CN111339637A - Electricity selling method and device based on virtual power plant - Google Patents

Electricity selling method and device based on virtual power plant Download PDF

Info

Publication number
CN111339637A
CN111339637A CN202010079112.6A CN202010079112A CN111339637A CN 111339637 A CN111339637 A CN 111339637A CN 202010079112 A CN202010079112 A CN 202010079112A CN 111339637 A CN111339637 A CN 111339637A
Authority
CN
China
Prior art keywords
load
virtual power
power plant
loads
distributed energy
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202010079112.6A
Other languages
Chinese (zh)
Inventor
叶飞
王宣元
窦迅
邵平
郭艳敏
黄文渊
刘蓁
冯树海
杨争林
龙苏岩
郑亚先
薛必克
程海花
王高琴
黄春波
徐骏
陈爱林
吕建虎
史新红
张旭
冯凯
杨辰星
冯恒
王一凡
曹晓峻
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
State Grid Corp of China SGCC
China Electric Power Research Institute Co Ltd CEPRI
State Grid Jibei Electric Power Co Ltd
Original Assignee
State Grid Corp of China SGCC
China Electric Power Research Institute Co Ltd CEPRI
State Grid Jibei Electric Power Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by State Grid Corp of China SGCC, China Electric Power Research Institute Co Ltd CEPRI, State Grid Jibei Electric Power Co Ltd filed Critical State Grid Corp of China SGCC
Priority to CN202010079112.6A priority Critical patent/CN111339637A/en
Publication of CN111339637A publication Critical patent/CN111339637A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06315—Needs-based resource requirements planning or analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply

Landscapes

  • Business, Economics & Management (AREA)
  • Human Resources & Organizations (AREA)
  • Engineering & Computer Science (AREA)
  • Economics (AREA)
  • Strategic Management (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Tourism & Hospitality (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Marketing (AREA)
  • General Physics & Mathematics (AREA)
  • General Business, Economics & Management (AREA)
  • Quality & Reliability (AREA)
  • Development Economics (AREA)
  • Game Theory and Decision Science (AREA)
  • Operations Research (AREA)
  • Educational Administration (AREA)
  • Health & Medical Sciences (AREA)
  • Public Health (AREA)
  • Water Supply & Treatment (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Supply And Distribution Of Alternating Current (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The invention provides an electricity selling method and device based on a virtual power plant, which are used for acquiring the respective quantity of distributed energy sources, interruptible loads and translatable loads in the virtual power plant; inputting the obtained quantity into a pre-constructed electricity selling optimization model for solving to obtain the states of the distributed energy, the interruptible load and the translatable load which respectively participate in the virtual power plant, the active output of the distributed energy, the interrupting load capacity of the interruptible load and the translating load capacity of the translatable load; the electricity selling optimization model is constructed based on the relationship among load requirements, distributed energy sources and adjustable loads in the governed power distribution network and by taking the maximum volume of the electric selling company as a target, so that the resource utilization rate is greatly improved, the influence on the stability of the power grid is small, the cost for realizing peak clipping and valley filling is reduced, the distributed resources are fully utilized, the distributed energy sources and the adjustable loads which are dispersed in the governed power distribution network are aggregated into a plurality of virtual power plants with external characteristics, a scheduling plan is responded, the distributed resources are fully utilized, the volume of the electric selling company is improved, and the electricity selling optimization model has popularization and implementation.

Description

Electricity selling method and device based on virtual power plant
Technical Field
The invention relates to the technical field of technical power system simulation, in particular to a virtual power plant-based electricity selling method and device.
Background
Under the power market environment with separated distribution and sale, the power selling competition is more intense, and how the power selling company with the power distribution network operation right schedules power resources in the governed distribution network improves the power purchasing income is the key for the power selling company to obtain a favorable position in the market competition. Distributed Electric Resources (DER) and tunable loads (DL) capable of participating in demand response are dispersedly existing in a distribution network governed by an electric power selling company, and due to transaction admission and scheduling security requirements, part of resources cannot participate in transaction and scheduling, so that resource waste is caused. The Virtual Power Plant (VPP) can integrate various distributed energy sources to participate in the operation of the power market, and provides a new way for the power selling company to utilize the distributed energy sources and the adjustable load in a large scale.
In the prior art, the electricity selling method of an electricity selling company aims at DR behaviors of large-load users such as industrial users, describes the selection behaviors of the users to the electricity selling company based on a psychological method, analyzes the competitive behavior characteristics of the electricity selling company in a market environment, stimulates the electricity selling company to provide diversified value-added services through a control and marketing mode, and provides an electricity selling strategy of the electricity selling company; and (3) analyzing the effect of the load on the user side on balancing the deviation electric quantity by combining the development demand of the power distribution network and the specific situation of the electric power system reform aiming at the response market price and the incentive information of the user, formulating a user power consumption contract package and a DR contract package considering the contribution degree of the user, and providing a power selling strategy for a power selling company. However, the prior art has low resource utilization rate, has large influence on the stability of the power grid, and has high cost for realizing peak clipping and valley filling.
Disclosure of Invention
In order to overcome the defects of low resource utilization rate, large influence on the stability of a power grid and high cost for realizing peak clipping and valley filling in the prior art, the invention provides the power selling method and the device based on the virtual power plant, which greatly improve the resource utilization rate, have small influence on the stability of the power grid and reduce the cost for realizing peak clipping and valley filling.
In order to achieve the purpose of the invention, the invention adopts the following technical scheme:
in one aspect, the invention provides a virtual power plant-based electricity selling method, which comprises the following steps:
acquiring respective quantities of distributed energy sources, interruptible loads and translatable loads in a virtual power plant;
inputting the respective quantities of the distributed energy sources, the interruptible loads and the translatable loads in the virtual power plant into a pre-constructed electricity selling optimization model for solving to obtain the states of the distributed energy sources, the interruptible loads and the translatable loads participating in the virtual power plant, and the active output of the distributed energy sources, the interrupting load quantity of the interruptible loads and the translating load quantity of the translatable loads;
the electricity selling optimization model is constructed based on the relation among the load demand, the distributed energy sources and the adjustable load in the governed power distribution network and by taking the maximum volume of trades of the electricity selling company as a target.
The construction of the electricity selling optimization model comprises the following steps:
dividing a virtual power plant into distributed energy and an adjustable load, and dividing the adjustable load into an interruptible load and a translatable load;
based on the respective quantity of distributed energy sources, interruptible loads and translatable loads in the virtual power plant and the relationship among load demands, distributed energy sources and adjustable loads in the governed power distribution network, constructing an objective function with the maximization of the volume of charge of a power selling company as a target;
and determining constraint conditions corresponding to the objective function, wherein the constraint conditions comprise power selling income constraint, power flow constraint, interruptible load quantity constraint, translatable load quantity constraint, distributed energy output constraint, virtual power plant calling constraint, distribution network operation safety constraint, energy storage operation constraint, real-time market transaction constraint and power selling price constraint.
The objective function is as follows:
Figure BDA0002379633520000021
wherein F is the volume of the electric power selling company; delta T is the calling time interval of the virtual power plant; t is the total duration; d is the number of power supply type virtual power plants; h is the number of interruptible load type virtual power plants; l is the number of load-type virtual power plants capable of translating; e is the number of distributed energy sources; k is the number of interruptible loads; g is the number of translatable loads;
Figure BDA0002379633520000022
the active power output of the e-th distributed energy source in the t period;
Figure BDA0002379633520000023
a variable of 0 to 1, which represents the state that the e distributed energy source participates in the virtual power plant;
Figure BDA0002379633520000024
an interrupt load amount for a kth interruptible load for a period t;
Figure BDA0002379633520000025
a variable of 0-1, which represents the state of the kth interruptible load participating in the virtual plant;
Figure BDA0002379633520000026
the translational load amount of the g-th translatable load in the t period;
Figure BDA0002379633520000027
is a 0-1 variable representing the state of the g translatable load participating in the virtual plant.
The electricity selling profit constraint is as follows:
Figure BDA0002379633520000028
in the formula, CminThe minimum income of the power selling company all day; n is the number of distribution network nodes; m is the number of virtual power plants;
Figure BDA0002379633520000029
the price of electricity sold in the time period t;
Figure BDA00023796335200000210
is the active load of the node i in the period t;
Figure BDA00023796335200000211
the calling quantity of the mth virtual power plant in the t period is obtained;
Figure BDA00023796335200000212
for the day-ahead market price of electricity, Pt dPurchasing electricity for the day-ahead market；
Figure BDA00023796335200000213
For trading electricity prices in real time, Pt rTrading the electricity in real time;
Figure BDA00023796335200000214
paying for the unit of the e distributed energy;
Figure BDA0002379633520000031
compensating the cost for the unit of load interruption for the kth interruptible load;
Figure BDA0002379633520000032
a unit compensation cost for load translation for the g-th translatable load; cVPPAnd the virtual power plant calling cost is the combined distributed energy and adjustable load.
Virtual power plant calling cost C after combination of distributed energy and adjustable loadVPPSatisfies the following conditions:
Figure BDA0002379633520000033
in the formula (I), the compound is shown in the specification,
Figure BDA0002379633520000034
unit cost for the calling amount of the d-th PSVPP;
Figure BDA0002379633520000035
the unit cost of the call volume of the h interruptible load type virtual power plant;
Figure BDA0002379633520000036
the unit cost of the call volume of the first load-suppressible virtual power plant.
When the number of the distributed energy sources is more than the total number of interruptible loads and translatable loads, the virtual power plant is a power source type virtual power plant;
when the number of the interruptible loads is more than the total number of the distributed energy sources and the translatable loads, the virtual power plant is an interruptible load virtual power plant;
and when the quantity of the translatable loads is more than the total quantity of the distributed energy sources and the interruptible loads, the virtual power plant is a translatable load type virtual power plant.
Inputting the respective quantities of the distributed energy sources, the interruptible loads and the translatable loads in the virtual power plant into a pre-constructed electricity selling optimization model for solving, wherein the solving comprises the following steps:
and solving the electricity selling optimization model by adopting a DICOPT solver.
In another aspect, the present invention further provides an electricity selling device based on a virtual power plant, including:
the acquisition module is used for acquiring the respective quantity of distributed energy sources, interruptible loads and translatable loads in the virtual power plant;
the solving module is used for inputting the respective quantities of the distributed energy sources, the interruptible loads and the translatable loads in the virtual power plant into a pre-constructed electricity selling optimization model for solving to obtain the states of the distributed energy sources, the interruptible loads and the translatable loads participating in the virtual power plant, the active output of the distributed energy sources, the interrupting load quantity of the interruptible loads and the translating load quantity of the translatable loads;
the electricity selling optimization model is constructed based on the relation among the load demand, the distributed energy sources and the adjustable load in the governed power distribution network and by taking the maximum volume of trades of the electricity selling company as a target.
The system further comprises a modeling module, wherein the modeling module is specifically used for:
dividing a virtual power plant into distributed energy and an adjustable load, and dividing the adjustable load into an interruptible load and a translatable load;
based on the respective quantity of distributed energy sources, interruptible loads and translatable loads in the virtual power plant and the relationship among load demands, distributed energy sources and adjustable loads in the governed power distribution network, constructing an objective function with the maximization of the volume of charge of a power selling company as a target;
and determining constraint conditions corresponding to the objective function, wherein the constraint conditions comprise power selling income constraint, power flow constraint, interruptible load quantity constraint, translatable load quantity constraint, distributed energy output constraint, virtual power plant calling constraint, distribution network operation safety constraint, energy storage operation constraint, real-time market transaction constraint and power selling price constraint.
The objective function constructed by the modeling module is as follows:
Figure BDA0002379633520000041
wherein F is the volume of the electric power selling company; delta T is the calling time interval of the virtual power plant; t is the total duration; d is the number of power supply type virtual power plants; h is the number of interruptible load type virtual power plants; l is the number of load-type virtual power plants capable of translating; e is the number of distributed energy sources; k is the number of interruptible loads; g is the number of translatable loads;
Figure BDA0002379633520000042
the active power output of the e-th distributed energy source in the t period;
Figure BDA0002379633520000043
a variable of 0 to 1, which represents the state that the e distributed energy source participates in the virtual power plant;
Figure BDA0002379633520000044
an interrupt load amount for a kth interruptible load for a period t;
Figure BDA0002379633520000045
a variable of 0-1, which represents the state of the kth interruptible load participating in the virtual plant;
Figure BDA0002379633520000046
the translational load amount of the g-th translatable load in the t period;
Figure BDA0002379633520000047
is a 0-1 variable representing the state of the g translatable load participating in the virtual plant.
The electricity selling profit constraint determined by the modeling module is as follows:
Figure BDA0002379633520000048
in the formula, CminThe minimum income of the power selling company all day; n is the number of distribution network nodes; m is the number of virtual power plants;
Figure BDA0002379633520000049
the price of electricity sold in the time period t;
Figure BDA00023796335200000410
is the active load of the node i in the period t;
Figure BDA00023796335200000411
the calling quantity of the mth virtual power plant in the t period is obtained;
Figure BDA00023796335200000412
for the day-ahead market price of electricity, Pt dPurchasing electric quantity for the market at the day before;
Figure BDA00023796335200000413
for trading electricity prices in real time, Pt rTrading the electricity in real time;
Figure BDA00023796335200000414
paying for the unit of the e distributed energy;
Figure BDA00023796335200000415
compensating the cost for the unit of load interruption for the kth interruptible load;
Figure BDA00023796335200000416
a unit compensation cost for load translation for the g-th translatable load; cVPPThe virtual power plant calling cost after the combination of distributed energy and adjustable load is met:
Figure BDA00023796335200000417
in the formula (I), the compound is shown in the specification,
Figure BDA00023796335200000418
unit cost for the calling amount of the d-th PSVPP;
Figure BDA00023796335200000419
the unit cost of the call volume of the h interruptible load type virtual power plant;
Figure BDA00023796335200000420
the unit cost of the call volume of the first load-suppressible virtual power plant.
When the number of the distributed energy sources is more than the total number of interruptible loads and translatable loads, the virtual power plant is a power source type virtual power plant;
when the number of the interruptible loads is more than the total number of the distributed energy sources and the translatable loads, the virtual power plant is an interruptible load virtual power plant;
and when the quantity of the translatable loads is more than the total quantity of the distributed energy sources and the interruptible loads, the virtual power plant is a translatable load type virtual power plant.
The solving module is specifically configured to:
and solving the electricity selling optimization model by adopting a DICOPT solver.
Compared with the closest prior art, the technical scheme provided by the invention has the following beneficial effects:
the virtual power plant-based electricity selling method provided by the invention comprises the following steps: acquiring respective quantities of distributed energy sources, interruptible loads and translatable loads in a virtual power plant; inputting the respective quantity of distributed energy, interruptible load and translatable load in the virtual power plant into a pre-constructed electricity selling optimization model for solving to obtain the state that the distributed energy, the interruptible load and the translatable load respectively participate in the virtual power plant, and the active output of the distributed energy, the interrupting load quantity of the interruptible load and the translating load quantity of the translatable load; the electricity selling optimization model is constructed based on the relationship among the load demand, the distributed energy and the adjustable load in the governed power distribution network and by taking the maximum volume of trades of the electricity selling company as a target, so that the resource utilization rate is greatly improved, the influence on the stability of the power grid is small, and the cost for realizing peak clipping and valley filling is reduced;
the technical scheme provided by the invention defines the relationship between the operation of an electricity selling company containing the virtual power plants and an external market, load requirements in a managed distribution network, distributed energy sources and adjustable loads, fully utilizes distributed resources, aggregates the distributed energy sources and the adjustable loads in the managed distribution network into a plurality of virtual power plants with external characteristics, and responds to a scheduling plan, so that the distributed resources are fully utilized, and the volume of finished goods of the electricity selling company is increased;
the method adopts the DICOPT solver to solve the electricity selling optimization model, the solving process is simple, the solving efficiency is high, and the accuracy of the solving result is high;
the technical scheme provided by the invention is beneficial to the integrated utilization of distributed resources which are difficult to be admitted and regulated by the power selling company containing the virtual power plant, improves the utilization rate of the distributed resources by the power selling company, makes reasonable power selling price within a market allowable range, improves the economic benefit of power selling operation of the power selling company while ensuring the stable operation of the power distribution network, and has popularization and implementation.
Drawings
FIG. 1 is a flow chart of a virtual power plant based electricity selling method in an embodiment of the invention;
FIG. 2 is a schematic diagram of a modified IEEE33 node power distribution network system in accordance with an embodiment of the present invention;
FIG. 3 is a diagram illustrating a VPP dynamic combination strategy for periods 8-11h and 18-21h according to an embodiment of the present invention;
FIG. 4 is a diagram illustrating a VPP dynamic combination strategy during periods 12-17h and 22-23h according to an embodiment of the present invention;
FIG. 5 is a diagram illustrating a VPP dynamic combination strategy for periods 1-7h and 24h according to an embodiment of the present invention;
FIG. 6 is a schematic diagram illustrating declaration and actual calling of IL in the range of distribution networks administered by power selling companies including virtual power plants in the embodiment of the present invention;
FIG. 7 is a schematic diagram of TL declaration and actual calling in the range of distribution network managed by an electric power selling company including a virtual power plant in the embodiment of the invention;
FIG. 8 is a schematic diagram of the declaration and actual invocation of DER in the range of distribution networks governed by electricity-selling companies including virtual power plants in the embodiment of the present invention;
FIG. 9 is a schematic diagram of energy storage operation conditions in a distribution network range governed by an electric power selling company including a virtual power plant in the embodiment of the invention;
FIG. 10 is a schematic diagram of the electricity selling prices of electricity selling companies including virtual power plants to users in the range of the administered power distribution network in the embodiment of the present invention;
fig. 11 is a schematic diagram of a transaction situation of a power selling company with a virtual power plant in a real-time market in the embodiment of the invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
Example 1
The embodiment 1 of the invention provides a virtual power plant-based electricity selling method, a specific flow chart is shown in fig. 1, and the specific process is as follows:
s101: acquiring respective quantities of distributed energy sources, interruptible loads and translatable loads in a virtual power plant;
s102: inputting the respective quantity of distributed energy, interruptible load and translatable load in the virtual power plant into a pre-constructed electricity selling optimization model for solving to obtain the state that the distributed energy, the interruptible load and the translatable load respectively participate in the virtual power plant, and the active output of the distributed energy, the interrupting load quantity of the interruptible load and the translating load quantity of the translatable load;
the electricity selling optimization model is constructed based on the relation among the load demand, the distributed energy sources and the adjustable load in the governed power distribution network and by taking the maximum trading volume of an electricity selling company as a target.
The construction of the electricity selling optimization model in the step S102 comprises the following steps:
dividing a virtual power plant into distributed energy and an adjustable load, and dividing the adjustable load into an interruptible load and a translatable load;
based on the respective quantity of distributed energy sources, interruptible loads and translatable loads in the virtual power plant and the relationship among load demands, distributed energy sources and adjustable loads in the governed power distribution network, constructing an objective function with the maximization of the volume of charge of a power selling company as a target;
and determining constraint conditions corresponding to the objective function, wherein the constraint conditions comprise power selling income constraint, power flow constraint, interruptible load quantity constraint, translatable load quantity constraint, distributed energy output constraint, virtual power plant calling constraint, distribution network operation safety constraint, energy storage operation constraint, real-time market transaction constraint and power selling price constraint.
The objective function is as follows:
Figure BDA0002379633520000071
wherein F is the volume of the electric power selling company; delta T is the calling time interval of the virtual power plant; t is the total duration; d is the number of power supply type virtual power plants; h is the number of interruptible load type virtual power plants; l is the number of load-type virtual power plants capable of translating; e is the number of distributed energy sources; k is the number of interruptible loads; g is the number of translatable loads;
Figure BDA0002379633520000072
the active power output of the e-th distributed energy source in the t period;
Figure BDA0002379633520000073
a variable of 0 to 1, which represents the state that the e distributed energy source participates in the virtual power plant;
Figure BDA0002379633520000074
an interrupt load amount for a kth interruptible load for a period t;
Figure BDA0002379633520000075
a variable of 0-1, which represents the state of the kth interruptible load participating in the virtual plant;
Figure BDA0002379633520000076
the translational load amount of the g-th translatable load in the t period;
Figure BDA0002379633520000077
is a 0-1 variable representing the state of the g translatable load participating in the virtual plant.
(1) The electricity selling profit is constrained by the following formula:
Figure BDA0002379633520000078
in the formula, CminThe minimum income of the power selling company all day; n is the number of distribution network nodes; m is the number of virtual power plants;
Figure BDA0002379633520000079
the price of electricity sold in the time period t;
Figure BDA00023796335200000710
is the active load of the node i in the period t;
Figure BDA00023796335200000711
the calling quantity of the mth virtual power plant in the t period is obtained;
Figure BDA00023796335200000712
for the day-ahead market price of electricity, Pt dPurchasing electric quantity for the market at the day before;
Figure BDA00023796335200000713
for trading electricity prices in real time, Pt rTrading the electricity in real time;
Figure BDA00023796335200000714
paying for the unit of the e distributed energy;
Figure BDA00023796335200000715
for load interruption of the kth interruptible loadUnit compensation cost;
Figure BDA00023796335200000716
a unit compensation cost for load translation for the g-th translatable load; cVPPAnd the virtual power plant calling cost is the combined distributed energy and adjustable load.
Virtual power plant calling cost C after combination of distributed energy and adjustable loadVPPSatisfies the following conditions:
Figure BDA00023796335200000717
in the formula (I), the compound is shown in the specification,
Figure BDA00023796335200000718
unit cost for the calling amount of the d-th PSVPP;
Figure BDA00023796335200000719
the unit cost of the call volume of the h interruptible load type virtual power plant;
Figure BDA00023796335200000720
the unit cost of the call volume of the first load-suppressible virtual power plant.
(2) Power flow constraint:
Figure BDA0002379633520000081
Figure BDA0002379633520000082
Figure BDA0002379633520000083
Figure BDA0002379633520000084
Figure BDA0002379633520000085
Figure BDA0002379633520000086
in the formula: pij,tAnd Qij,tRespectively the active power and the reactive power of the branch ij in the t period; k represents an end node taking the node j as a head node; r isijAnd xijThe resistance and reactance of branch ij are respectively; i isij,tThe line current amplitude for branch ij; pi,tAnd Qi,tRespectively the active power net injection value and the reactive power net injection value at the node i;
Figure BDA0002379633520000087
in order to provide the charging power for the stored energy,
Figure BDA0002379633520000088
discharge power for stored energy;
Figure BDA0002379633520000089
for the load reactive power at node i for the period t,
Figure BDA00023796335200000810
the g th TL reactive load is transferred into the load for the t period,
Figure BDA00023796335200000811
for the reactive load shedding of the g th TL during the t period,
Figure BDA00023796335200000812
for a reactive interrupt load of the kth IL for a period t,
Figure BDA00023796335200000813
for the reactive power of the e DER during the t period,
Figure BDA00023796335200000814
the reactive power of the mth virtual power plant in the t period; vi,tAnd Vj,tAre respectively a sectionThe magnitude of the voltage at points i and j.
(3) Interruptible load amount constraint:
Figure BDA00023796335200000815
in the formula:
Figure BDA00023796335200000816
and
Figure BDA00023796335200000817
an upper limit and a lower limit of an interrupt load amount of the kth interruptible load, respectively;
Figure BDA00023796335200000818
is the power factor angle of IL.
(4) Translatable load volume constraint
Figure BDA00023796335200000819
Figure BDA00023796335200000820
Figure BDA00023796335200000821
Figure BDA00023796335200000822
In the formula:
Figure BDA00023796335200000823
and
Figure BDA00023796335200000824
the upper limit and the lower limit of the translational load quantity of the g th TL respectively;
Figure BDA00023796335200000825
power factor of TLAnd (4) an angle.
(5) And (3) output constraint of distributed energy:
Figure BDA00023796335200000826
Figure BDA00023796335200000827
in the formula:
Figure BDA0002379633520000091
and
Figure BDA0002379633520000092
respectively setting the active output upper limit and the active output lower limit of the e-th distributed energy source;
Figure BDA0002379633520000093
and
Figure BDA0002379633520000094
respectively corresponding upper and lower limits of reactive output of DER;
Figure BDA0002379633520000095
and taking 1 to indicate that the e-th DER participates in the virtual power plant, and taking 0 to indicate that the e-th DER does not participate in the virtual power plant.
The distributed energy resource of the embodiment of the invention comprises photovoltaic resources, and the active power and the reactive power of the photovoltaic cell meet the following constraints:
Figure BDA0002379633520000096
Figure BDA0002379633520000097
in the formula:
Figure BDA0002379633520000098
the active power output of the e-th photovoltaic DER in the t period;
Figure BDA0002379633520000099
the active output predicted value of the e-th photovoltaic DER in the t period is obtained;
Figure BDA00023796335200000910
the reactive power output of the e-th photovoltaic DER in the t period;
Figure BDA00023796335200000911
the maximum apparent power of the photovoltaic inverter.
(6) Virtual power plant calling constraint:
Figure BDA00023796335200000912
Figure BDA00023796335200000913
Figure BDA00023796335200000914
Figure BDA00023796335200000915
Figure BDA00023796335200000916
in the formula:
Figure BDA00023796335200000917
and
Figure BDA00023796335200000918
respectively setting the upper limit and the lower limit of the calling quantity of the mth virtual power plant;
Figure BDA00023796335200000919
is the power factor angle of the virtual power plant;
Figure BDA00023796335200000920
and
Figure BDA00023796335200000921
the upper limit and the lower limit of the call quantity of IL participating in ILVPP are respectively;
Figure BDA00023796335200000922
and
Figure BDA00023796335200000923
upper limit and lower limit of calling amount of TL participating TLVPP respectively;
Figure BDA00023796335200000924
and
Figure BDA00023796335200000925
the upper limit and the lower limit of the call volume of DER participation PSVPP are respectively.
(7) And (4) power distribution network operation safety restraint:
Vi,min≤Vi,t≤Vi,max
Iij,t≤Iij,max
|P0,t|≤P0,max
|Q0,t|≤Q0,max
in the formula: vi,maxAnd Vi,minRespectively an upper limit and a lower limit of the voltage amplitude of the node i; i isij,maxIs the current amplitude upper limit of the branch ij; p0,tAnd Q0,tRespectively representing the inflow and outflow active and reactive power of a main network connecting line between a superior power grid and a power selling company in a t period, wherein the positive time represents the inflow from the superior power grid to the power selling company, and the negative time represents the inflow from the power selling company to the superior power grid; p0,max、Q0,maxUpper limits for active and reactive power respectively flowing in/out of the main network connection.
(8) Energy storage operation restraint:
Figure BDA0002379633520000101
Figure BDA0002379633520000102
Figure BDA0002379633520000103
Figure BDA0002379633520000104
in the formula: ESSi,tThe total energy of the connection energy storage on the node i in the period t;
Figure BDA0002379633520000105
the charging state of the energy storage connected to node i,
Figure BDA0002379633520000106
the discharge state for the connection of the stored energy on node i,
Figure BDA0002379633520000107
and
Figure BDA0002379633520000108
all of which are variables from 0 to 1,
Figure BDA0002379633520000109
when 1 is taken out, the node i is connected with the stored energy and is in a charging state,
Figure BDA00023796335200001010
when 0 is taken, the connection energy storage on the node i is in an uncharged state;
Figure BDA00023796335200001011
when 1 is taken out, the connection energy storage on the node i is in a discharging state,
Figure BDA00023796335200001012
when 0 is taken, the connection energy storage on the node i is in the undischarged state, ηchCharging efficiency for stored energy, ηdisDischarge efficiency for energy storage;
Figure BDA00023796335200001013
the upper limit of the charging power for connecting the stored energy on the node i,
Figure BDA00023796335200001014
and the upper limit of the discharge power of the energy storage connected to the node i.
Wherein, ESSi,tSatisfies the following conditions:
ESSi,max×20％≤ESSi,t≤ESSi,max×90％
ESSi,maxin order to connect the upper limit of the capacity of the energy storage to the node i, limit the electric quantity in order to ensure the working efficiency and prolong the service life of the energy storage system in normal use, the embodiment of the invention sets the actual use range to be 20% -90%.
Meanwhile, in order to ensure that the stored energy can be charged and discharged at the beginning of scheduling and has the same regulation characteristic in a new scheduling period, the initial electric quantity of the stored energy is set to be 50% of the capacity limit and is equal to the initial capacity setting of the next period, namely the stored energy is charged and discharged at the beginning of scheduling, and the initial electric quantity of the stored energy is set to be 50% of the capacity limit and
Figure BDA00023796335200001015
(9) real-time market trading constraints:
Figure BDA00023796335200001016
Figure BDA00023796335200001017
in the formula: a and b are the relation coefficients of real-time market electricity price and load,
Figure BDA0002379633520000111
trading the upper limit of the electric quantity in the real-time market for the electric power selling company.
(10) And (3) restricting electricity selling price:
the electricity selling price is an important influence factor of the income of the electricity selling company, in a competitive electricity retail market, the electricity selling company needs to comprehensively consider factors such as electricity purchasing cost, market share, user satisfaction and the like to set the electricity selling price, and then the electricity selling price is constrained by the following formula:
Figure BDA0002379633520000112
Figure BDA0002379633520000113
in the formula:
Figure BDA0002379633520000114
in order to be the upper limit of the price of electricity sold,
Figure BDA0002379633520000115
a lower limit for electricity selling price;
Figure BDA0002379633520000116
the average electricity selling price is determined by the negotiation between an electricity selling company and users in the governed power distribution network.
When the number of the distributed energy sources is more than the total number of interruptible loads and translatable loads, the virtual power plant is a power source type virtual power plant;
when the number of interruptible loads is more than the total number of distributed energy sources and translatable loads, the virtual power plant is an interruptible load virtual power plant;
when the number of the translatable loads is more than the total number of the distributed energy sources and the interruptible loads, the virtual power plant is a translatable load type virtual power plant.
Inputting the respective quantities of distributed energy sources, interruptible loads and translatable loads in a virtual power plant into a pre-constructed electricity selling optimization model for solving, wherein the solving comprises the following steps:
and solving the electricity selling optimization model by adopting a DICOPT solver.
Example 2
Based on the same inventive concept, embodiment 2 of the present invention further provides an electricity selling device based on a virtual power plant, and the following describes the functions of each component in detail:
the acquisition module is used for acquiring the respective quantity of distributed energy sources, interruptible loads and translatable loads in the virtual power plant;
the solving module is used for inputting the respective quantities of the distributed energy, the interruptible load and the translatable load in the virtual power plant into a pre-constructed electricity selling optimization model for solving to obtain the states of the distributed energy, the interruptible load and the translatable load participating in the virtual power plant respectively, and the active output of the distributed energy, the interrupting load capacity of the interruptible load and the translating load capacity of the translatable load;
the electricity selling optimization model is constructed based on the relation among the load demand, the distributed energy sources and the adjustable load in the governed power distribution network and by taking the maximum trading volume of an electricity selling company as a target.
The electricity selling device provided by the embodiment 2 of the invention further comprises a modeling module, wherein the modeling module is specifically used for:
dividing a virtual power plant into distributed energy and an adjustable load, and dividing the adjustable load into an interruptible load and a translatable load;
based on the respective quantity of distributed energy sources, interruptible loads and translatable loads in the virtual power plant and the relationship among load demands, distributed energy sources and adjustable loads in the governed power distribution network, constructing an objective function with the maximization of the volume of charge of a power selling company as a target;
and determining constraint conditions corresponding to the objective function, wherein the constraint conditions comprise power selling income constraint, power flow constraint, interruptible load quantity constraint, translatable load quantity constraint, distributed energy output constraint, virtual power plant calling constraint, distribution network operation safety constraint, energy storage operation constraint, real-time market transaction constraint and power selling price constraint.
The objective function constructed by the modeling module is as follows:
Figure BDA0002379633520000121
wherein F is the volume of the electric power selling company; delta T is the tone of the virtual power plantWith a time interval; t is the total duration; d is the number of power supply type virtual power plants; h is the number of interruptible load type virtual power plants; l is the number of load-type virtual power plants capable of translating; e is the number of distributed energy sources; k is the number of interruptible loads; g is the number of translatable loads;
Figure BDA0002379633520000122
the active power output of the e-th distributed energy source in the t period;
Figure BDA0002379633520000123
a variable of 0 to 1, which represents the state that the e distributed energy source participates in the virtual power plant;
Figure BDA0002379633520000124
an interrupt load amount for a kth interruptible load for a period t;
Figure BDA0002379633520000125
a variable of 0-1, which represents the state of the kth interruptible load participating in the virtual plant;
Figure BDA0002379633520000126
the translational load amount of the g-th translatable load in the t period;
Figure BDA0002379633520000127
is a 0-1 variable representing the state of the g translatable load participating in the virtual plant.
The electricity selling profit constraint determined by the modeling module is as follows:
Figure BDA0002379633520000128
in the formula, CminThe minimum income of the power selling company all day; n is the number of distribution network nodes; m is the number of virtual power plants;
Figure BDA0002379633520000129
the price of electricity sold in the time period t;
Figure BDA00023796335200001210
is the active load of the node i in the period t;
Figure BDA00023796335200001211
the calling quantity of the mth virtual power plant in the t period is obtained;
Figure BDA00023796335200001212
for the day-ahead market price of electricity, Pt dPurchasing electric quantity for the market at the day before;
Figure BDA00023796335200001213
for trading electricity prices in real time, Pt rTrading the electricity in real time;
Figure BDA00023796335200001214
paying for the unit of the e distributed energy;
Figure BDA00023796335200001215
compensating the cost for the unit of load interruption for the kth interruptible load;
Figure BDA00023796335200001216
a unit compensation cost for load translation for the g-th translatable load; cVPPThe virtual power plant calling cost after the combination of distributed energy and adjustable load is met:
Figure BDA00023796335200001217
in the formula (I), the compound is shown in the specification,
Figure BDA0002379633520000131
unit cost for the calling amount of the d-th PSVPP;
Figure BDA0002379633520000132
the unit cost of the call volume of the h interruptible load type virtual power plant;
Figure BDA0002379633520000133
the unit cost of the call volume of the first load-suppressible virtual power plant.
When the number of the distributed energy sources is more than the total number of interruptible loads and translatable loads, the virtual power plant is a power source type virtual power plant;
when the number of interruptible loads is more than the total number of distributed energy sources and translatable loads, the virtual power plant is an interruptible load virtual power plant;
when the number of the translatable loads is more than the total number of the distributed energy sources and the interruptible loads, the virtual power plant is a translatable load type virtual power plant.
The solving module is specifically configured to: and solving the electricity selling optimization model by adopting a DICOPT solver.
Example 3
In embodiment 3 of the present invention, taking the modified IEEE33 node power distribution network system shown in fig. 2 as an example, based on the original network structure, node 1 is connected to a main network, Interruptible Load (IL) is connected to nodes 24 and 25, capacity is 150kW, Translatable Load (TL) is connected to nodes 5 and 6, the upper limit of the capacity is 30kW, the translation interval is 4h, distributed photovoltaic PV is connected to nodes 18 and 31, installed capacities are 200kW and 150kW, controllable DG is connected to nodes 7 and 21, maximum outputs are 200kW and 150kW, energy storage ESS is connected to nodes 17 and 33, maximum capacities are 80kW · h and 60 · h, and charging and discharging efficiencies are 95% and 90%, respectively. Assuming that 80% of electricity is purchased from the market at the day before, the purchase price is 0.4 yuan/(kW.h). The real-time market electricity price and the electricity selling price are determined by taking 1h as a time interval. The average electricity selling price of the electricity selling company to the user is 0.55 yuan/(kWh), and the range of the electricity selling price is set to [0.35, 0.85] yuan/(kWh).
Fig. 3-5 are VPP combination strategies for periods 8-11h, 18-21h, 12-17h, 22-23h, and 1-7h and 24h, respectively, where the period corresponding to fig. 3 is defined as a peak period, the period corresponding to fig. 4 is defined as a flat period, and the period corresponding to fig. 5 is defined as a valley period, and PSVPP, ILVPP and TLVPP are formed by combining them according to different Distributed Electric Resource (DER) and DL contract price amounts. The axis represents the call volume for participating in the combination type 3 VPP and not participating in the combination type 3 VPP. Comparing and analyzing the VPP combination conditions in different time periods in the figures 3-5, it can be seen that in a peak time period, the real-time price is relatively high, except for a part of distributed photovoltaics with large generating capacity, all the other distributed resources which do not reach the admittance amount participate in the combination, the output is maximum, and the electricity purchasing cost of the electricity selling company is reduced. In the normal period, the combination quantity of the IL is obviously reduced, the compensation cost for the interruptible load is reduced, and part of the interruptible loads meeting the admission requirement independently participate in scheduling. In the valley period, the real-time price is relatively low, the distributed resources cannot bring higher economic benefit to the electricity selling company, only the distributed photovoltaic which does not reach the admittance amount participates in the combination, and the distributed resources participating in the dispatching are the least.
Fig. 6-8 are schematic diagrams of declaration and actual calling of IL, TL and DER in the range of distribution network managed by the power selling company including the virtual power plant. Except that part of IL, photovoltaic output and DG output in the graphs of fig. 6 and fig. 8 reach the admission amount and participate in the scheduling, most of the admission amount does not reach the admission requirement, the VPP is used for combining the distributed resources which do not reach the admission requirement, so that the distributed resources can successfully participate in the transaction and be called, and in combination with the graphs of fig. 3-fig. 5, most of the resources are called in the peak time period and the flat time period, so that the higher electricity price of the real-time market in the time period is avoided, and the electricity purchasing cost of the electricity selling company is greatly reduced. The electricity price of the real-time market in the valley period is low, and the calling cost of distributed resources is low, so that even if the declaration quantity of part of resources in the valley period meets the admission requirement, the part of resources are not called.
Fig. 9 shows a schematic diagram of an energy storage operation condition within a distribution network range governed by an electricity selling company including a virtual power plant, wherein charging is performed in a valley time period with a low real-time electricity price, and discharging is performed in a peak time period with a high real-time electricity price, so that a load is supplied to the electricity selling company, electricity purchasing quantity in a real-time market is reduced, even electricity can be sold to the real-time market, and economic benefits of the electricity selling company are improved.
A schematic diagram of the electricity selling prices of users in the power distribution network range of the electricity selling company containing the virtual power plant is shown in fig. 10, the electricity selling prices basically change along with the price change of the real-time market, the electricity selling prices of the users are increased in the time period with higher real-time market prices, loss caused by overhigh electricity purchasing cost is avoided, a certain peak clipping effect is achieved because the electricity selling company calls IL, TL, DER and the like in a large amount in the peak time period and the average time period, the electricity purchasing amount of the electricity selling company is reduced in the time period with higher electricity price, and the electricity purchasing cost is reduced, so that the electricity selling price peak in fig. 10 is relatively shifted to the left compared with the real-time electricity price peak. The electricity selling price jump of 12h is because the electricity selling company needs to satisfy the average electricity selling price constraint negotiated with the user for selling electricity to the user.
A schematic diagram of a transaction situation of an electricity selling company containing a virtual power plant in a real-time market is shown in fig. 11, a part of the schematic diagram, which is larger than 0, is that the electricity selling company purchases electricity from the real-time market, and a part of the schematic diagram, which is smaller than 0, is that the electricity selling company sells electricity to the real-time market.
For convenience of description, each part of the above-described apparatus is separately described as being functionally divided into various modules or units. Of course, the functionality of the various modules or units may be implemented in the same one or more pieces of software or hardware when implementing the present application.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only intended to illustrate the technical solution of the present invention and not to limit the same, and a person of ordinary skill in the art can make modifications or equivalent substitutions to the specific embodiments of the present invention with reference to the above embodiments, and any modifications or equivalent substitutions which do not depart from the spirit and scope of the present invention are within the protection scope of the present invention as claimed in the appended claims.

Claims (13)

1. A power selling method based on a virtual power plant is characterized by comprising the following steps:
acquiring respective quantities of distributed energy sources, interruptible loads and translatable loads in a virtual power plant;
inputting the respective quantities of the distributed energy sources, the interruptible loads and the translatable loads in the virtual power plant into a pre-constructed electricity selling optimization model for solving to obtain the states of the distributed energy sources, the interruptible loads and the translatable loads participating in the virtual power plant, and the active output of the distributed energy sources, the interrupting load quantity of the interruptible loads and the translating load quantity of the translatable loads;
the electricity selling optimization model is constructed based on the relation among the load demand, the distributed energy sources and the adjustable load in the governed power distribution network and by taking the maximum volume of trades of the electricity selling company as a target.
2. The virtual power plant based electricity selling method according to claim 1, wherein the building of the electricity selling optimization model comprises:
dividing a virtual power plant into distributed energy and an adjustable load, and dividing the adjustable load into an interruptible load and a translatable load;
based on the respective quantity of distributed energy sources, interruptible loads and translatable loads in the virtual power plant and the relationship among load demands, distributed energy sources and adjustable loads in the governed power distribution network, constructing an objective function with the maximization of the volume of charge of a power selling company as a target;
and determining constraint conditions corresponding to the objective function, wherein the constraint conditions comprise power selling income constraint, power flow constraint, interruptible load quantity constraint, translatable load quantity constraint, distributed energy output constraint, virtual power plant calling constraint, distribution network operation safety constraint, energy storage operation constraint, real-time market transaction constraint and power selling price constraint.
3. The virtual power plant based electricity selling method of claim 2, wherein the objective function is as follows:
Figure FDA0002379633510000011
wherein F is the volume of the electric power selling company; delta T is the calling time interval of the virtual power plant; t is the total duration; d is the number of power supply type virtual power plants;h is the number of interruptible load type virtual power plants; l is the number of load-type virtual power plants capable of translating; e is the number of distributed energy sources; k is the number of interruptible loads; g is the number of translatable loads;
Figure FDA0002379633510000012
the active power output of the e-th distributed energy source in the t period;
Figure FDA0002379633510000013
a variable of 0 to 1, which represents the state that the e distributed energy source participates in the virtual power plant;
Figure FDA0002379633510000014
an interrupt load amount for a kth interruptible load for a period t;
Figure FDA0002379633510000015
a variable of 0-1, which represents the state of the kth interruptible load participating in the virtual plant;
Figure FDA0002379633510000016
the translational load amount of the g-th translatable load in the t period;
Figure FDA0002379633510000017
is a 0-1 variable representing the state of the g translatable load participating in the virtual plant.
4. The virtual power plant based electricity selling method of claim 3, wherein the electricity selling profit constraint is as follows:
Figure FDA0002379633510000021
in the formula, CminThe minimum income of the power selling company all day; n is the number of distribution network nodes; m is the number of virtual power plants;
Figure FDA0002379633510000022
the price of electricity sold in the time period t;
Figure FDA0002379633510000023
is the active load of the node i in the period t;
Figure FDA0002379633510000024
the calling quantity of the mth virtual power plant in the t period is obtained;
Figure FDA0002379633510000025
for the day-ahead market price of electricity, Pt dPurchasing electric quantity for the market at the day before;
Figure FDA0002379633510000026
for trading electricity prices in real time, Pt rTrading the electricity in real time;
Figure FDA0002379633510000027
paying for the unit of the e distributed energy;
Figure FDA0002379633510000028
compensating the cost for the unit of load interruption for the kth interruptible load;
Figure FDA0002379633510000029
a unit compensation cost for load translation for the g-th translatable load; cVPPAnd the virtual power plant calling cost is the combined distributed energy and adjustable load.
5. The virtual power plant based electricity selling method of claim 4, wherein the distribution energy source and the adjustable load are combined to obtain the calling cost C of the virtual power plantVPPSatisfies the following conditions:
Figure FDA00023796335100000210
in the formula (I), the compound is shown in the specification,
Figure FDA00023796335100000211
unit cost for the calling amount of the d-th PSVPP;
Figure FDA00023796335100000212
the unit cost of the call volume of the h interruptible load type virtual power plant;
Figure FDA00023796335100000213
the unit cost of the call volume of the first load-suppressible virtual power plant.
6. The virtual power plant-based electricity selling method according to claim 3,
when the number of the distributed energy sources is more than the total number of interruptible loads and translatable loads, the virtual power plant is a power source type virtual power plant;
when the number of the interruptible loads is more than the total number of the distributed energy sources and the translatable loads, the virtual power plant is an interruptible load virtual power plant;
and when the quantity of the translatable loads is more than the total quantity of the distributed energy sources and the interruptible loads, the virtual power plant is a translatable load type virtual power plant.
7. The virtual power plant based electricity selling method according to claim 1, wherein the step of inputting the respective quantities of distributed energy sources, interruptible loads and translatable loads in the virtual power plant into a pre-constructed electricity selling optimization model for solving comprises the following steps:
and solving the electricity selling optimization model by adopting a DICOPT solver.
8. An electricity selling device based on a virtual power plant is characterized by comprising:
the acquisition module is used for acquiring the respective quantity of distributed energy sources, interruptible loads and translatable loads in the virtual power plant;
the solving module is used for inputting the respective quantities of the distributed energy sources, the interruptible loads and the translatable loads in the virtual power plant into a pre-constructed electricity selling optimization model for solving to obtain the states of the distributed energy sources, the interruptible loads and the translatable loads participating in the virtual power plant, the active output of the distributed energy sources, the interrupting load quantity of the interruptible loads and the translating load quantity of the translatable loads;
the electricity selling optimization model is constructed based on the relation among the load demand, the distributed energy sources and the adjustable load in the governed power distribution network and by taking the maximum volume of trades of the electricity selling company as a target.
9. The virtual power plant-based electricity selling device according to claim 8, further comprising a modeling module, the modeling module being specifically configured to:
dividing a virtual power plant into distributed energy and an adjustable load, and dividing the adjustable load into an interruptible load and a translatable load;
based on the respective quantity of distributed energy sources, interruptible loads and translatable loads in the virtual power plant and the relationship among load demands, distributed energy sources and adjustable loads in the governed power distribution network, constructing an objective function with the maximization of the volume of charge of a power selling company as a target;
and determining constraint conditions corresponding to the objective function, wherein the constraint conditions comprise power selling income constraint, power flow constraint, interruptible load quantity constraint, translatable load quantity constraint, distributed energy output constraint, virtual power plant calling constraint, distribution network operation safety constraint, energy storage operation constraint, real-time market transaction constraint and power selling price constraint.
10. The virtual power plant-based electricity selling device according to claim 9, wherein the objective function constructed by the modeling module is as follows:
Figure FDA0002379633510000031
wherein F is the volume of the electric power selling company; delta T is the calling time interval of the virtual power plant; t is the total duration; d is the number of power supply type virtual power plants; h is the number of interruptible load type virtual power plants; l is the number of load-type virtual power plants capable of translating; e is the number of distributed energy sources; k is the number of interruptible loads; g is the number of translatable loads;
Figure FDA0002379633510000032
the active power output of the e-th distributed energy source in the t period;
Figure FDA0002379633510000033
a variable of 0 to 1, which represents the state that the e distributed energy source participates in the virtual power plant;
Figure FDA0002379633510000034
an interrupt load amount for a kth interruptible load for a period t;
Figure FDA0002379633510000035
a variable of 0-1, which represents the state of the kth interruptible load participating in the virtual plant;
Figure FDA0002379633510000036
the translational load amount of the g-th translatable load in the t period;
Figure FDA0002379633510000037
is a 0-1 variable representing the state of the g translatable load participating in the virtual plant.
11. The virtual power plant-based electricity sales device of claim 10, wherein the modeling module determines the electricity sales revenue constraint as follows:
Figure FDA0002379633510000038
in the formula, CminTo sellThe minimum revenue of the electric company throughout the day; n is the number of distribution network nodes; m is the number of virtual power plants;
Figure FDA0002379633510000041
the price of electricity sold in the time period t;
Figure FDA0002379633510000042
is the active load of the node i in the period t;
Figure FDA0002379633510000043
the calling quantity of the mth virtual power plant in the t period is obtained;
Figure FDA0002379633510000044
for the day-ahead market price of electricity, Pt dPurchasing electric quantity for the market at the day before;
Figure FDA0002379633510000045
for trading electricity prices in real time, Pt rTrading the electricity in real time;
Figure FDA0002379633510000046
paying for the unit of the e distributed energy;
Figure FDA0002379633510000047
compensating the cost for the unit of load interruption for the kth interruptible load;
Figure FDA0002379633510000048
a unit compensation cost for load translation for the g-th translatable load; cVPPThe virtual power plant calling cost after the combination of distributed energy and adjustable load is met:
Figure FDA0002379633510000049
in the formula (I), the compound is shown in the specification,
Figure FDA00023796335100000410
unit cost for the calling amount of the d-th PSVPP;
Figure FDA00023796335100000411
the unit cost of the call volume of the h interruptible load type virtual power plant;
Figure FDA00023796335100000412
the unit cost of the call volume of the first load-suppressible virtual power plant.
12. The virtual power plant-based electricity selling device according to claim 10,
when the number of the distributed energy sources is more than the total number of interruptible loads and translatable loads, the virtual power plant is a power source type virtual power plant;
when the number of the interruptible loads is more than the total number of the distributed energy sources and the translatable loads, the virtual power plant is an interruptible load virtual power plant;
and when the quantity of the translatable loads is more than the total quantity of the distributed energy sources and the interruptible loads, the virtual power plant is a translatable load type virtual power plant.
13. The virtual power plant-based electricity selling device according to claim 8, wherein the solving module is specifically configured to:
and solving the electricity selling optimization model by adopting a DICOPT solver.
CN202010079112.6A 2020-02-03 2020-02-03 Electricity selling method and device based on virtual power plant Pending CN111339637A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010079112.6A CN111339637A (en) 2020-02-03 2020-02-03 Electricity selling method and device based on virtual power plant

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010079112.6A CN111339637A (en) 2020-02-03 2020-02-03 Electricity selling method and device based on virtual power plant

Publications (1)

Publication Number Publication Date
CN111339637A true CN111339637A (en) 2020-06-26

Family

ID=71183875

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010079112.6A Pending CN111339637A (en) 2020-02-03 2020-02-03 Electricity selling method and device based on virtual power plant

Country Status (1)

Country Link
CN (1) CN111339637A (en)

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113435651A (en) * 2021-06-30 2021-09-24 南京理工大学 Three-stage multi-subject optimized game method and system considering user comfort for virtual power plant
CN114154910A (en) * 2021-12-08 2022-03-08 国网山西省电力公司电力科学研究院 Multi-energy distributed resource-oriented virtual power plant multistage polymerization method and device and storage medium
CN114205381A (en) * 2021-11-29 2022-03-18 国网福建省电力有限公司经济技术研究院 System comprising virtual power plant load classification, resource modeling and participation in electric power market transaction
CN114243779A (en) * 2021-12-22 2022-03-25 国网江苏省电力有限公司营销服务中心 User adjustable load resource demand response method and system based on virtual power plant

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113435651A (en) * 2021-06-30 2021-09-24 南京理工大学 Three-stage multi-subject optimized game method and system considering user comfort for virtual power plant
CN114205381A (en) * 2021-11-29 2022-03-18 国网福建省电力有限公司经济技术研究院 System comprising virtual power plant load classification, resource modeling and participation in electric power market transaction
CN114205381B (en) * 2021-11-29 2024-05-14 国网福建省电力有限公司经济技术研究院 System comprising virtual power plant load classification, resource modeling and participation in electric power market transaction
CN114154910A (en) * 2021-12-08 2022-03-08 国网山西省电力公司电力科学研究院 Multi-energy distributed resource-oriented virtual power plant multistage polymerization method and device and storage medium
WO2023103862A1 (en) * 2021-12-08 2023-06-15 国网山西省电力公司电力科学研究院 Multi-energy distributed resource-oriented multi-level aggregation method and apparatus for virtual power plant, and storage medium
CN114243779A (en) * 2021-12-22 2022-03-25 国网江苏省电力有限公司营销服务中心 User adjustable load resource demand response method and system based on virtual power plant
CN114243779B (en) * 2021-12-22 2024-03-08 国网江苏省电力有限公司营销服务中心 User adjustable load resource demand response method and system based on virtual power plant

Similar Documents

Publication Publication Date Title
Kong et al. Bi-level multi-time scale scheduling method based on bidding for multi-operator virtual power plant
Shayegan-Rad et al. Day-ahead scheduling of virtual power plant in joint energy and regulation reserve markets under uncertainties
CN110188950B (en) Multi-agent technology-based optimal scheduling modeling method for power supply side and demand side of virtual power plant
CN111339637A (en) Electricity selling method and device based on virtual power plant
Zhou et al. Time-of-use pricing model based on power supply chain for user-side microgrid
CN108510212A (en) A kind of the distributed energy dispatching method and system of interactive mode energy resource system
KR102672397B1 (en) System and method for agencying power trading system and method
CN103532157A (en) Electric-energy scheduling method for power grid system integrated into energy storage equipment
CN109376970B (en) Dynamic real-time electricity price mechanism forming method and system suitable for energy Internet
CN112068436A (en) Layered and distributed control method and system for comprehensive energy system of industrial park
CN112671022A (en) Optical storage charging station capacity optimal configuration method, system, terminal and storage medium
Sakr et al. Techno-economic assessment of consumers' participation in the demand response program for optimal day-ahead scheduling of virtual power plants
Tan et al. Joint scheduling optimization of virtual power plants and equitable profit distribution using shapely value theory
CN116402223A (en) Cooperative scheduling method, system and equipment for power distribution network
CN111445154A (en) Power market resource self-scheduling optimization method, system and equipment
Park et al. Prosumer energy management considering contract with consumers under progressive pricing policy
CN115062835A (en) Active power distribution network distributed resource optimization scheduling method based on virtual power plant
Li et al. A consumer-oriented incentive strategy for EV charging in multiareas under stochastic risk-constrained scheduling framework
Kong et al. Independence enhancement of distributed generation systems by integrating shared energy storage system and energy community with internal market
CN111798070A (en) Configuration method and device of user side optical storage system
Kou et al. Game-theoretic approach for smartgrid energy trading with microgrids during restoration
Heydarian-Forushani et al. A proactive strategy for virtual power plants including multiple private owners equipped with energy storages
Silva et al. Aggregation of prosumers in the context of demand response and distributed generation remuneration and scheduling
CN112381399A (en) Day-ahead and real-time clearing method and system for energy system
CN112102047A (en) Virtual power plant optimization combination bidding method, device, equipment and storage medium

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination