CN115099456A - Distributed energy source configuration method and device, mobile terminal and storage medium - Google Patents

Distributed energy source configuration method and device, mobile terminal and storage medium Download PDF

Info

Publication number
CN115099456A
CN115099456A CN202210528393.8A CN202210528393A CN115099456A CN 115099456 A CN115099456 A CN 115099456A CN 202210528393 A CN202210528393 A CN 202210528393A CN 115099456 A CN115099456 A CN 115099456A
Authority
CN
China
Prior art keywords
transaction
distributed energy
clearing
distributed
electric quantity
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.)
Granted
Application number
CN202210528393.8A
Other languages
Chinese (zh)
Other versions
CN115099456B (en
Inventor
杨喆麟
邢玉辉
张茂林
陈玲
陈清贵
刘祥瑞
金美含
杨璇
王帮灿
周娜
孙恒一
高航
李维劼
唐维
蔡娜
陈丹琦
王子石
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.)
Kunming Electric Power Transaction Center Co ltd
Original Assignee
Kunming Electric Power Transaction Center 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 Kunming Electric Power Transaction Center Co ltd filed Critical Kunming Electric Power Transaction Center Co ltd
Priority to CN202210528393.8A priority Critical patent/CN115099456B/en
Publication of CN115099456A publication Critical patent/CN115099456A/en
Application granted granted Critical
Publication of CN115099456B publication Critical patent/CN115099456B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

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
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • 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
    • 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
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/04—Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange
    • 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
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/04—Constraint-based CAD
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2113/00—Details relating to the application field
    • G06F2113/04—Power grid distribution networks
    • 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]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S50/00—Market activities related to the operation of systems integrating technologies related to power network operation or related to communication or information technologies
    • Y04S50/14—Marketing, i.e. market research and analysis, surveying, promotions, advertising, buyer profiling, customer management or rewards

Landscapes

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

Abstract

The invention discloses a configuration method, a configuration device, a mobile terminal and a storage medium of distributed energy, wherein the method comprises the following steps: establishing a distributed energy clearing model according to the electric power data and the electricity price data in the distributed energy to be configured; decoupling the distributed energy source clearing model according to the augmented Lagrange function to obtain a plurality of transaction sub-models; carrying out iterative alternate solution according to the transaction submodels to obtain the transaction electric quantity and clearing price of the power users in the distributed transaction; and obtaining a scheduling processing plan and a unit combination according to the transaction electric quantity and the clearing price, and configuring the distributed energy to be configured according to the scheduling processing plan and the unit combination. The embodiment of the invention improves the configuration precision of the distributed energy.

Description

Distributed energy source configuration method and device, mobile terminal and storage medium
Technical Field
The present invention relates to the field of data processing technologies, and in particular, to a method and an apparatus for configuring distributed energy, a mobile terminal, and a storage medium.
Background
According to a traditional electric power market clearing model, a sending party and a using party are used for centralized quotation, the aim of maximizing social welfare or minimizing total generating cost is taken as the target, various constraint conditions of a power grid are considered, and the optimal allocation of the whole electric power resource is realized. The current clearing optimization algorithm is relatively efficient when the solving scale is small, and can meet the scheduling requirement.
With the continuous development of a novel power system, an energy structure is greatly changed, large-scale distributed energy is continuously connected into a power grid, in the face of massive distributed energy information, a traditional market clearing model cannot effectively cope with the problem, the solving scale and constraint conditions are continuously increased, the calculation efficiency of a clearing model algorithm is greatly reduced, even the problem that the solving algorithm cannot be converged occurs, therefore, in the face of distributed resources, a centralized clearing method is difficult to fully integrate and utilize distributed resources of different types to realize the optimal configuration of the distributed resources, namely, the configuration precision of the distributed energy is not high, and the safe and stable operation of the system is further endangered.
Therefore, the existing distributed energy resource configuration method has the problem of low precision due to low calculation efficiency of the clearing model.
Disclosure of Invention
The embodiment of the invention provides a configuration method and device of distributed energy, a mobile terminal and a storage medium, and improves the configuration precision of the distributed energy.
A first aspect of an embodiment of the present application provides a method for configuring a distributed energy source, including:
establishing a distributed energy clearing model according to the electric power data and the electricity price data in the distributed energy to be configured;
decoupling the distributed energy source clearing model according to an augmented Lagrange function to obtain a plurality of transaction submodels;
carrying out iterative alternative solution according to the transaction submodels to obtain the transaction electric quantity and clearing price of the power users in the distributed transaction;
and obtaining a scheduling processing plan and a unit combination according to the transaction electric quantity and the clearing price, and configuring the distributed energy to be configured according to the scheduling processing plan and the unit combination.
In a possible implementation manner of the first aspect, the distributed energy source clearing model is decoupled according to an augmented lagrangian function to obtain a plurality of transaction submodels, which specifically are:
establishing a global optimization model according to the distributed energy clearing model;
obtaining an augmented Lagrange function according to the global optimization model and the Lagrange multiplier;
and decoupling the distributed energy source clearing model according to the augmented Lagrange function to obtain a plurality of transaction submodels.
In a possible implementation manner of the first aspect, the iterative alternative solution is performed according to a plurality of transaction submodels to obtain the transaction electric quantity and the clearing price of the power consumer in the distributed transaction, specifically:
wherein, the category of the transaction submodel comprises: the electric vendor market trading sub-model and the electric power user market trading sub-model;
updating and optimizing the power user market transaction submodel according to the coordinated price and the recommended electric quantity to obtain a first optimization result;
and feeding back the first optimization result to the market trading submodel of the electricity vendor, optimizing again to obtain the next coordinated price and the proposed electric quantity, and obtaining the final trading electric quantity and clearing price of the power users in the distributed trading through multiple iterations until convergence.
In a possible implementation manner of the first aspect, the establishing a distributed energy clearing model according to the electric power data and the electricity price data in the distributed energy to be configured specifically includes:
acquiring a target function according to power data and electricity price data in the distributed energy to be configured; wherein the objective function is a function for representing the minimization of the system operation cost;
establishing a distributed energy clearing model according to the objective function and the constraint condition; wherein the constraint condition comprises: the system comprises a user power balance constraint condition, a distributed transaction balance constraint condition and a user distributed energy transaction electric quantity constraint condition.
A second aspect of an embodiment of the present application provides a configuration apparatus for a distributed energy source, including: the system comprises an establishing module, a decoupling module, a solving module and a configuration module;
the establishing module is used for establishing a distributed energy clearing model according to electric power data and electricity price data in distributed energy to be configured;
the decoupling module is used for decoupling the distributed energy clearing model according to the augmented Lagrange function to obtain a plurality of transaction submodels;
the solving module is used for carrying out iterative alternative solving according to the transaction submodels to obtain the transaction electric quantity and the clearing price of the power users in the distributed transaction;
the configuration module is used for obtaining a scheduling processing plan and a unit combination according to the transaction electric quantity and the clearing price and configuring distributed energy to be configured according to the scheduling processing plan and the unit combination.
In a possible implementation manner of the second aspect, the distributed energy source clearing model is decoupled according to an augmented lagrangian function to obtain a plurality of transaction submodels, specifically:
establishing a global optimization model according to the distributed energy clearing model;
obtaining an augmented Lagrange function according to the global optimization model and the Lagrange multiplier;
and decoupling the distributed energy source clearing model according to the augmented Lagrange function to obtain a plurality of transaction sub-models.
In a possible implementation manner of the second aspect, the iterative alternative solution is performed according to a plurality of transaction submodels to obtain the transaction electric quantity and the clearing price of the power consumer in the distributed transaction, specifically:
wherein, the category of the transaction submodel comprises: the electric vendor market trading sub-model and the electric power user market trading sub-model;
updating and optimizing the power consumer market transaction submodel according to the coordinated price and the suggested electric quantity to obtain a first optimization result;
and feeding back the first optimization result to the market trading sub-model of the electricity selling merchant, optimizing again to obtain the next coordinated price and the proposed electric quantity, and obtaining the final trading electric quantity and clearing price of the power users in the distributed trading through multiple iterations until convergence.
In a possible implementation manner of the second aspect, the establishing a distributed energy clearing model according to the electric power data and the electricity price data in the distributed energy to be configured specifically includes:
acquiring a target function according to power data and electricity price data in the distributed energy to be configured; the objective function is a function for representing the minimization of the system operation cost;
establishing a distributed energy clearing model according to the objective function and the constraint condition; wherein the constraint condition comprises: the system comprises a user power balance constraint condition, a distributed transaction balance constraint condition and a user distributed energy transaction electric quantity constraint condition.
A third aspect of the embodiments of the present application provides a mobile terminal, which includes a processor and a memory, where the memory stores computer readable program codes, and the processor implements the steps of the method for configuring a distributed energy resource when executing the computer readable program codes.
A fourth aspect of embodiments of the present application provides a storage medium storing computer readable program code, which when executed, implements the steps of a method for configuring a distributed energy source as described above.
Compared with the prior art, the method, the device, the mobile terminal and the storage medium for configuring the distributed energy provided by the embodiment of the invention comprise the following steps: establishing a distributed energy clearing model according to the electric power data and the electricity price data in the distributed energy to be configured; decoupling the distributed energy source clearing model according to an augmented Lagrange function to obtain a plurality of transaction submodels; carrying out iterative alternative solution according to the transaction submodels to obtain the transaction electric quantity and clearing price of the power users in the distributed transaction; and obtaining a scheduling processing plan and a unit combination according to the transaction electric quantity and the clearing price, and configuring the distributed energy to be configured according to the scheduling processing plan and the unit combination.
The beneficial effects are that: according to the embodiment of the invention, after the distributed energy clearing model is established according to the electric power data and the electricity price data in the distributed energy to be configured, the distributed energy clearing model is decoupled according to the augmented Lagrange function to obtain a plurality of transaction submodels, iterative alternative solution is carried out according to the transaction submodels to obtain a scheduling processing plan and a unit combination, and the distributed energy to be configured is configured according to the scheduling processing plan and the unit combination. According to the embodiment of the invention, the distributed energy clearing model is decoupled into a plurality of transaction submodels, the original problem of large-scale distributed optimization can be decomposed into a plurality of small-scale subproblems, the plurality of subproblems are solved in parallel, the solution of each subproblem is iteratively coordinated to obtain the global optimal solution of the original problem, the problems that the distributed transaction clearing efficiency is low and the solving algorithm cannot be converged are solved, so that the clearing efficiency of power grid scheduling optimization is improved, the calculation precision of a scheduling processing plan and a unit combination is improved, and the configuration precision of distributed energy is improved; meanwhile, the embodiment of the invention can ensure that a power grid dispatching mechanism can arrange the dispatching processing plan in time, realize the fine management of the dispatching processing plan, and can actively deal with the intermittence or fluctuation of new energy or distributed resources, thereby ensuring the safety and stability of the system.
Drawings
Fig. 1 is a schematic flow chart of a method for configuring a distributed energy resource according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a device for configuring a distributed energy source according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, which is a schematic flow chart of a configuration method of a distributed energy resource according to an embodiment of the present invention, includes S101-S104:
s101: and establishing a distributed energy clearing model according to the electric power data and the electricity price data in the distributed energy to be configured.
The distributed energy clearing model is a distributed energy sharing transaction clearing model.
In this embodiment, the establishing a distributed energy clearing model according to the electric power data and the electricity price data in the distributed energy to be configured specifically includes:
acquiring a target function according to power data and electricity price data in the distributed energy to be configured; wherein the objective function is a function for representing minimization of system operation cost;
establishing the distributed energy clearing model according to the objective function and the constraint condition; wherein the constraint condition comprises: the system comprises a user power balance constraint condition, a distributed transaction balance constraint condition and a user distributed energy transaction electric quantity constraint condition.
In a specific embodiment, the user shares the self distributed energy with other users, compared with the user independent operation state, the operation cost of the user is increased, and in order to encourage the user to actively participate in the market optimization operation, the electricity vendor, as a market organizer, needs to pay corresponding fees to the user. Setting the electricity cost of a user to C i The income of the electricity seller is r, and the fee paid to the user i by the electricity seller is r
Figure BDA0003645536660000061
The method comprises the following steps of establishing an objective function by combining power data and electricity price data in distributed energy sources to be configured and aiming at maximization of social welfare or minimization of system operation cost, wherein the objective function can be represented by the following formula:
Figure BDA0003645536660000062
Figure BDA0003645536660000063
Figure BDA0003645536660000064
wherein, min X For indicating system operating cost minimization, Ω s Represents a set of scenes, Ω T Represents a set of periods, Ω U Representing a set of users. Epsilon s Is the probability of scene s. Further, the electricity price data in the distributed energy resource to be configured includes:
Figure BDA0003645536660000065
and,
Figure BDA0003645536660000066
Figure BDA0003645536660000067
Represents the price of electricity sold by an electricity seller to the power grid,
Figure BDA0003645536660000068
represents the electricity purchase price of the electricity seller from the power grid,
Figure BDA0003645536660000069
indicating the retail price of electricity offered by the electricity vendor to the user,
Figure BDA00036455366600000610
indicating the net metered electricity price provided by the electricity vendor to the user; the power data in the distributed energy resource to be configured includes:
Figure BDA00036455366600000611
and
Figure BDA00036455366600000612
Figure BDA00036455366600000613
for the net remaining power of user i at scene s and time period t,
Figure BDA00036455366600000614
for the payload of user i at scene s and time period t,
Figure BDA00036455366600000615
for the net remaining power of all users at scene s and time period t,
Figure BDA00036455366600000616
for the payload of all users at scene s and time period t,
Figure BDA00036455366600000617
for controllable load of user i in scene s and time t, U i (. cndot.) is the utility function of user i.
In a specific embodiment, the constraints are as follows:
(1) user power balance constraint:
Figure BDA00036455366600000618
wherein,
Figure BDA00036455366600000619
the amount of power participating in the distributed transaction on behalf of the user.
(2) Distributed trade balance constraints:
Figure BDA0003645536660000071
wherein, the distributed trade balance constraint is used for representing that the sum of the electric quantities of all the users participating in the distributed trade under each scene and time interval is 0, namely the distributed tradeSelf-balancing.
Figure BDA0003645536660000072
Lagrange relaxation factor for this equilibrium condition.
(3) And (3) user distributed energy transaction electric quantity constraint:
Figure BDA0003645536660000073
wherein, the user distributed energy trading electric quantity constraint indicates that the electric quantity of the user participating in the market trading can be positive or negative,
Figure BDA0003645536660000074
the upper limit of the user distributed energy trading electric quantity is represented, the trading electric quantity is positive to represent that the market is a supplier, and the trading electric quantity is negative to represent that the market is a consumer.
And the objective function and the constraint condition form a distributed energy sharing trade clearing model.
S102: and decoupling the distributed energy source clearing model according to the augmented Lagrange function to obtain a plurality of transaction sub-models.
In this embodiment, the decoupling the distributed energy source clearing model according to the augmented lagrangian function to obtain a plurality of transaction submodels specifically includes:
establishing a global optimization model according to the distributed energy clearing model;
obtaining the augmented Lagrangian function according to the global optimization model and the Lagrangian multiplier;
and decoupling the distributed energy clearing model according to the augmented Lagrange function to obtain a plurality of transaction submodels.
In one embodiment, a global optimization model is established according to the distributed energy resource clearance model, and the global optimization model corresponds to the distributed energy resource clearance model as follows:
Figure BDA0003645536660000075
s.t.
h(X，Y)＝0;
wherein X and Y are optimization decision variables, X represents a user variable, and Y represents an agent electricity vendor variable. The constraint may be expressed as a coupled constraint of the customer variable and the agent vendor variable. The augmented Lagrangian function of the global optimization model is as follows:
Figure BDA0003645536660000081
wherein λ is lagrange multiplier of constraint condition, and ρ is penalty factor of penalty term. The cross direction multiplier method is designed as follows:
Figure BDA0003645536660000082
Figure BDA0003645536660000083
λ(k+1)＝λ(k)+ρ·h(X(k+1)，Y(k+1))；
the Lagrangian multiplier lambda (k) and partial optimal solution Y (k) of the kth time are given, the Lagrangian function is optimized and expanded, and the other partial optimal solution X (k +1) of the kth +1 is obtained; giving a kth Lagrange multiplier lambda (k) and a kth +1 part of optimal solution X (k +1), and optimizing and amplifying a Lagrange function to obtain the other part of optimal solution Y (k +1) of the kth + 1; updating the Lagrange multiplier to obtain the value of the (k +1) th time; finally, the next iteration is restarted.
Introducing auxiliary variables
Figure BDA0003645536660000084
The net load, net surplus power and electric quantity participating in market trading, which are suggested to the user i by the representative electricity vendor, are added into the distributed energy clearing model, and are decomposed according to a cross direction multiplier method to obtain the saleThe system comprises an E-commerce market trading sub-model and an electric power user market trading sub-model.
Further, the electric vendor market trading submodel is as follows:
the objective function of the electric vendor market transaction submodel is as follows:
Figure BDA0003645536660000085
the constraint conditions of the electric vendor market transaction submodel are a first constraint, a second constraint and a third constraint:
a first constraint:
Figure BDA0003645536660000086
a second constraint:
Figure BDA0003645536660000091
and a third constraint:
Figure BDA0003645536660000092
wherein, the optimization decision variable of the agent electricity selling merchant market trading submodel is
Figure BDA0003645536660000093
Figure BDA0003645536660000094
Is a dual multiplier of the third constraint. In the constraint condition, the optimization decision variables related to the agent electricity vendors are replaced by
Figure BDA0003645536660000095
By optimizing the model, the electricity utilization level of the user suggested by the agent electricity seller can be obtained
Figure BDA0003645536660000096
The power consumer market transaction submodel is as follows:
the objective function of the power consumer market transaction submodel is as follows:
Figure BDA0003645536660000097
the constraint conditions of the power consumer market trading submodel are the user power balance constraint and the user distributed energy trading electric quantity constraint, and the following conditions are as follows:
Figure BDA0003645536660000098
Figure BDA0003645536660000099
wherein, the optimization decision variable of the user market trading submodel is X i And
Figure BDA00036455366600000910
X i including the net remaining power and payload of user i,
Figure BDA00036455366600000911
the trading sub-model of the power consumer market is optimized for the user i to trade the electric quantity, and the actual electric consumption level of each user can be obtained.
S103: and carrying out iterative alternative solution according to the transaction submodels to obtain the transaction electric quantity and clearing price of the power users in the distributed transaction.
In a specific embodiment, the iterative alternation solving process adopts an iteration rule of a cross direction multiplier method.
In this embodiment, the iterative and alternative solution is performed according to the plurality of transaction submodels to obtain the transaction electric quantity and the clearing price of the power consumer in the distributed transaction, which specifically includes:
wherein the category of the transaction submodel comprises: the system comprises an e-vendor market transaction submodel and an electric power user market transaction submodel;
updating and optimizing the power consumer market transaction submodel according to the coordinated price and the recommended electric quantity to obtain a first optimization result;
and after the first optimization result is fed back to the e-vendor market trading submodel, optimizing again to obtain the next coordinated price and the next proposed electric quantity, and obtaining the final trading electric quantity and clearing price of the power users in the distributed trading through multiple iterations until convergence.
In this embodiment, the first optimization result includes: the optimized net load, the optimized net power surplus and the optimized electricity quantity participating in market trading.
In one embodiment, the update optimization power consumer market trading submodel is as follows:
Figure BDA0003645536660000101
wherein the penalty factor, i.e. the iteration step, is selected as
Figure BDA0003645536660000102
S104: and obtaining a scheduling processing plan and a unit combination according to the transaction electric quantity and the clearing price, and configuring the distributed energy to be configured according to the scheduling processing plan and the unit combination.
To further explain the configuration apparatus of the distributed energy resource, please refer to fig. 2, fig. 2 is a schematic structural diagram of the configuration apparatus of the distributed energy resource according to an embodiment of the present invention, including: the system comprises an establishing module 201, a decoupling module 202, a solving module 203 and a configuration module 204;
the establishing module 201 is configured to establish a distributed energy clearing model according to power data and electricity price data in distributed energy to be configured;
the decoupling module 202 is used for decoupling the distributed energy source clearing model according to an augmented Lagrange function to obtain a plurality of transaction submodels;
the solving module 203 is used for carrying out iterative alternative solving according to the transaction submodels to obtain the transaction electric quantity and clearing price of the power users in the distributed transaction;
the configuration module 204 is configured to obtain a scheduling processing plan and a unit combination according to the transaction electric quantity and the clearing price, and configure the distributed energy to be configured according to the scheduling processing plan and the unit combination.
In this embodiment, the decoupling the distributed energy source clearing model according to the augmented lagrangian function to obtain a plurality of transaction submodels specifically includes:
establishing a global optimization model according to the distributed energy clearing model;
obtaining the augmented Lagrangian function according to the global optimization model and the Lagrangian multiplier;
and decoupling the distributed energy clearing model according to the augmented Lagrange function to obtain a plurality of transaction submodels.
In this embodiment, the performing iterative alternative solution according to the transaction submodels to obtain the transaction electric quantity and clearing price of the power consumer in the distributed transaction specifically includes:
wherein the category of the transaction submodel comprises: the system comprises an e-vendor market transaction submodel and an electric power user market transaction submodel;
updating and optimizing the power consumer market transaction submodel according to the coordinated price and the recommended electric quantity to obtain a first optimization result;
and after the first optimization result is fed back to the e-vendor market trading submodel, optimizing again to obtain the next coordinated price and the next proposed electric quantity, and obtaining the final trading electric quantity and clearing price of the power users in the distributed trading through multiple iterations until convergence.
In this embodiment, the establishing a distributed energy clearing model according to the electric power data and the electricity price data in the distributed energy to be configured specifically includes:
acquiring a target function according to power data and electricity price data in the distributed energy to be configured; wherein the objective function is a function for representing minimization of system operation cost;
establishing the distributed energy clearing model according to the objective function and the constraint condition; wherein the constraint condition comprises: the system comprises a user power balance constraint condition, a distributed transaction balance constraint condition and a user distributed energy transaction electric quantity constraint condition.
A specific embodiment of the present invention provides a mobile terminal, including a processor and a memory, where the memory stores a computer readable program code, and the processor implements the steps of the method for configuring distributed energy resources when executing the computer readable program code.
An embodiment of the present invention provides a storage medium storing computer readable program code, which when executed, implements the steps of a method for configuring a distributed energy source as described above.
According to the embodiment of the invention, a distributed energy source clearing model is established through an establishing module 201 according to electric power data and electricity price data in distributed energy sources to be configured; decoupling the distributed energy source clearing model through a decoupling module 202 according to the augmented Lagrange function to obtain a plurality of transaction submodels; performing iterative alternative solution according to the transaction submodels through a solution module 203 to obtain the transaction electric quantity and clearing price of the power users in the distributed transaction; the configuration module 204 obtains a scheduling processing plan and a unit combination according to the transaction electric quantity and the clearing price, and configures the distributed energy to be configured according to the scheduling processing plan and the unit combination.
According to the embodiment of the invention, after the distributed energy clearing model is established according to the electric power data and the electricity price data in the distributed energy to be configured, the distributed energy clearing model is decoupled according to the augmented Lagrange function to obtain a plurality of transaction submodels, iterative alternative solution is carried out according to the transaction submodels to obtain a scheduling processing plan and a unit combination, and the distributed energy to be configured is configured according to the scheduling processing plan and the unit combination. According to the embodiment of the invention, the distributed energy clearing model is decoupled into a plurality of transaction submodels, the original problem of large-scale distributed optimization can be decomposed into a plurality of small-scale subproblems, the plurality of subproblems are solved in parallel, the solution of each subproblem is iteratively coordinated to obtain the global optimal solution of the original problem, the problems that the distributed transaction clearing efficiency is low and the solving algorithm cannot be converged are solved, so that the clearing efficiency of power grid scheduling optimization is improved, the calculation precision of a scheduling processing plan and a unit combination is improved, and the configuration precision of distributed energy is improved; meanwhile, the embodiment of the invention can ensure that a power grid dispatching mechanism arranges the dispatching processing plan in time, realizes the fine management of the dispatching processing plan, and can actively cope with the intermittence or fluctuation of new energy or distributed resources, thereby ensuring the safety and stability of the system.
The foregoing is a preferred embodiment of the present invention, and it should be noted that those skilled in the art can make various improvements and modifications without departing from the principle of the present invention, and such improvements and modifications are also considered as the protection scope of the present invention.

Claims (10)

1. A method of configuring a distributed energy source, comprising:
establishing a distributed energy clearing model according to the electric power data and the electricity price data in the distributed energy to be configured;
decoupling the distributed energy clearing model according to an augmented Lagrange function to obtain a plurality of transaction submodels;
carrying out iterative alternative solution according to the transaction submodels to obtain the transaction electric quantity and clearing price of the power users in the distributed transaction;
and obtaining a scheduling processing plan and a unit combination according to the transaction electric quantity and the clearing price, and configuring the distributed energy to be configured according to the scheduling processing plan and the unit combination.
2. The method according to claim 1, wherein the distributed energy source clearance model is decoupled according to an augmented lagrange function to obtain a plurality of transaction submodels, specifically:
establishing a global optimization model according to the distributed energy clearing model;
obtaining the augmented Lagrangian function according to the global optimization model and the Lagrangian multiplier;
and decoupling the distributed energy clearing model according to the augmented Lagrange function to obtain a plurality of transaction submodels.
3. The method according to claim 1, wherein the iterative alternative solution is performed according to the plurality of transaction submodels to obtain the transaction electric quantity and clearing price of the power consumer in the distributed transaction, specifically:
wherein the category of the transaction submodel comprises: the electric vendor market trading sub-model and the electric power user market trading sub-model;
updating and optimizing the power consumer market transaction submodel according to the coordinated price and the recommended electric quantity to obtain a first optimization result;
and after the first optimization result is fed back to the e-vendor market trading submodel, optimizing again to obtain the next coordinated price and the next proposed electric quantity, and obtaining the final trading electric quantity and clearing price of the power users in the distributed trading through multiple iterations until convergence.
4. The method according to claim 1, wherein the building of the distributed energy clearing model according to the power data and the electricity price data in the distributed energy to be configured is specifically as follows:
acquiring a target function according to power data and electricity price data in the distributed energy to be configured; wherein the objective function is a function for representing minimization of system operation cost;
establishing the distributed energy clearing model according to the objective function and the constraint condition; wherein the constraint condition comprises: the system comprises a user power balance constraint condition, a distributed transaction balance constraint condition and a user distributed energy transaction electric quantity constraint condition.
5. A distributed energy source deployment apparatus, comprising: the system comprises an establishing module, a decoupling module, a solving module and a configuration module;
the establishing module is used for establishing a distributed energy clearing model according to electric power data and electricity price data in distributed energy to be configured;
the decoupling module is used for decoupling the distributed energy clearing model according to an augmented Lagrange function to obtain a plurality of transaction submodels;
the solving module is used for carrying out iterative alternative solving according to the transaction submodels to obtain the transaction electric quantity and clearing price of the power users in the distributed transaction;
the configuration module is used for obtaining a scheduling processing plan and a unit combination according to the transaction electric quantity and the clearing price, and configuring the distributed energy to be configured according to the scheduling processing plan and the unit combination.
6. The device according to claim 5, wherein the decoupling of the distributed energy source clearance model according to the augmented Lagrangian function results in a plurality of transaction submodels, specifically:
establishing a global optimization model according to the distributed energy clearing model;
obtaining the augmented Lagrangian function according to the global optimization model and the Lagrangian multiplier;
and decoupling the distributed energy clearing model according to the augmented Lagrange function to obtain a plurality of transaction submodels.
7. The device for configuring distributed energy according to claim 5, wherein the iterative alternative solution is performed according to the plurality of transaction submodels to obtain the transaction electric quantity and clearing price of the power consumer in the distributed transaction, specifically:
wherein the category of the transaction submodel comprises: the electric vendor market trading sub-model and the electric power user market trading sub-model;
updating and optimizing the power consumer market transaction submodel according to the coordinated price and the recommended electric quantity to obtain a first optimization result;
and after the first optimization result is fed back to the e-vendor market trading submodel, optimizing again to obtain the next coordinated price and the next proposed electric quantity, and obtaining the final trading electric quantity and clearing price of the power users in the distributed trading through multiple iterations until convergence.
8. The device for configuring distributed energy according to claim 5, wherein the building of the distributed energy clearing model according to the power data and the electricity price data in the distributed energy to be configured is specifically:
acquiring a target function according to power data and electricity price data in the distributed energy to be configured; wherein the objective function is a function for representing minimization of system operation cost;
establishing the distributed energy clearing model according to the objective function and the constraint condition; wherein the constraint condition comprises: the system comprises a user power balance constraint condition, a distributed transaction balance constraint condition and a user distributed energy transaction electric quantity constraint condition.
9. A mobile terminal, characterized in that it comprises a processor and a memory, said memory storing computer readable program code, said processor when executing said computer readable program code implementing the steps of a method of configuration of a distributed energy resource of any of claims 1 to 4.
10. A storage medium, characterized in that it stores computer readable program code which, when executed, implements the steps of a method of configuration of a distributed energy source according to any one of claims 1 to 4.
CN202210528393.8A 2022-05-16 2022-05-16 Distributed energy configuration method and device, mobile terminal and storage medium Active CN115099456B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210528393.8A CN115099456B (en) 2022-05-16 2022-05-16 Distributed energy configuration method and device, mobile terminal and storage medium

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210528393.8A CN115099456B (en) 2022-05-16 2022-05-16 Distributed energy configuration method and device, mobile terminal and storage medium

Publications (2)

Publication Number Publication Date
CN115099456A true CN115099456A (en) 2022-09-23
CN115099456B CN115099456B (en) 2024-06-11

Family

ID=83287966

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210528393.8A Active CN115099456B (en) 2022-05-16 2022-05-16 Distributed energy configuration method and device, mobile terminal and storage medium

Country Status (1)

Country Link
CN (1) CN115099456B (en)

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2006195848A (en) * 2005-01-14 2006-07-27 Electric Power Dev Co Ltd Method of analyzing determinant for contract price in electric power transaction market and computer program for analysis
CN106447432A (en) * 2016-09-13 2017-02-22 中国农业大学 Electricity selling method for retailer in environment of power retail market
CN109800927A (en) * 2019-03-21 2019-05-24 东南大学 Power distribution network distributed optimization method under bilateral Power Market
CN110620402A (en) * 2019-10-21 2019-12-27 山东大学 Distributed multi-scene-based planning operation joint optimization method and system for electric-gas hybrid system
CN110866773A (en) * 2019-09-29 2020-03-06 西安交通大学 Electric-gas combined market distributed clearing method based on alternative direction multiplier method
CN110929913A (en) * 2019-09-27 2020-03-27 河海大学 Multi-target power generation plan decomposition coordination calculation method for direct-current cross-district interconnected power grid
CN111489267A (en) * 2020-04-14 2020-08-04 国网吉林省电力有限公司 Market clearing model of distribution network side electric coupling system
US20210304307A1 (en) * 2020-03-26 2021-09-30 Xi'an Jiaotong University Distributed energy transaction matching method based on energy network constraints and multiple knapsack model

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2006195848A (en) * 2005-01-14 2006-07-27 Electric Power Dev Co Ltd Method of analyzing determinant for contract price in electric power transaction market and computer program for analysis
CN106447432A (en) * 2016-09-13 2017-02-22 中国农业大学 Electricity selling method for retailer in environment of power retail market
CN109800927A (en) * 2019-03-21 2019-05-24 东南大学 Power distribution network distributed optimization method under bilateral Power Market
CN110929913A (en) * 2019-09-27 2020-03-27 河海大学 Multi-target power generation plan decomposition coordination calculation method for direct-current cross-district interconnected power grid
CN110866773A (en) * 2019-09-29 2020-03-06 西安交通大学 Electric-gas combined market distributed clearing method based on alternative direction multiplier method
CN110620402A (en) * 2019-10-21 2019-12-27 山东大学 Distributed multi-scene-based planning operation joint optimization method and system for electric-gas hybrid system
US20210304307A1 (en) * 2020-03-26 2021-09-30 Xi'an Jiaotong University Distributed energy transaction matching method based on energy network constraints and multiple knapsack model
CN111489267A (en) * 2020-04-14 2020-08-04 国网吉林省电力有限公司 Market clearing model of distribution network side electric coupling system

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
吴界辰 等: "面向智能园区多产消者能量管理的 对等模型(P2P)建模与优化运行", 《电网技术》, vol. 44, no. 1, 31 January 2020 (2020-01-31), pages 52 - 60 *

Also Published As

Publication number Publication date
CN115099456B (en) 2024-06-11

Similar Documents

Publication Publication Date Title
CN109784594B (en) Adjustable load decision method and system for electronic vendor
CN111815018A (en) Optimal scheduling method and device for virtual power plant
CN112329230A (en) Multi-microgrid main body non-cooperative game transaction method
CN114358929A (en) Distributed energy centralized transaction method and system based on block chain
CN114037192A (en) Virtual power plant transaction management method, device, equipment and medium based on big data
CN114140202A (en) Block chain-based distributed electric energy bidding transaction service method and system
Banaei et al. Impacts of large-scale penetration of wind power on day-ahead electricity markets and forward contracts
Duarte et al. Operations of data centers with onsite renewables considering greenhouse gas emissions
CN117635189A (en) Virtual power plant master-slave multi-chain resource interactive matching method based on improved consensus algorithm
CN111402015B (en) Virtual power plant double-layer bidding method and system based on purchase and sale risks
CN113362178A (en) Calculation method and device for guiding power distribution network to charge users participating in P2P transaction
Song et al. Two-stage robust optimal scheduling method for virtual power plants considering the controllability of electric vehicles
CN117522424A (en) Carbon transaction method, system, equipment and medium based on carbon asset contribution degree
CN112488740A (en) Processing method and device for target balance game
CN115099456B (en) Distributed energy configuration method and device, mobile terminal and storage medium
CN113807961A (en) Multi-microgrid energy transaction method and system based on alliance chain
CN112633771B (en) Power resource numerical value calculation method, device, computer equipment and storage medium
Jiang et al. Bilateral planning and operation for integrated energy service provider and prosumers-A Nash bargaining-based method
Xia et al. Grid-friendly peer-to-peer transactive energy market for inhibiting players’ strategic behavior
CN116416027B (en) Micro-grid energy trading method and system
CN118381022B (en) Shared energy storage capacity distribution method
CN118054439B (en) Distributed frequency stable inertia transaction method and system
CN116012078A (en) Electricity price optimization method and device based on genetic algorithm
CN118485232A (en) Energy optimization scheduling method of comprehensive energy system, storage medium and processor
CN114529107A (en) Energy transaction data processing method and device, computer 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
GR01 Patent grant
GR01 Patent grant