CN117318166B - Multi-virtual power plant low-carbon scheduling method based on cooperative game under consideration of fault risk - Google Patents
Multi-virtual power plant low-carbon scheduling method based on cooperative game under consideration of fault risk Download PDFInfo
- Publication number
- CN117318166B CN117318166B CN202310997372.5A CN202310997372A CN117318166B CN 117318166 B CN117318166 B CN 117318166B CN 202310997372 A CN202310997372 A CN 202310997372A CN 117318166 B CN117318166 B CN 117318166B
- Authority
- CN
- China
- Prior art keywords
- virtual power
- power plant
- carbon
- virtual
- scheduling
- 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.)
- Active
Links
- 229910052799 carbon Inorganic materials 0.000 title claims abstract description 89
- 238000000034 method Methods 0.000 title claims abstract description 48
- 230000005540 biological transmission Effects 0.000 claims abstract description 68
- 238000010248 power generation Methods 0.000 claims abstract description 17
- 230000002776 aggregation Effects 0.000 claims abstract description 14
- 238000004220 aggregation Methods 0.000 claims abstract description 14
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 claims description 46
- 230000005611 electricity Effects 0.000 claims description 40
- 238000004146 energy storage Methods 0.000 claims description 34
- 230000009977 dual effect Effects 0.000 claims description 18
- 238000004590 computer program Methods 0.000 claims description 16
- 230000008569 process Effects 0.000 claims description 15
- 230000006870 function Effects 0.000 claims description 13
- 238000009826 distribution Methods 0.000 claims description 11
- 239000000446 fuel Substances 0.000 claims description 11
- 230000009467 reduction Effects 0.000 claims description 11
- 238000007599 discharging Methods 0.000 claims description 9
- 230000009471 action Effects 0.000 claims description 6
- 230000004044 response Effects 0.000 claims description 6
- 238000003860 storage Methods 0.000 claims description 6
- 238000004364 calculation method Methods 0.000 claims description 4
- 238000005452 bending Methods 0.000 claims description 3
- 238000012546 transfer Methods 0.000 claims description 3
- 239000002699 waste material Substances 0.000 claims description 3
- RWSOTUBLDIXVET-UHFFFAOYSA-N Dihydrogen sulfide Chemical compound S RWSOTUBLDIXVET-UHFFFAOYSA-N 0.000 claims description 2
- 238000010586 diagram Methods 0.000 description 13
- 238000012545 processing Methods 0.000 description 4
- 238000012360 testing method Methods 0.000 description 4
- 230000000694 effects Effects 0.000 description 3
- 238000010521 absorption reaction Methods 0.000 description 2
- 230000000903 blocking effect Effects 0.000 description 2
- 238000005265 energy consumption Methods 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000000295 complement effect Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- 230000002747 voluntary effect Effects 0.000 description 1
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
- G06Q10/06—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
- G06Q10/06313—Resource planning in a project environment
-
- 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
- G06Q10/0635—Risk analysis of enterprise or organisation activities
-
- 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
- G06Q10/067—Enterprise or organisation modelling
-
- 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
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02H—EMERGENCY PROTECTIVE CIRCUIT ARRANGEMENTS
- H02H7/00—Emergency protective circuit arrangements specially adapted for specific types of electric machines or apparatus or for sectionalised protection of cable or line systems, and effecting automatic switching in the event of an undesired change from normal working conditions
- H02H7/26—Sectionalised protection of cable or line systems, e.g. for disconnecting a section on which a short-circuit, earth fault, or arc discharge has occured
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
- H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
- H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/22—The renewable source being solar energy
- H02J2300/24—The renewable source being solar energy of photovoltaic origin
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/28—The renewable source being wind energy
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Tourism & Hospitality (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Marketing (AREA)
- Physics & Mathematics (AREA)
- Educational Administration (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Game Theory and Decision Science (AREA)
- Power Engineering (AREA)
- Development Economics (AREA)
- Health & Medical Sciences (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Life Sciences & Earth Sciences (AREA)
- Biodiversity & Conservation Biology (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a multi-virtual power plant low-carbon scheduling method based on cooperative game under consideration of fault risk, which solves the problem of optimal scheduling of multi-virtual power plants when a power transmission network breaks down in extreme weather. And expanding optimal scheduling of multiple virtual power plants accessed in the power transmission network by applying a cooperative game idea, and establishing a multiple virtual power plant cooperative game model under extreme weather by considering the allocation of virtual power plants to the power transmission network risks. And carrying out fine modeling on the virtual power plant aggregation unit, responding to other virtual power plants in the alliance based on renewable energy power generation information under multiple time sections according to the basic theory of cooperative game, further establishing a multi-virtual power plant low-carbon scheduling double-layer model considering electricity-carbon transaction, taking typhoons as the representative of extreme weather, and calculating the line fault rate to solve. The invention provides a low-carbon collaborative scheduling strategy of the virtual power plant in extreme weather, thereby providing support for reasonable decision-making of scheduling personnel and having certain engineering use value.
Description
Technical Field
The invention relates to a multi-virtual power plant low-carbon scheduling method based on cooperative game under consideration of fault risk, and belongs to the technical field of power system scheduling.
Background
Aiming at the safety problem existing in the running of the virtual power plant (Virtual Power Plant, VPP), some current researches analyze and calculate the uncertainty risk of the virtual power plant, such as the randomness of the output of renewable energy sources in the virtual power plant, the fluctuation of the energy end load and the like, and further, by applying a demand response means, the targets of resource aggregation, power curtailment, low-carbon emission reduction and the like can be realized by optimizing and configuring demand side response resources according to market change and demand quick adjustment strategies accurately. However, the research is mostly based on the multi-dimensional uncertainty in the virtual power plant, and the influence generated by the dispatching operation of the virtual power plant is analyzed in the disaster scene of the power grid line caused by typhoons and other extreme weather, so that the remote consumption of renewable energy sources in the virtual power plant is restricted, and the enthusiasm of clean energy sources in markets is difficult to be exerted; in addition, the power industry has increasingly strong requirements for low-carbon transformation, brings new challenges to low-carbon operation of a power grid, and has a great need to solve the problem of coordinating multiple virtual power plants to perform low-carbon scheduling on the power grid level.
Disclosure of Invention
The technical problems to be solved by the invention are as follows: the multi-virtual power plant low-carbon scheduling method based on the cooperative game under the consideration of the fault risk can provide effective support for improving the low-carbon performance of the multi-virtual power plant in different scenes.
The invention adopts the following technical scheme for solving the technical problems:
a multi-virtual power plant low-carbon scheduling method based on cooperative game under consideration of fault risk comprises the following steps:
Step S1, carrying out fine modeling on a virtual power plant aggregation unit, constructing a multi-virtual power plant low-carbon scheduling double-layer model considering electric-carbon transaction, and guiding the multi-virtual power plant to form a cooperative game alliance;
S2, taking typhoons as representative of extreme weather, simulating a scene of broken line faults of a power transmission network under the action of the extreme weather, and constructing a line fault model under the extreme weather;
And step S3, based on the step S1 and the step S2, a multi-virtual power plant low-carbon scheduling model based on cooperative game under the consideration of fault risk is established, and a double-layer model is converted into a mixed integer linear programming problem and solved based on Karush-Kuhn-Tucker conditions and a strong dual principle, so that each virtual power plant scheduling scheme under the form of cooperative game is obtained.
As a preferable scheme of the invention, in the step S1, in the fine modeling of the virtual power plant aggregation unit, an expression of an interruptible load model inside the virtual power plant is as follows:
In the method, in the process of the invention, The electricity purchasing and selling states of the virtual power plant on the node j are respectively the virtual power plant on the node i at the moment t; and the electricity purchasing and selling states of the virtual power plant on the node i at the moment t and the power transmission network are respectively.
As a preferable scheme of the invention, in the step S1, in the fine modeling of the virtual power plant aggregation unit, the power balance constraint expression in the virtual power plant is as follows:
In the method, in the process of the invention, For a normal load within the virtual power plant,The output and the waste power of renewable energy sources in the virtual power plant on the node i at the moment t are respectively,Respectively purchasing and selling electricity quantity between the virtual power plant and the power transmission network,The purchase and sales amounts of electricity between the virtual power plant on the node i and the virtual power plant on the node j are respectively,Respectively the charge and discharge amounts of the stored energy,As a response to interruptible loads in the virtual power plant,As the power generation amount of the fuel cell in the virtual power plant,The maximum power of the virtual power plant and the power transmission network for purchasing and selling electricity,The maximum power of the power purchased by the virtual power plant and the power sold by other virtual power plants,The upper and lower limits of the fuel cell output are respectively.
As a preferable scheme of the invention, in the step S1, in the fine modeling of the virtual power plant aggregation unit, the energy storage model expression in the virtual power plant is as follows:
xi,t+yi,t＝1
wherein x i,t、yi,t is an energy storage running state variable of the virtual power plant on a node i at the moment t, x i,t =1 represents that the energy storage is in a charging state, and y i,t =1 represents that the energy storage is in a discharging state; respectively charging and discharging power of the energy storage, Respectively the rated charge and discharge power of the stored energy,Respectively the energy storage charging and discharging efficiency, delta t=1h is the interval time,Respectively the maximum charge and discharge quantity of the stored energy in the period t,And r i are the self-discharge electric quantity and the self-discharge rate of the stored energy respectively,AndThe electric quantity stored in the energy storage at the initial time and the t time respectively,For the amount of electricity stored in the energy storage at time t-1, The SOC i,max、SOCi,min is the maximum and minimum states of charge of the stored energy respectively.
As a preferred solution of the present invention, the multi-virtual power plant low-carbon dispatch bilayer model considering the electricity-carbon transaction in step S1 specifically includes:
The upper layer objective function is from the perspective of the virtual power plant, and the objective function of the multi-virtual power plant transaction model is as follows:
Wherein T, N respectively represents day scheduling time and the number of virtual power plants, pi i,t is the marginal electricity price of a node i at a moment t, c b、cs is the purchase and sale electricity price when the virtual power plants transact with a power transmission network, beta is the electricity interruption compensation contract electricity price provided by the virtual power plants for interruption load users, gamma and mu are the charge and discharge electricity price of energy stored in the virtual power plants, c DG is the unit power generation cost of a fuel cell, and c E is the transaction price coefficient of a carbon market;
for the carbon emission amount of the virtual power plant on the node i at the time t to participate in the carbon market transaction, the expression is as follows:
In the method, in the process of the invention, The carbon emission amount of the virtual power plant on the node i at the moment t participating in the carbon market transaction,AndRespectively represents the actual carbon emission quantity, the initial carbon emission quota and the wind-solar nuclear evidence emission reduction quantity,The wind power and photovoltaic power output are respectively, χ c is the carbon emission intensity of unit output, χ m is the initial distribution coefficient of unit electric quantity carbon emission, χ g is the calculation coefficient of wind-solar nuclear evidence emission reduction;
the underlying objective function requires the grid to purchase the least total cost of power:
In the method, in the process of the invention, AndRespectively competing for interruptible load in the virtual power plant on the node i at the moment t, energy storage and a traditional thermal generator set in the power grid,The output of the traditional thermal power generating unit;
The power balance constraints of the grid are as follows:
Where L i,t is the load on node i at time t, lambda t is a dual variable related to power balance at time t, Maximum and minimum limits of interruptible load output in the virtual power plant,Respectively the maximum and minimum limit values of energy storage discharge in the virtual power plant,The maximum and minimum limits of the traditional generator output in the virtual power plant are respectively,AndIn order to have a dual variable, the two variables,AndIn order to have a dual variable, the two variables,AndAs dual variables;
the power flow constraint of the power transmission line is as follows:
where Lim l denotes the transmission capacity limit of line l, AndIs a dual variable of t period related to the transmission capacity constraint of line l, GSF l,i is a power transfer distribution factor.
As a preferred scheme of the present invention, in the step S1, the cooperative game of multiple virtual power plants follows the principles of nearby combination and wind-solar complementation, that is, virtual power plants preferentially consider other virtual power plants close to the geographic location to form a alliance, and simultaneously, it is required that virtual power plants with wind power generation and photovoltaic power generation as distributed renewable energy sources must exist in each alliance.
As a preferable mode of the present invention, in the step S2, in which typhoons are used as representative of extreme weather, wind speeds and directions of points on a typhoon path are as follows:
Wherein v is typhoon speed, R is distance from the power transmission line to the typhoon center, R max is distance from the cyclone center to the strongest wind band, Is the wind speed in the center of the cyclone.
As a preferable scheme of the invention, in the scene of simulating the disconnection fault of the power transmission network under the action of extreme weather in the step S2, the wind load borne on the power transmission network line is calculated as follows:
Wherein N l is wind load, v is typhoon wind speed, d is wire outer diameter, and θ is included angle between wind direction and power transmission line;
The failure rates of the wires and the electric poles in the transmission network are respectively as follows:
Wherein p fl and p fp are unreliable running probabilities of a wire and an electric pole respectively, sigma g is stress born by a wire section, M T is bending moment born by a pole root, mu l and delta l are mean value and standard deviation of strength of a wire element respectively, mu p and delta p are mean value and standard deviation of strength of the electric pole element respectively, and sigma l and M p are parameters;
the fault rate of the overhead distribution line is as follows:
Wherein p fl,l (v) is the failure rate of the line l, m 1 and m 2 are the number of electric poles and the number of wire stages of the line l respectively, and p fp,k,l(v)、pfl,h,l (v) is the failure rate of the kth electric pole and the h wire of the line l respectively.
A computer device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, which when executed implements the steps of the collaborative game-based multi-virtual power plant low-carbon scheduling method taking into account risk of failure as described above.
A computer readable storage medium storing a computer program which when executed by a processor implements the steps of a collaborative game based multi-virtual power plant low-carbon scheduling method taking into account risk of failure as described above.
Compared with the prior art, the technical scheme provided by the invention has the following technical effects:
1. According to the invention, the cooperative low-carbon scheduling technical characteristics of the multi-virtual power plant based on the cooperative game under the consideration of the fault risk are adopted, and the cooperative low-carbon scheduling scheme of the multi-virtual power plant double-layer model in the power grid can be solved, so that the multi-energy complementation of renewable energy sources in the alliance can be well realized, the low carbon performance of the system and the capability of resisting the fault risk of broken wires are improved, and therefore, effective support is provided for correct decision-making of scheduling personnel.
2. The scheduling method designed by the invention is used for allocating the risk of the power grid, improving the wind-solar energy absorption rate, playing a good guiding role in improving the renewable energy source absorption in the power distribution network, and having a certain practical value.
Drawings
FIG. 1 is a flow chart of the steps of the scheduling method of the present invention;
FIG. 2 is a diagram of a multi-virtual power plant trading framework;
FIG. 3 is a diagram of a modified IEEE118 node test system;
FIG. 4 is a diagram of an actual geographical wiring of the 118 node system;
FIG. 5 is a graph of low-carbon dispatch results for multiple virtual power plants;
FIG. 6 is a diagram of the results of a multi-virtual power plant cooperative game dispatch;
FIG. 7 is a graph of scheduling results for independent operation of multiple virtual power plants in a fault scenario;
Fig. 8 is a diagram of a scheduling result of the multi-VPP consideration cooperative game in a failure scenario.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings. The embodiments described below by referring to the drawings are exemplary only for explaining the present invention and are not to be construed as limiting the present invention.
The invention provides a multi-virtual power plant low-carbon scheduling method based on cooperative game under the consideration of fault risk, which comprises the following specific steps as shown in fig. 1:
Carrying out fine modeling on the virtual power plant aggregation unit, constructing a multi-virtual power plant low-carbon scheduling double-layer model considering electric-carbon transaction, and guiding the multi-virtual power plant to form a cooperative game alliance;
The typhoon is used as the representative of extreme weather, the scene of broken line fault of the power transmission network under the action of the extreme weather is simulated, and the influence of the typhoon on the dispatching of the multiple virtual power plants is evaluated;
Based on a line fault model under extreme weather combined by a multi-virtual power plant cooperative game model, a multi-virtual power plant low-carbon scheduling model based on cooperative games under the consideration of fault risk is established, the double-layer scheduling model is converted into a mixed integer linear programming problem based on Karush-Kuhn-Tucker conditions and a strong dual principle and solved, and then each virtual power plant scheduling scheme under the cooperative game form is obtained, so that multi-energy complementation of renewable energy sources in a alliance is realized, and the low carbon performance of the system and the capability of resisting broken line fault risks are improved.
In the fine modeling of the virtual power plant aggregation unit, the expression of the interruptible load model inside the virtual power plant is as follows:
In the method, in the process of the invention, The electricity purchasing and selling states of the virtual power plant on the node j are respectively the virtual power plant on the node i at the moment t; when (when)The virtual power plant on the node i at the moment t is shown to purchase power to the virtual power plant on the node j; when (when)Representing that the virtual power plant on the node i sells electricity to the virtual power plant on the node j; when (when)Indicating that no power transaction is performed between the virtual power plant on node i and the virtual power plant on node j at time t. Also, the process of the present invention is,And the electricity purchasing and selling states of the virtual power plant on the node i at the moment t and the power transmission network are respectively.
In the fine modeling of the virtual power plant aggregation unit, the power balance constraint expression inside the virtual power plant is as follows:
In the method, in the process of the invention, For a normal load within the virtual power plant,The output and the waste power of renewable energy sources in the virtual power plant on the node i at the moment t are respectively,Respectively purchasing and selling electricity quantity between the virtual power plant and the power transmission network,The purchase and sales amounts of electricity between the virtual power plant on the node i and the virtual power plant on the node j are respectively,Respectively the charge and discharge amounts of the stored energy,As a response to interruptible loads in the virtual power plant,As the power generation amount of the fuel cell in the virtual power plant,The maximum power of the virtual power plant and the power transmission network for purchasing and selling electricity,The maximum power of the power purchased by the virtual power plant and the power sold by other virtual power plants,The upper and lower limits of the fuel cell output are respectively.
In the fine modeling of the virtual power plant aggregation unit, the expression of the internal energy storage model of the virtual power plant is as follows:
xi,t+yi,t＝1
wherein x i,t、yi,t is an energy storage running state variable of the virtual power plant on a node i at the moment t, x i,t =1 represents that the energy storage is in a charging state, and y i,t =1 represents that the energy storage is in a discharging state; respectively charging and discharging power of the energy storage, Respectively the rated charge and discharge power of the stored energy,Respectively the energy storage charging and discharging efficiency, delta t=1h is the interval time,Respectively the maximum charge and discharge quantity of the stored energy in the period t,And r i are the self-discharge electric quantity and the self-discharge rate of the stored energy respectively,AndThe electric quantity stored in the energy storage at the initial time and the t time respectively,For the amount of electricity stored in the energy storage at time t-1, The SOC i,max、SOCi,min is the maximum and minimum states of charge of the stored energy respectively.
The multi-virtual power plant low-carbon scheduling double-layer model considering the electricity-carbon transaction is specifically as follows:
1) Carbon market transaction model
The carbon market model adopts a mode of combining initial allocation of carbon emission quota and voluntary emission reduction of nuclear evidence. The initial allocation of carbon emission is a way of issuing free quota to the subject with relief obligation according to a certain proportion of total carbon emission according to relevant regulations, and the reasonable allocation of the initial carbon emission quota is beneficial to realizing the optimal allocation of resources. The nuclear evidence emission reduction of wind and light refers to counteracting the carbon emission participating in the carbon market by quantifying the carbon emission reduction effect of renewable energy sources.
The carbon emissions involved in the carbon market can be expressed as:
In the method, in the process of the invention, The carbon emission amount of the virtual power plant on the node i at the moment t participating in the carbon market transaction,AndRespectively represents the actual carbon emission quantity, the initial carbon emission quota and the wind-solar nuclear evidence emission reduction quantity,The wind power and photovoltaic power output are respectively, χ c is the carbon emission intensity of unit output, χ m is the initial distribution coefficient of unit electric quantity carbon emission, and χ g is the calculation coefficient of wind-light nuclear evidence emission reduction.
The carbon emission amount participating in the running cost of the carbon market is the difference value between the actual carbon emission amount and the initial quota and the nuclear evidence emission reduction amount.
2) Low-carbon scheduling double-layer model of multi-virtual power plant
The present invention assumes that the scheduling of virtual power plants is strategic, i.e., the virtual power plants are participants in the power market activity, not just the owners of the prices.
From the perspective of the virtual power plant, the objective function of the multi-virtual power plant transaction model is:
Wherein T, N respectively represents day scheduling time and virtual power plant quantity, pi i,t is marginal electricity price of node i at t time, c b、cs is electricity purchasing and selling price when the virtual power plant and a power transmission network trade, beta is electricity interruption compensation contract electricity price provided by the virtual power plant for an interruption load user, gamma and mu are charge and discharge electricity price stored in the virtual power plant, c DG is unit power generation cost of a fuel cell, and c E is carbon market trade price coefficient.
The underlying objective function is the market clearing process, where the total cost of grid purchased power is required to be the lowest:
In the method, in the process of the invention, AndRespectively competing for interruptible load in the virtual power plant on the node i at the moment t, energy storage and a traditional thermal generator set in the power grid,Is the output of the traditional thermal power generating unit.
The power balance constraints of the grid are as follows:
Where L i,t is the load on node i at time t, lambda t is a dual variable related to power balance at time t, Maximum and minimum limits of interruptible load output in the virtual power plant,Respectively the maximum and minimum limit values of energy storage discharge in the virtual power plant,The maximum and minimum limits of the traditional generator output in the virtual power plant are respectively,AndIn order to have a dual variable, the two variables,AndIn order to have a dual variable, the two variables,AndIs a dual variable.
The power flow constraint of the power transmission line is as follows:
where Lim l denotes the transmission capacity limit of line l, AndIs a dual variable of t period related to the transmission capacity constraint of line l, GSF l,i is a power transfer distribution factor.
The Lagrangian function based on the above equation is:
Further obtaining the marginal electricity price of the i node at the t moment as follows:
the virtual power plants form a cooperative game alliance mode as follows:
When the virtual power plant operates independently, only electric power transaction is carried out with the power transmission network, and serious wind and light abandoning phenomena can be generated in the virtual power plant under the condition that electric power transaction limits exist. However, when the virtual power plant with surplus electric quantity and the virtual power plant with loss electric quantity exist in a certain area at the same time, the two types of virtual power plants can realize electric energy complementation through cooperative matching, so that the output loss of renewable energy sources is avoided, and meanwhile, the high cost caused by the high-price electricity purchasing mode and the low-price electricity selling mode of the virtual power plant and a power transmission network can be reduced to a certain extent. Wherein, any one virtual power plant realizes mutual electricity and cost minimization through electric power trade with other virtual power plants or a transmission network.
Collaborative gaming refers to a type of game in which participants optimize the benefits of a coalition by executing a constrained protocol. The cooperative game of virtual power plants in a power transmission network follows the principle of 'near combination and wind-solar complementation', namely, the virtual power plants take precedence over other virtual power plants which are similar to the geographic position to form alliances, and in order to consume renewable energy sources as much as possible, virtual power plants taking wind power generation and photovoltaic power generation as distributed renewable energy sources must exist in each alliance at the same time in consideration of different wind-solar output large-generation periods. When a plurality of virtual power plants form a alliance and are in cooperative operation, the overall cost of the alliance can be reduced through power interaction among the virtual power plants. FIG. 2 is a diagram of a multi-virtual power plant trading framework.
In the typhoon as representative of extreme weather, the wind speed and direction at each point in the typhoon path are as follows:
Wherein v is typhoon speed, R is distance from the power transmission line to the typhoon center, R max is distance from the cyclone center to the strongest wind band, Is the wind speed in the center of the cyclone.
In a scene of simulating the occurrence of disconnection faults of a power transmission network under the action of extreme weather, wind load borne on a power transmission network line is calculated as follows:
wherein N l is wind load, v is typhoon wind speed, d is wire outer diameter, and θ is included angle between wind direction and power transmission line.
The failure rates of the wires and the electric poles in the transmission network are respectively as follows:
Where p fl and p fp are respectively unreliable operation probabilities of the wire and the electric pole, σ g is stress on the wire section, M T is bending moment on the pole root, μ l and δ l are respectively mean and standard deviation of the strength of the wire element, μ p and δ p are respectively mean and standard deviation of the strength of the electric pole element, and σ l and M p are all parameters in calculation, which is not practical. The mean value and standard deviation of the strength of the wire and the pole element can be obtained according to actual operation.
The fault rate of the overhead distribution line is as follows:
Wherein p fl,l (v) is the failure rate of the line l, m 1 and m 2 are the number of electric poles and the number of wire stages of the line l respectively, and p fp,k,l(v)、pfl,h,l (v) is the failure rate of the kth electric pole and the h wire of the line l respectively.
The invention is described below by way of example in terms of an improved 118-node test system:
The system comprises 186 power transmission lines, 91 load nodes and 10 virtual power plants, the obtained 118-node test system is shown in fig. 3, and an actual geographic wiring diagram is shown in fig. 4. Assuming that typhoons move transversely along a straight path in fig. 4 at a speed of 20km/h, circles in the figure are positions corresponding to the radius of the maximum wind speed during entry of typhoons into the ground. After the overhead transmission line has the fault of pole reversing and line breaking, the line cannot be successfully reclosed and needs to be manually repaired, so that the fault line can be restored after typhoons pass the border under the normal condition. Based on the above considerations, the fault scenario assumed in the study is within a period of time after the wire break that has not yet been repaired. The following three scenarios are set for the 118 node test system to verify the utility of the proposed method: 1) Each virtual power plant operates independently and only carries out power transaction with the power transmission network; 2) The virtual power plants are in cooperative game, and any virtual power plant can conduct electric power transaction with a power transmission network and other virtual power plants; 3) The transmission network breaks line fault due to extreme weather, and the virtual power plant respectively carries out the two operation modes: ① The power transmission network has disconnection fault, and each virtual power plant operates independently and only carries out power transaction with the power transmission network; ② The transmission network breaks down, the virtual power plants play a cooperative game, and can conduct electric power transaction with the transmission network and other virtual power plants.
Table 1 comparison of scheduling results in different scenarios
And carrying out cooperative scheduling solving on the three scenes, wherein the obtained scheduling results are shown in fig. 5, 6, 7 and 8, and table 1 is used for comparing the scheduling results in different scenes. In the figure, V b and V s are respectively the electric quantity purchased and sold by the multiple virtual power plants to the power transmission network, P dre is the output of renewable energy sources in the multiple virtual power plants, and P cur is the abandoned wind, The sum of the amount of abandoned light, E ch and E dis are the charge and discharge amounts of stored energy, P dg is the fuel cell output, and DR is the interruptible load response. It can be seen from the figure that in scenario one, the electricity trade between the virtual power plant and the power transmission network is frequent, and the power transmitted by the virtual power plant to the power transmission network is significantly lower than the total power transmitted by the power transmission network to the virtual power plant. Analysis shows that the wind-solar power generation output in each virtual power plant still has a margin after meeting the load demand of the virtual power plant, so that the power is selectively fed to the power transmission network to realize the off-site consumption of renewable energy sources. In the second scenario, because cooperative games exist among the virtual power plants, the virtual power plants in the alliance can perform electric energy mutual power utilization among the multiple virtual power plants according to the residual electricity or lack electricity state estimated by the virtual power plants, and further electric power interaction with an upper power grid is reduced. Especially considering the wind-solar complementary characteristics of renewable energy sources in alliance, on one hand, photovoltaic power stations are at 8:00-15: the generated energy in 00 accounts for more than 80% of the total generated energy of the virtual power plant, and in the period of time, the photovoltaic output is jointly consumed in the alliance, so that the power supply of the virtual power plant to the power transmission network is reduced; on the other hand, the output peak period of the wind turbine is mainly concentrated at 14:00-23:00, the generated energy in the period is more than 70% of the total generated energy of the wind power generation system, and the wind power generation system has large motor group body quantity and large quantity in the multi-virtual power plant group, so that the wind power has higher surplus electric quantity relative to the photovoltaic power output, and the electric energy sold to the power transmission network is also in the peak period. In the third scenario, the transmission network has a disconnection fault due to extreme weather, and at this time, whether the virtual power plants perform cooperative game or not, the renewable energy consumption rate of the virtual power plants is reduced, because the influence caused by typhoons affects the distribution areas of the plurality of virtual power plants, so as to relieve the possible blocking phenomenon in the line of the disconnection area, the electric quantity flowing from the transmission network to the plurality of virtual power plants is increased, and accordingly, the consumption of renewable energy sources in the virtual power plants in the transmission network is reduced. In terms of carbon emission, the carbon emission in the market is determined by the output of a fuel cell, the amount of abandoned wind and the thermal power generation capacity purchased by a virtual power plant from a power transmission network, and the renewable energy consumption rate is improved in the scene II compared with the scene I, so that the carbon emission in the market is reduced to a certain extent, and the low carbon performance of the operation of the multiple virtual power plants is promoted. After a line break fault occurs in the power transmission network, the line break causes a blocking phenomenon possibly occurring in a partial area, so that the off-site consumption level of renewable energy sources is reduced, and both the wind and light abandoning and the power transmission network power feeding quantity increase to the virtual power plant cause the increase of carbon emission participating in the market. The simulation results verify the validity and practicability of the model constructed by the invention.
Based on the same inventive concept, the embodiment of the application provides a computer device, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor realizes the steps of the multi-virtual power plant low-carbon scheduling method based on cooperative game under the consideration of fault risk when executing the computer program.
Based on the same inventive concept, an embodiment of the present application provides a computer readable storage medium storing a computer program, which when executed by a processor, implements the steps of the method for low-carbon scheduling of multiple virtual power plants based on cooperative game under consideration of fault risk.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention 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 invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above embodiments are only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited thereto, and any modification made on the basis of the technical scheme according to the technical idea of the present invention falls within the protection scope of the present invention.
Claims (5)
1. The multi-virtual power plant low-carbon scheduling method based on the cooperative game under the consideration of fault risk is characterized by comprising the following steps of:
Step S1, carrying out fine modeling on a virtual power plant aggregation unit, constructing a multi-virtual power plant low-carbon scheduling double-layer model considering electric-carbon transaction, and guiding the multi-virtual power plant to form a cooperative game alliance;
in the fine modeling of the virtual power plant aggregation unit, an expression of an interruptible load model in the virtual power plant is as follows:
In the method, in the process of the invention, The electricity purchasing and selling states of the virtual power plant on the node j are respectively the virtual power plant on the node i at the moment t; The electricity purchasing and selling states of the virtual power plant on the node i at the moment t and the power transmission network are respectively;
In the fine modeling of the virtual power plant aggregation unit, the power balance constraint expression inside the virtual power plant is as follows:
In the method, in the process of the invention, For a normal load within the virtual power plant,The output and the waste power of renewable energy sources in the virtual power plant on the node i at the moment t are respectively,Respectively purchasing and selling electricity quantity between the virtual power plant and the power transmission network,The purchase and sales amounts of electricity between the virtual power plant on the node i and the virtual power plant on the node j are respectively,Respectively the charge and discharge amounts of the stored energy,As a response to interruptible loads in the virtual power plant,For the power generation amount of the fuel cell in the virtual power plant, P i b,max、Pi s,max is the maximum power of the power purchased and sold between the virtual power plant and the power transmission network, P i vb,max、Pi vs,max is the maximum power of the power purchased and sold between the virtual power plant and other virtual power plants,The upper limit and the lower limit of the output of the fuel cell are respectively;
In the fine modeling of the virtual power plant aggregation unit, the energy storage model expression in the virtual power plant is as follows:
xi,t+yi,t＝1
wherein x i,t、yi,t is an energy storage running state variable of the virtual power plant on a node i at the moment t, x i,t =1 represents that the energy storage is in a charging state, and y i,t =1 represents that the energy storage is in a discharging state; respectively charging and discharging power of the energy storage, Respectively the rated charge and discharge power of the stored energy,Respectively the energy storage charging and discharging efficiency, delta t=1h is the interval time,Respectively the maximum charge and discharge quantity of the stored energy in the period t,And r i are the self-discharge electric quantity and the self-discharge rate of the stored energy respectively,AndThe electric quantity stored in the energy storage at the initial time and the t time respectively,For the amount of electricity stored in the energy storage at time t-1, The electric quantity stored in the energy storage at the moment 1, the self-discharge electric quantity of the energy storage, the charge quantity and the discharge quantity of the energy storage, and the SOC i,max、SOCi,min are the maximum charge state and the minimum charge state of the energy storage respectively;
The multi-virtual power plant low-carbon scheduling double-layer model considering the electricity-carbon transaction is specifically as follows:
The upper layer objective function is from the perspective of the virtual power plant, and the objective function of the multi-virtual power plant transaction model is as follows:
Wherein T, N respectively represents day scheduling time and the number of virtual power plants, pi i,t is the marginal electricity price of a node i at a moment t, c b、cs is the purchase and sale electricity price when the virtual power plants transact with a power transmission network, beta is the electricity interruption compensation contract electricity price provided by the virtual power plants for interruption load users, gamma and mu are the charge and discharge electricity price of energy stored in the virtual power plants, c DG is the unit power generation cost of a fuel cell, and c E is the transaction price coefficient of a carbon market;
for the carbon emission amount of the virtual power plant on the node i at the time t to participate in the carbon market transaction, the expression is as follows:
In the method, in the process of the invention, The carbon emission amount of the virtual power plant on the node i at the moment t participating in the carbon market transaction,AndRespectively represents the actual carbon emission quantity, the initial carbon emission quota and the wind-solar nuclear evidence emission reduction quantity,The wind power and photovoltaic power output are respectively, χ c is the carbon emission intensity of unit output, χ m is the initial distribution coefficient of unit electric quantity carbon emission, χ g is the calculation coefficient of wind-solar nuclear evidence emission reduction;
the underlying objective function requires the grid to purchase the least total cost of power:
In the method, in the process of the invention, AndRespectively competing for interruptible load in the virtual power plant on the node i at the moment t, energy storage and a traditional thermal generator set in the power grid,The output of the traditional thermal power generating unit;
The power balance constraints of the grid are as follows:
Where L i,t is the load on node i at time t, lambda t is a dual variable related to power balance at time t, Maximum and minimum limits of interruptible load output in the virtual power plant,Respectively the maximum and minimum limit values of energy storage discharge in the virtual power plant,The maximum and minimum limits of the traditional generator output in the virtual power plant are respectively,AndIn order to have a dual variable, the two variables,AndIn order to have a dual variable, the two variables,AndAs dual variables;
the power flow constraint of the power transmission line is as follows:
where Lim l denotes the transmission capacity limit of line l, AndIs a dual variable of t period related to transmission capacity constraint of line l, and GSF l,i is a power transfer distribution factor;
The cooperative game of multiple virtual power plants follows the principle of nearby combination and wind-solar complementation, namely, the virtual power plants take precedence in forming alliances with other virtual power plants close to the geographic position, and simultaneously, virtual power plants taking wind power generation and photovoltaic power generation as distributed renewable energy sources are required to exist in each alliance;
S2, taking typhoons as representative of extreme weather, simulating a scene of broken line faults of a power transmission network under the action of the extreme weather, and constructing a line fault model under the extreme weather;
And step S3, based on the step S1 and the step S2, a multi-virtual power plant low-carbon scheduling model based on cooperative game under the consideration of fault risk is established, and a double-layer model is converted into a mixed integer linear programming problem and solved based on Karush-Kuhn-Tucker conditions and a strong dual principle, so that each virtual power plant scheduling scheme under the form of cooperative game is obtained.
2. The method for low-carbon scheduling of multiple virtual power plants based on cooperative game under consideration of fault risk according to claim 1, wherein in the step S2, typhoons are taken as representative of extreme weather, the wind speed and wind direction of each point of typhoons paths are as follows:
Wherein v is typhoon speed, R is distance from the power transmission line to the typhoon center, R max is distance from the cyclone center to the strongest wind band, Is the wind speed in the center of the cyclone.
3. The method for low-carbon scheduling of multiple virtual power plants based on cooperative game under consideration of fault risk according to claim 1, wherein in the step S2, in the scenario of simulating disconnection fault of the power transmission network under the action of extreme weather, wind load borne on a power transmission network line is calculated as follows:
Wherein N l is wind load, v is typhoon wind speed, d is wire outer diameter, and θ is included angle between wind direction and power transmission line;
The failure rates of the wires and the electric poles in the transmission network are respectively as follows:
Wherein p fl and p fp are unreliable running probabilities of a wire and an electric pole respectively, sigma g is stress born by a wire section, M T is bending moment born by a pole root, mu l and delta l are mean value and standard deviation of strength of a wire element respectively, mu p and delta p are mean value and standard deviation of strength of the electric pole element respectively, and sigma l and M p are parameters;
the fault rate of the overhead distribution line is as follows:
Wherein p fl,l (v) is the failure rate of the line l, m 1 and m 2 are the number of electric poles and the number of wire stages of the line l respectively, and p fp,k,l(v)、pfl,h,l (v) is the failure rate of the kth electric pole and the h wire of the line l respectively.
4. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor, when executing the computer program, implements the steps of the method for collaborative game-based low-carbon scheduling of multiple virtual power plants taking into account risk of failure according to any of claims 1 to 3.
5. A computer readable storage medium storing a computer program, wherein the computer program when executed by a processor implements the steps of the collaborative game based multi-virtual power plant low-carbon dispatch method of any one of claims 1 to 3 taking into account risk of failure.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310997372.5A CN117318166B (en) | 2023-08-09 | 2023-08-09 | Multi-virtual power plant low-carbon scheduling method based on cooperative game under consideration of fault risk |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310997372.5A CN117318166B (en) | 2023-08-09 | 2023-08-09 | Multi-virtual power plant low-carbon scheduling method based on cooperative game under consideration of fault risk |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117318166A CN117318166A (en) | 2023-12-29 |
| CN117318166B true CN117318166B (en) | 2024-07-02 |
Family
ID=89248762
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310997372.5A Active CN117318166B (en) | 2023-08-09 | 2023-08-09 | Multi-virtual power plant low-carbon scheduling method based on cooperative game under consideration of fault risk |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117318166B (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109523052A (en) * | 2018-09-18 | 2019-03-26 | 国网浙江省电力有限公司经济技术研究院 | A kind of virtual plant Optimization Scheduling considering demand response and carbon transaction |
| CN116542474A (en) * | 2023-05-08 | 2023-08-04 | 浙江大学 | Multi-main-body optimized scheduling method considering carbon emission flow and non-cooperative game |
Family Cites Families (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101517996B1 (en) * | 2012-12-31 | 2015-05-08 | 한국전기연구원 | VPP Operation System based on Multi-Agent System |
| US20150213466A1 (en) * | 2014-01-24 | 2015-07-30 | Fujitsu Limited | Demand response aggregation optimization |
| CN106372760A (en) * | 2016-09-28 | 2017-02-01 | 武汉大学 | Game theory-based regional power grid electric energy dealer marketization bidding method |
| CN109687532B (en) * | 2019-03-08 | 2021-02-05 | 燕山大学 | Combined heat and power scheduling method for improving wind power consumption based on cooperative game |
| CN109902884A (en) * | 2019-03-27 | 2019-06-18 | 合肥工业大学 | A kind of virtual plant Optimization Scheduling based on leader-followers games strategy |
| CN110783967B (en) * | 2019-10-29 | 2021-03-30 | 清华大学 | Constraint aggregation-based virtual power plant output feasible region identification method and device |
| CN110929391B (en) * | 2019-11-08 | 2021-08-24 | 中国农业大学 | Method and system for calculating fault rate of power distribution network under typhoon disaster |
| CN112001752A (en) * | 2020-08-18 | 2020-11-27 | 四川大学 | Multi-virtual power plant dynamic game transaction behavior analysis method based on limited rationality |
| CN113162025B (en) * | 2021-01-27 | 2023-03-24 | 四川大学 | Demand response-containing distributed low-carbon economic dispatching method for electrical interconnection network |
| CN113888204A (en) * | 2021-09-06 | 2022-01-04 | 中国能源建设集团天津电力设计院有限公司 | Multi-subject game virtual power plant capacity optimization configuration method |
| CN114676551A (en) * | 2022-01-27 | 2022-06-28 | 广西电网有限责任公司电力科学研究院 | Distribution line tower collapse and line break accident evaluation method under flood disaster |
| CN115187099A (en) * | 2022-07-20 | 2022-10-14 | 国网上海市电力公司 | Virtual power plant aggregation method considering carbon transaction and alliance game |
-
2023
- 2023-08-09 CN CN202310997372.5A patent/CN117318166B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109523052A (en) * | 2018-09-18 | 2019-03-26 | 国网浙江省电力有限公司经济技术研究院 | A kind of virtual plant Optimization Scheduling considering demand response and carbon transaction |
| CN116542474A (en) * | 2023-05-08 | 2023-08-04 | 浙江大学 | Multi-main-body optimized scheduling method considering carbon emission flow and non-cooperative game |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117318166A (en) | 2023-12-29 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110188950A (en) | Virtual plant supply side and Demand-side Optimized Operation modeling method based on multi-agent technology | |
| CN114256836A (en) | Capacity optimization configuration method for new energy power station shared energy storage | |
| CN109784554A (en) | A kind of electric system optimal scheduling method based on leader-followers games | |
| CN115271438B (en) | Multi-main-body game collaborative scheduling method capable of considering carbon emission and electronic equipment | |
| CN115689166A (en) | Method and system for aggregated utilization of regional distributed energy resources | |
| CN105956693A (en) | Method for economic dispatch of virtual power plant based on distributed gradient algorithm | |
| Jafarpour et al. | Resiliency assessment of the distribution system considering smart homes equipped with electrical energy storage, distributed generation and plug-in hybrid electric vehicles | |
| Dai et al. | An equilibrium model of the electricity market considering the participation of virtual power plants | |
| Lei et al. | The optimal operation and revenue allocation method of virtual power plant considering carbon trading | |
| CN117318166B (en) | Multi-virtual power plant low-carbon scheduling method based on cooperative game under consideration of fault risk | |
| CN117332937A (en) | Multi-energy complementary virtual power plant economic dispatching method considering demand response | |
| Fleck et al. | Day-ahead optimization for railway energy management system | |
| CN110992206A (en) | Optimized scheduling method and system for multi-source electric field | |
| CN112270432B (en) | Energy management method of comprehensive energy system considering multi-subject benefit balance | |
| CN114447923A (en) | Electric energy and frequency modulation auxiliary service game optimization method for high wind power ratio electric power system | |
| Zhang et al. | Real-time active power dispatch for virtual power plant via a multi-agent leader-follower game approach | |
| CN109948879A (en) | The methodology of the out-of-limit index of Branch Power Flow under a kind of Power Market | |
| Parastegari et al. | Coordinated scheduling of renewable energy sources in the unit commitment problem: a review of recent literature | |
| Liu et al. | Bi-level optimized operation strategy for interconnected microgrids | |
| Ying et al. | Research on Bidding Strategy of Virtual Power Plant Participating in Spot Market | |
| Jin et al. | The International practice and enlightenment of capacity compensation mechanism | |
| Wu et al. | Multi-alliance market subject auxiliary peak shaving strategy for new energy consumption | |
| CN112651637B (en) | Distributed power supply excitation mechanism analysis method for virtual power plant | |
| Qi et al. | Agent Simulation-Based Market Clearing Model for Reactive Auxiliary Services | |
| Fu et al. | P2P Trading Mechanism for Power Retail Market with Consideration of MVPPs |
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 | ||
| GR01 | Patent grant |