virtual power plant, the regulation capacity and the regulation demand of the virtual power plant are shown in fig. 7.
Step S70, calculating the adjusting power distribution of the resource participating in the adjustment
Determining the resource combination state of each resource participating in regulation by the steps S40-S60
And (4) bringing an optimization model, wherein the model is MILP, and solving the regulated power of each resource participating in combination by directly using commercial solving software CPLEX. The distribution of the regulated power of various resources is shown in fig. 8, which can meet the regulation demand of the power market in time; combining the physical position distribution situation of each resourceThe transmission power distribution of each branch circuit after adjustment is as shown in fig. 9, so that the transmission power of each branch circuit is within the allowable safety range, and the blocking management by the distribution network system operator is effectively assisted.
Fig. 10 is a block diagram of a multi-type resource optimal combination system of a virtual power plant according to the present invention, and as shown in fig. 10, the multi-type resource optimal combination system of the virtual power plant includes:
the method comprises the following steps that a first construction module 1 constructs an interactive framework of a multi-party main body in the operation of a virtual power plant, in the interactive framework, tasks born by each main body in the operation process are determined according to the adjusting capacity and the requirements of each main body involved in the operation process, and the main bodies are distributed energy owners, power markets and power distribution systems;
the second construction module 2 is used for constructing a resource regulation characteristic model of the interactive resources of the virtual power plant, establishing a resource regulation characteristic model which can participate in the regulation of the virtual power plant according to the agreement of the virtual power plant and a distributed energy source owner, and preliminarily determining the regulation capacity of the virtual power plant;
the third building module 3 is used for building a resource optimization combination model of the virtual power plant considering the blocking management of the power distribution network, combining the adjusting capacity of the virtual power plant with an adjusting target and building a resource optimization combination model meeting the requirements of each main body;
the constraint linearization module 4 is used for carrying out constraint linearization on branch power flow, introducing sensitivity parameters and describing the relationship between the change of the branch transmission power and the regulated power of the virtual power plant in a linearization manner;
and the resource optimization combination module 5 determines the combination mode of the resources directly controlled by the virtual power plant according to the regulation and control authority of the virtual power plant on the resources and the combination mode of the resources controlled by the distributed energy resource owner according to the resource optimization combination model.
The specific implementation of the multi-type resource optimal combination system of the virtual power plant is substantially the same as that of the multi-type resource optimal combination method of the virtual power plant, and is not repeated herein.
While the foregoing disclosure shows illustrative embodiments of the invention, it should be noted that various changes and modifications could be made herein without departing from the scope of the invention as defined by the appended claims. The functions, steps and/or actions of the method claims in accordance with the inventive embodiments described herein need not be performed in any particular order. Furthermore, although elements of the invention may be described or claimed in the singular, the plural is contemplated unless limitation to a single element is explicitly stated.
Claims (10)
1. A virtual power plant multi-type resource optimal combination method is characterized by comprising
Constructing an interactive framework of a multi-party main body in the operation of a virtual power plant, wherein in the interactive framework, tasks born by each main body in the operation process are determined according to the regulating capacity and the requirement of each main body involved in the operation process, and the main bodies comprise distributed energy owners, power markets and power distribution system operators;
establishing a resource regulation characteristic model of the interactive resources of the virtual power plant, and establishing a resource regulation characteristic model which can participate in the regulation of the virtual power plant according to the agreement of the virtual power plant and a distributed energy source owner, so as to preliminarily determine the regulation capacity of the virtual power plant;
establishing a resource optimization combination model of a virtual power plant considering power distribution network blocking management, combining the regulation capacity of the virtual power plant with a regulation target, and establishing a resource optimization combination model meeting the requirements of each main body;
the branch power flow constraint linearization is realized, sensitivity parameters are introduced, and the relation between the branch transmission power change and the virtual power plant regulation power is described in a linearization manner;
according to the regulation and control authority of the virtual power plant on the resources, firstly, the combination mode of directly controlling the resources by the virtual power plant is determined according to the resource optimization combination model, and then the combination mode of the resources controlled by the distributed energy resource owner is determined.
2. The method of claim 1, wherein the step of constructing an interactive framework of multi-party agents in the operation of the virtual power plant comprises:
the virtual power plant interacts with distributed energy, and resources are divided into three types according to regulation authority: the first kind of resources are observable and uncontrollable, and distributed energy owners submit power demand predictions of the second day to the virtual power plant in the day ahead; the second type of resources can be observed, the running state of the equipment is directly controlled by the virtual power plant, and distributed energy resource owners submit power demand prediction of the second day to the virtual power plant in the day ahead and agree with the virtual power plant to participate in regulation time, regulation mode and regulation power; the third type of resources can be observed, the running state of the equipment is controlled by a distributed energy resource owner, and the distributed energy resource owner provides the power demand prediction and the power adjustable range of the second day for the virtual power plant; the virtual power plant directly controls the equipment running state of the second type of resources according to the regulation demand, and issues the regulation power required by the second day resources to the third type of resources;
the virtual power plant interacts with the power market, the virtual power plant submits a bidding power curve to the power market before, and the power market clears and issues bidding results to the virtual power plant;
the virtual power plant interacts with a power distribution system operator, the virtual power plant predicts and aggregates distributed resources according to the output of the distributed resources to form baseline power, the virtual power plant submits the baseline power to the power distribution system operator, and the power distribution system operator is assisted to predict load distribution in the whole distribution network area; and the distribution system operator issues a distribution network load distribution prediction result to the virtual power plant.
3. The method of claim 2, wherein the step of constructing the resource adjustment characteristic model of the interactive resources of the virtual power plant comprises:
according to the regulation authority of the virtual power plant to the resources and the adjustable characteristic of the resources, the second type of resources change the running state of the equipment according to the regulation target through the following formula,
wherein, Δ PtIs a secondValue of regulation power, P, provided by class resource at time ttFor the output power value in actual operation after the second type of resource is adjusted at the time t,
the predicted value of the output power at the time t when the second type of resources are not regulated and controlled;
according to the regulation authority of the virtual power plant to the resources and the adjustable characteristic of the resources, the third class of resources provides the second class of resources with the regulation power within the following formula range,
Plb,t≤ΔPt≤Pub,t
wherein, Plb,t、Pub,tThe lower and upper limits of the power may be adjusted for the second type of resource, respectively.
4. The method of claim 3, wherein the step of constructing the resource adjustment characteristic model of the interactive resources of the virtual power plant further comprises:
the distributed energy comprises an electric automobile and an energy storage, and the distributed energy owner comprises an electric automobile user and an energy storage user;
for the electric automobile user, appointing an adjustable time interval of the electric automobile, changing the charging track of the electric automobile by slowing down the charging power of the electric automobile in an allowable charging power range by a virtual power plant on the premise of ensuring that the accumulated charging amount in the adjustment period before and after adjustment does not change,
et+1＝et+Pev,t
wherein e istIs the cumulative amount of charge at time t, Pev,tFor the charging power at the time t,
in order to allow the maximum charging power,
is a predicted value of charging power, delta P, at time t when not chargedev,tAdjustment value for charging power provided at time t, etbIs the accumulated charge amount at the end of the adjustment time;
for the energy storage users, the participation regulation time period and the adjustable power range are appointed, the charging power of the energy storage is regulated by keeping the charging state of the energy storage within the healthy range on the premise of ensuring that the energy storage electric quantity is not changed after a regulation and control period,
-PN≤Pess,t≤PN
et+1＝et+Pess,t
SOCt＝SOC0+et/CN
wherein, PNRated charging power for energy storage;
and Pess,tRespectively representing the charging power of the stored energy at t moments before and after adjustment; SOCtRepresenting the state of charge of energy storage at time t; SOC0Is the state of charge for initial energy storage; cNRepresenting the capacity of stored energy;
andSOCand the upper limit and the lower limit of the SOC allowed in the energy storage regulation process are shown.
5. The method of claim 3, wherein the step of constructing the resource adjustment characteristic model of the interactive resources of the virtual power plant further comprises:
the distributed energy resource includes controllable loads including industrially controllable loads and commercially controllable loads, the distributed energy resource owner including industrially controllable load users and commercially controllable load users;
the method comprises the following steps that load requirements of an industrial controllable load user and a commercial controllable load user on the second day are reported to a virtual power plant operator every day and are reported as an uncontrollable load and a controllable load, the virtual power plant issues an expected regulation target to the industrial controllable load user and the commercial controllable load user according to controllable load regulation characteristics and an expected regulation requirement, and the regulation power of the controllable load when the controllable load participates in regulation is as follows:
Plb,ind,t≤ΔPind,t≤Pub,ind,t
Plb,com,t≤ΔPcom,t≤Pub,com,t
wherein, Δ Pind,tRegulated power available for industrial controllable loads; plb,ind,tAnd Pub,ind,tThe lower limit and the upper limit of the adjustable power of the industrial controllable load are respectively; delta Pcom,tRegulated power available to a user of the commercially controllable load; plb,com,tAnd Pub,com,tThe lower and upper limits of the power can be adjusted for the commercially controllable load, respectively.
6. The method of claim 1, wherein the step of establishing a resource optimization portfolio model of the virtual power plant considering distribution grid blocking management comprises: selecting a resource optimization combination mode participating in virtual power plant regulation with the aim of minimizing regulation cost,
wherein, i is the resource type, n is the distribution network node number, and represents the resource access distribution network position, CostiThe adjustment cost coefficient of the resource i is the electric power market punishment cost suffered by unit electric quantity deviation; Δ t denotes the adjustment interval period, λ is the access cost, P, required for a single resource to participate in the adjustmentVPP,tRepresenting the actual operating power, P, of the virtual power plant at the time t after regulationaim,tRepresents the adjusted expected operating power of the virtual power plant and the virtual power plant at the moment t of the power market,
and
and for decision variables, jointly indicate that the resource i at the node n participates in the regulation state,
is an integer variable from 0 to 1, and is,
indicating that the resource is selected to participate in the adjustment combination,
indicating the regulated power provided by the resource at time t.
7. The method of claim 6, wherein the step of establishing a resource optimization portfolio model of the virtual power plant considering distribution grid blocking management further comprises: analyzing the regulation capacity of the virtual power plant from two scales of space and time respectively, comprising:
on a spatial scale, the regulation capability of the virtual power plant at node n is shown as follows:
wherein, Δ Pn,tRepresenting the regulated power which can be provided by the virtual power plant after aggregation at the node n;
Pn,tadjusting active injection power of the front and rear distribution network nodes n for the virtual power plant respectively; pub,n,tAnd Plb,n,tRespectively representing an upper limit and a lower limit of the regulated power which can be provided by the node n;
on a time scale, the regulation capacity of the virtual power plant at time t is shown as follows:
wherein, Pub,tAnd Plb,tRespectively represents the upper limit and the lower limit of the available regulating power at the time t after the virtual power plant aggregation,
representing the actual operating power of the virtual power plant before regulation and control;
and
respectively representing the upper limit and the lower limit of the available regulating power of the resource i at the node n at the time t;
the regulatory requirements of the virtual power plant on a time scale meet the external performance constraints imposed on participating in the power market,
|PVPP,t-Paim,t|≤er
wherein er is the allowed power deviation agreed by the virtual power plant and the power market;
the auxiliary power distribution network of the virtual power plant carries out blocking management, and after the virtual power plant is regulated, the transmission power of each branch of the power distribution network is constrained as follows:
-Pmn,N≤Pmn,t≤Pmn,N
wherein the content of the first and second substances,
the transmission power of the branch m-n when not regulated is obtained, and m and n are respectively serial numbers of connecting nodes at two ends of the branch;
respectively representing the magnitude and phase of the voltage at node m when unregulated; gmn、bmnRespectively representing admittance values of the branches m-n; pmn,tAdjusting the m-n transmission power of the rear branch; pmn,,NThe maximum transmission power allowed for the branch; delta Pmn,tThe amount of change in the transmission power for the branch.
8. The virtual power plant multi-type resource optimal combination method of claim 1, wherein the step of branch flow constraint linearization comprises:
the nonlinear constraint of the branch power flow is linearized by introducing sensitivity parameters,
wherein, Δ Pmn,tRepresenting the change in transmission power, Δ P, of branch mnn,tRepresents the regulated power, alpha, available after the virtual power plant aggregation at node nP,mn,n,tAnd alphaQ,mn,n,tRespectively representing the sensitivity of the change of the active injection power and the reactive injection power of the node N to the change of the transmission power of the branch mn, wherein N represents that the available regulation exists in the range of the distribution networkNumber of nodes, delta Q, for capacity efficient virtual power plant interactive resource accessn,tThere is an active injected power change for node n.
9. The virtual power plant multi-type resource optimal combination method of claim 8, wherein the step of branch flow constraint linearization comprises:
the nonlinear constraint of the branch power flow is linearized by introducing sensitivity parameters,
wherein the content of the first and second substances,
is the power factor angle of node n.
10. A virtual power plant multi-type resource optimal combination system is characterized by comprising:
the system comprises a first construction module, a second construction module and a third construction module, wherein the first construction module is used for constructing an interactive framework of a plurality of main bodies in the operation of the virtual power plant, in the interactive framework, tasks born by each main body in the operation process are determined according to the adjusting capacity and the requirements of each main body involved in the operation process, and the main bodies are distributed energy owners, power markets and power distribution systems;
the second construction module is used for constructing a resource regulation characteristic model of the interactive resources of the virtual power plant, establishing a resource regulation characteristic model which can participate in the regulation of the virtual power plant according to the agreement of the virtual power plant and a distributed energy source owner, and preliminarily determining the regulation capacity of the virtual power plant;
the third building module is used for building a resource optimization combination model of the virtual power plant considering the blocking management of the power distribution network, combining the adjusting capacity of the virtual power plant with an adjusting target and building a resource optimization combination model meeting the requirements of each main body;
the constraint linearization module is used for carrying out constraint linearization on branch power flow, introducing sensitivity parameters and describing the relationship between the change of the branch transmission power and the regulated power of the virtual power plant in a linearization manner;
and the resource optimization combination module determines a combination mode of directly controlling resources by the virtual power plant according to the regulation and control authority of the virtual power plant on the resources and determines a combination mode of the resources controlled by the distributed energy owner according to the resource optimization combination model.
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Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112383093A (en) * | 2020-11-30 | 2021-02-19 | 深圳供电局有限公司 | Controllable resource control and evaluation system of virtual power plant |
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| CN112381146B (en) * | 2020-11-16 | 2024-05-21 | 海南省电力学校(海南省电力技工学校) | Distributed resource self-organizing aggregation and cooperative control method under virtual power plant |
| CN112381146A (en) * | 2020-11-16 | 2021-02-19 | 海南省电力学校(海南省电力技工学校) | Distributed resource self-organization aggregation and cooperative control method under virtual power plant |
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| CN115237080B (en) * | 2022-09-19 | 2022-12-09 | 国网信息通信产业集团有限公司 | Virtual power plant based equipment regulation and control method, device, equipment and readable medium |
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| WO2024060413A1 (en) * | 2022-09-20 | 2024-03-28 | 国网上海能源互联网研究院有限公司 | Method and apparatus for constructing adjustable capacity of virtual power plant, electronic device, storage medium, program, and program product |
| CN115222298A (en) * | 2022-09-20 | 2022-10-21 | 国网上海能源互联网研究院有限公司 | Virtual power plant adjustable capacity construction method and device, storage medium and electronic equipment |
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| CN116505595B (en) * | 2023-06-29 | 2023-09-15 | 国网安徽省电力有限公司巢湖市供电公司 | Power monitoring, dispatching and managing system based on virtual power plant |
| CN116757445A (en) * | 2023-08-14 | 2023-09-15 | 国网上海能源互联网研究院有限公司 | Method, device, equipment and medium for quickly distributing adjustment capability of virtual power plant |
| CN116757445B (en) * | 2023-08-14 | 2023-11-14 | 国网上海能源互联网研究院有限公司 | Method, device, equipment and medium for quickly distributing adjustment capability of virtual power plant |
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