CN113095611A - Low-voltage distribution network coordinated planning method containing electric heating equipment and photovoltaic power supply - Google Patents

Low-voltage distribution network coordinated planning method containing electric heating equipment and photovoltaic power supply Download PDF

Info

Publication number
CN113095611A
CN113095611A CN201911338950.4A CN201911338950A CN113095611A CN 113095611 A CN113095611 A CN 113095611A CN 201911338950 A CN201911338950 A CN 201911338950A CN 113095611 A CN113095611 A CN 113095611A
Authority
CN
China
Prior art keywords
power supply
photovoltaic power
node
distribution network
electric heating
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN201911338950.4A
Other languages
Chinese (zh)
Inventor
杨建华
侯斌
陈正
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.)
China Agricultural University
Original Assignee
China Agricultural University
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 China Agricultural University filed Critical China Agricultural University
Priority to CN201911338950.4A priority Critical patent/CN113095611A/en
Publication of CN113095611A publication Critical patent/CN113095611A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—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
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/12—Computing arrangements based on biological models using genetic models
    • G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • 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
    • 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
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
    • 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
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P80/00—Climate change mitigation technologies for sector-wide applications
    • Y02P80/10—Efficient use of energy, e.g. using compressed air or pressurized fluid as energy carrier
    • 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
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P80/00—Climate change mitigation technologies for sector-wide applications
    • Y02P80/20—Climate change mitigation technologies for sector-wide applications using renewable energy
    • 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
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/90—Financial instruments for climate change mitigation, e.g. environmental taxes, subsidies or financing
    • 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
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Landscapes

  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Physics & Mathematics (AREA)
  • Human Resources & Organizations (AREA)
  • Economics (AREA)
  • Health & Medical Sciences (AREA)
  • Theoretical Computer Science (AREA)
  • Strategic Management (AREA)
  • General Physics & Mathematics (AREA)
  • Biophysics (AREA)
  • Tourism & Hospitality (AREA)
  • General Business, Economics & Management (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Marketing (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • General Health & Medical Sciences (AREA)
  • Operations Research (AREA)
  • Game Theory and Decision Science (AREA)
  • Quality & Reliability (AREA)
  • Evolutionary Biology (AREA)
  • Development Economics (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Artificial Intelligence (AREA)
  • Software Systems (AREA)
  • Mathematical Physics (AREA)
  • General Engineering & Computer Science (AREA)
  • Computing Systems (AREA)
  • Molecular Biology (AREA)
  • Evolutionary Computation (AREA)
  • Data Mining & Analysis (AREA)
  • Computational Linguistics (AREA)
  • Biomedical Technology (AREA)
  • Genetics & Genomics (AREA)
  • Physiology (AREA)
  • Educational Administration (AREA)
  • Public Health (AREA)
  • Water Supply & Treatment (AREA)
  • Primary Health Care (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

The embodiment of the invention provides a coordinated planning method for a low-voltage distribution network containing electric heating equipment and a photovoltaic power supply, which comprises the following steps: constructing a topological graph of a low-voltage distribution network containing electric heating equipment and a photovoltaic power supply; establishing a planning model, taking the highest comprehensive benefit of users and power supply enterprises as an optimization target, and providing the operation constraint of the low-voltage distribution network; and performing optimization calculation on the topological graph and the planning model by using a genetic algorithm, and determining the access position and the access capacity of the photovoltaic power supply. The method provided by the embodiment of the invention can avoid the negative influence on the safe operation of the power grid and the reduction of the economic benefit of the photovoltaic power supply caused by the fact that the photovoltaic power supply is connected to the low-voltage power distribution network containing the electric heating equipment, and improves the comprehensive benefits of users and power supply enterprises.

Description

Low-voltage distribution network coordinated planning method containing electric heating equipment and photovoltaic power supply
Technical Field
The invention relates to the technical field of coordinated planning of power distribution networks, in particular to a coordinated planning method for a low-voltage power distribution network containing electric heating equipment and a photovoltaic power supply.
Background
Electric heating equipment is a way to provide a high-quality comfortable environment-friendly heating mode for converting clean electric energy into heat energy, is proved to provide incomparable advantages of other heating modes, and is accepted and accepted by more and more users in the world. The electric heating equipment is used as a consumption means of the photovoltaic power supply, the local consumption capability of the distributed photovoltaic power supply is improved, the coordinated development of photovoltaic engineering can be promoted, and the safe and stable operation of a power grid is ensured. For some areas with slower infrastructure reconstruction, a low-voltage distribution network is still used, and the quality of voltage and power which can be provided by the low-voltage distribution network have certain limits.
When a large number of devices are connected to a weak low-voltage distribution network from both supply and demand ends, a great challenge is brought to the normal operation of the devices. The main body is as follows:
(1) the effect of voltage quality. The output of the distributed photovoltaic power supply has obvious regionality and randomness, and the photovoltaic power supply is usually concentrated in one region, so that the characteristic of high permeability of the regional distributed power supply is formed. The output of the photovoltaic power supply is restricted by environmental factors and has a certain relation with the illumination intensity, the cloud layer can also have great influence on the output of the photovoltaic power supply, and the actual measurement shows that the maximum output change rate of the domestic photovoltaic power station can reach 20-70% per minute. The randomness can have great influence on the normal operation of the power grid, and causes the voltage quality problem of the power distribution network. Meanwhile, the low-voltage distribution network is relatively weak, and the problem that the voltage at the tail end of the system is out of limit and the like can be caused if an access point of a photovoltaic power supply is unreasonable.
(2) The effect of the power characteristics. The photovoltaic power supply with high permeability and the electric heating equipment with high proportion are connected to the power distribution network at the same time, so that the tide distribution in the power distribution network can be changed, and the power distribution network can work under the condition of multiple power supplies. Some extreme conditions can cause the frequency of the system to change and even cause the malfunction of the relay protection device.
(3) User economic impact. The electric heating equipment of the photovoltaic power supply and coal-to-electricity engineering changes the electricity utilization habits of users, and whether the users use the economy also affects the completion degree of the engineering.
In conclusion, the distributed photovoltaic power supply is connected to the low-voltage power distribution network containing the electric heating equipment in a large scale, so that the safe operation of the power grid and the economic benefit of the distributed photovoltaic power supply are greatly influenced, and the maximization of the social resource benefit cannot be achieved.
Therefore, how to avoid negative effects on the safe operation of the power grid and reduction of economic benefits of the photovoltaic power supply caused by the fact that the photovoltaic power supply is connected to the low-voltage power distribution network containing the electric heating equipment, and improve comprehensive economic benefits of power supply enterprises and users to the greatest extent still is a problem to be solved by technical staff in the field.
Disclosure of Invention
The embodiment of the invention provides a coordinated planning method for a low-voltage distribution network containing electric heating equipment and a photovoltaic power supply, which is used for solving the problems that negative effects on safe operation of a power grid and reduction of economic benefits of the photovoltaic power supply caused by the fact that the photovoltaic power supply is connected to the low-voltage distribution network containing the electric heating equipment cannot be processed in the prior art.
In a first aspect, an embodiment of the present invention provides a coordinated planning method for a low-voltage distribution network including an electric heating device and a photovoltaic power supply, including:
constructing a topological graph of a low-voltage distribution network containing electric heating equipment and a photovoltaic power supply;
establishing a planning model, taking the highest comprehensive benefit of users and power supply enterprises as an optimization target, and providing the operation constraint of the low-voltage distribution network;
and performing optimization calculation on the topological graph and the planning model by using a genetic algorithm, and determining the access position and the access capacity of the photovoltaic power supply.
Preferably, in the method, the constructing a topological diagram of the low-voltage distribution network including the electric heating equipment and the photovoltaic power supply specifically includes:
taking the electric heating equipment and the photovoltaic power supply as nodes in the topological graph;
numbering the nodes and the lines in the topological graph by using a depth-first search method;
and marking the capacity of the electric heating equipment on the node of the electric heating equipment.
Preferably, in the method, the establishing a planning model takes the highest comprehensive benefit of users and power supply enterprises as an optimization target, and provides the operation constraint of the low-voltage distribution network, and specifically includes:
constructing an objective function, wherein the objective function is the minimum term of the electricity consumption expenditure of a user and the comprehensive cost of a power grid;
and constructing constraint conditions, wherein the constraint conditions comprise node voltage constraint, line transmission power constraint, photovoltaic power supply access capacity constraint and node power balance constraint.
Preferably, in the method, the objective function is
min[Cuser,Cgrid]
Cuser＝(Weh×Cprice)+Cdevice-(Wpv×Cpricepv) Or Cuser＝Cdevice+Cfee
Cgrid＝CL+CPV-Cd
Wherein, WpvFor photovoltaic power on-line, WehElectric power consumption for electric heating apparatus, CpricepvFor photovoltaic on-line electricity prices, CpriceFor the residents to use the electricity price, CdeviceFor the purchase of electric heating and photovoltaic installations, CfeeSum of fees paid or charged for residents, CLSum of new or renovation costs and annual operating costs for the line, CPVFor photovoltaic power generation investment and annual operating costs, CdFor reduced electricity purchase costs;
the node voltage constraint specifically includes:
voltage V of node iiSatisfies the following conditions: vimin≤Vi≤Vimax；i∈Φ
Wherein, Vimin、VimaxLower and upper limits, respectively, of the voltage at node i; phi is a low-voltage distribution network node set;
the line transmission power constraint specifically includes:
transmission power S of line jjSatisfies the following conditions: i Sj|≤Sjmax；j∈Ω
Wherein S isjmaxThe upper limit of the transmission power of the jth line is shown, and omega is a branch set of the low-voltage distribution network;
the photovoltaic power access capacity constraint specifically comprises:
photovoltaic power supply access capacity P of node iDGiSatisfies the following conditions:
Figure BDA0002331731620000031
wherein, PLiIs the load of node i, s is the permeability of the photovoltaic power supply, ΩgSet of nodes, omega, for photovoltaic power accessLA load node set of the power distribution network;
number M of photovoltaic power sources actually connectedDGSatisfies the following conditions: mDG≤MDGmax
Wherein M isDGmaxThe maximum access number of the photovoltaic power supplies is obtained;
the node power balance constraint specifically includes:
the node satisfies that the injection power is equal to the output power.
Preferably, in the method, the first and second reaction conditions,
Figure BDA0002331731620000032
Figure BDA0002331731620000041
Figure BDA0002331731620000042
wherein k is1iFixing the annual cost coefficient of investment, k, for the photovoltaic power supply i2ijFor a fixed annual investment cost coefficient between node i and load point j, C1ijFor the new establishment of a line between node i and load point j, δijA binary decision variable between the node i and the load point j, if a new line between the node i and the load point j needs to be established, deltaijIs 1, if the line between the node i and the load point j does not need to be newly created, δijIs 0, VijIs the voltage difference between node i and load point j, ZijIs the impedance value between node i and coincidence point j, pf is the power factor, CeFor annual purchase of electricity, N, M and nPVRespectively the number of nodes, the number of load points and the number of feasible photovoltaic power supply installation points in the low-voltage distribution network CfiFor the investment cost of the photovoltaic power supply i, CriThe cost is upgraded for the line i,
Figure BDA0002331731620000043
maximum power, delta, of photovoltaic power source mountable for node iPViIs a binary decision variable of the ith node, delta when the ith node is installed in the photovoltaic power supplyPViIs 1, delta when the ith node is not provided with a photovoltaic power supplyPViIs 0, SpviIs the desired value of the output of the photovoltaic power supply i, pfPViIs the power factor of the photovoltaic power supply i.
Preferably, in the method, the optimizing calculation is performed on the topological graph and the planning model by using a genetic algorithm, and the determining of the access position and the access capacity of the photovoltaic power supply specifically includes:
setting parameters, namely setting the size of a population, selection probability, cross probability, mutation probability and the optimal storage number of the population;
randomly generating an initial population, carrying out chromosome coding, and carrying out iterative operation of a genetic algorithm by taking the target function as a fitness function until a stop criterion is met;
outputting the access position and the access capacity of the photovoltaic power supply in the result;
the chromosome code specifically comprises:
the method comprises the steps of dividing a chromosome into two parts, wherein one part is the access position and the access capacity of the photovoltaic power supply, the length of the chromosome is the same as the number of feasible installation points of the photovoltaic power supply, real number coding is adopted, the other part is whether a line needs to be upgraded and modified, the length of the chromosome is the same as the number of lines, and binary coding is adopted to indicate that the line is modified or not.
Preferably, in the method, the stopping criterion specifically includes:
when the iteration times are larger than the preset maximum iteration times, stopping the iteration; alternatively, the first and second electrodes may be,
and stopping the iteration when the difference value between the new generation group and the previous generation group is smaller than a preset threshold value.
In a second aspect, an embodiment of the present invention provides a low-voltage distribution network coordination planning apparatus including an electric heating device and a photovoltaic power supply, including:
the topological unit is used for constructing a topological graph of a low-voltage distribution network containing electric heating equipment and a photovoltaic power supply;
the planning unit is used for establishing a planning model, taking the highest comprehensive benefit of users and power supply enterprises as an optimization target and providing the operation constraint of the low-voltage distribution network;
and the optimization unit is used for performing optimization calculation on the topological graph and the planning model by using a genetic algorithm and determining the access position and the access capacity of the photovoltaic power supply.
In a third aspect, an embodiment of the present invention provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor executes the program to implement the steps of the method for coordinately planning a low-voltage distribution network including an electric heating device and a photovoltaic power supply according to the first aspect.
In a fourth aspect, an embodiment of the present invention provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the method for coordinated planning of a low-voltage distribution network including an electric heating device and a photovoltaic power source, as provided in the first aspect.
According to the low-voltage distribution network coordination planning method containing the electric heating equipment and the photovoltaic power supply, provided by the embodiment of the invention, a topological graph of the low-voltage distribution network containing the electric heating equipment and the photovoltaic power supply is constructed; establishing a planning model, taking the highest comprehensive benefit of users and power supply enterprises as an optimization target, providing the operation constraint of the low-voltage power distribution network, performing optimization calculation on the topological graph and the planning model by using a genetic algorithm, and determining the access position and the access capacity of the photovoltaic power supply, so that the constraint of physical parameters during the operation of the power grid is considered when the photovoltaic power supply is accessed into the low-voltage power grid, and the comprehensive benefit of the users and the power supply enterprises is also considered. Therefore, negative effects on safe operation of a power grid and reduction of economic benefits of the photovoltaic power supply caused by the fact that the photovoltaic power supply is connected to a low-voltage power distribution network containing electric heating equipment are avoided, and comprehensive benefits of users and power supply enterprises are improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, a brief description will be given below of the drawings required for the embodiments or the technical solutions in the prior art, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a schematic flow chart of a coordinated planning method for a low-voltage distribution network including electric heating equipment and a photovoltaic power supply according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a genetic algorithm provided by an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a low-voltage distribution network coordination planning device including an electric heating device and a photovoltaic power supply according to an embodiment of the present invention;
fig. 4 is a schematic physical structure diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, 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 some, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without inventive effort based on the embodiments of the present invention, are within the scope of the present invention.
The existing scheme that the photovoltaic power supply is connected to the low-voltage power distribution network containing the electric heating equipment generally has the problems of negative influence on safe operation of the power grid and reduction of economic benefits of the photovoltaic power supply, and comprehensive benefits of users and power supply enterprises cannot be guaranteed. Therefore, the embodiment of the invention provides a coordinated planning method for a low-voltage distribution network containing electric heating equipment and a photovoltaic power supply. Fig. 1 is a schematic flow chart of a method for coordinately planning a low-voltage distribution network including electric heating equipment and a photovoltaic power supply according to an embodiment of the present invention, where as shown in fig. 1, the method includes:
and 110, constructing a topological graph of a low-voltage distribution network containing electric heating equipment and a photovoltaic power supply.
Specifically, according to a wiring diagram of a low-voltage distribution network, a topological diagram is constructed, in which an electric heating device and a photovoltaic power supply are used as nodes, and a line between the electric heating device and the electric heating device, a line between the electric heating device and the photovoltaic power supply, and a line between the photovoltaic power supply and the photovoltaic power supply are used as branches. Preferably, in order to accurately represent the connection relationship between the nodes and other nodes in the low topology graph, the structure of the branches and the connection relationship between the branches, the nodes and the lines are numbered.
And 120, establishing a planning model, taking the highest comprehensive benefit of the user and the power supply enterprise as an optimization target, and providing the operation constraint of the low-voltage distribution network.
Specifically, the planning model is established, that is, the objective function and the constraint condition of the planning are established, wherein the minimization of the objective function is realized to enable the comprehensive benefit of users and power supply enterprises to be the highest, and the constraint condition is met so that the operation of the low-voltage distribution network does not affect the voltage quality and the power quality of the power grid and does not cause negative influence on the operation of the power grid.
And step 130, performing optimization calculation on the topological graph and the planning model by using a genetic algorithm, and determining the access position and the access capacity of the photovoltaic power supply.
Specifically, a topological graph meeting the planning model is found by using a genetic algorithm, and the final access position and the final access capacity of the photovoltaic power supply are determined according to the access position and the access capacity of the photovoltaic power supply in the topological graph.
According to the method provided by the embodiment of the invention, a topological graph of a low-voltage distribution network containing electric heating equipment and a photovoltaic power supply is constructed; establishing a planning model, taking the highest comprehensive benefit of users and power supply enterprises as an optimization target, providing the operation constraint of the low-voltage power distribution network, performing optimization calculation on the topological graph and the planning model by using a genetic algorithm, and determining the access position and the access capacity of the photovoltaic power supply, so that the constraint of physical parameters during the operation of the power grid is considered when the photovoltaic power supply is accessed into the low-voltage power grid, and the comprehensive benefit of the users and the power supply enterprises is also considered. Therefore, negative effects on safe operation of a power grid and reduction of economic benefits of the photovoltaic power supply caused by the fact that the photovoltaic power supply is connected to a low-voltage power distribution network containing electric heating equipment are avoided, and comprehensive benefits of users and power supply enterprises are improved.
Based on the above embodiment, in the method, the constructing a topological graph of the low-voltage distribution network including the electric heating device and the photovoltaic power supply specifically includes:
taking the electric heating equipment and the photovoltaic power supply as nodes in the topological graph;
numbering the nodes and the lines in the topological graph by using a depth-first search method;
and marking the capacity of the electric heating equipment on the node of the electric heating equipment.
Specifically, when the topological graph is constructed, according to a wiring graph of the low-voltage distribution network, the electric heating equipment and the photovoltaic power supply are used as nodes in the topological graph, and a line between the electric heating equipment and the electric heating equipment, a line between the electric heating equipment and the photovoltaic power supply, and a line between the photovoltaic power supply and the photovoltaic power supply are used as branches in the topological graph. In order to accurately represent the connection relationship between the nodes and other nodes in the low-voltage distribution network and the connection relationship between the branch structures and the branches, the nodes and the lines in the topological graph are numbered by using a depth-first search method, and meanwhile, the nodes of the electric heating equipment are marked with the capacity of the electric heating equipment, wherein the capacity is the power.
Based on any one of the above embodiments, in the method, the establishing a planning model, with the highest comprehensive benefit of the user and the power supply enterprise as an optimization target, and providing the operation constraint of the low-voltage distribution network specifically includes:
constructing an objective function, wherein the objective function is the minimum term of the electricity consumption expenditure of a user and the comprehensive cost of a power grid;
and constructing constraint conditions, wherein the constraint conditions comprise node voltage constraint, line transmission power constraint, photovoltaic power supply access capacity constraint and node power balance constraint.
Specifically, the planning model is established, that is, an objective function and a constraint function are established, the objective function considers the power consumption expenditure of the user and also considers the comprehensive cost of the power supply enterprise operating power grid, so the objective function is the minimum term of the power consumption expenditure of the user and the comprehensive cost of the power grid, and the constraint condition considers the limitation of each physical parameter during the operation of the power grid, such as node voltage constraint, line transmission power constraint, photovoltaic power supply access capacity constraint and node power balance constraint.
In the method according to any of the above embodiments, the objective function is
min[Cuser,Cgrid]
Cuser＝(Weh×Cprice)+Cdevice-(Wpv×Cpricepv) Or Cuser＝Cdevice+Cfee
Cgrid＝CL+CPV-Cd
Wherein, WpvFor photovoltaic power on-line, WehElectric power consumption for electric heating apparatus, CpricepvFor photovoltaic on-line electricity prices, CpriceFor the residents to use the electricity price, CdeviceFor the purchase of electric heating and photovoltaic installations, CfeeSum of fees paid or charged for residents, CLSum of new or renovation costs and annual operating costs for the line, CPVFor photovoltaic power generation investment and annual operating costs, CdFor reduced electricity purchase costs;
the node voltage constraint specifically includes:
voltage V of node iiSatisfies the following conditions: vimin≤Vi≤Vimax；i∈Φ
Wherein, Vimin、VimaxLower and upper limits, respectively, of the voltage at node i; phi is a low-voltage distribution network node set;
the line transmission power constraint specifically includes:
transmission power S of line jjSatisfies the following conditions: i Sj|≤Sjmax；j∈Ω
Wherein S isjmaxThe upper limit of the transmission power of the jth line is shown, and omega is a branch set of the low-voltage distribution network;
the photovoltaic power access capacity constraint specifically comprises:
photovoltaic power supply access capacity P of node iDGiSatisfies the following conditions:
Figure BDA0002331731620000091
wherein, PLiIs the load of node i, s is the permeability of the photovoltaic power supply, ΩgSet of nodes, omega, for photovoltaic power accessLA load node set of the power distribution network;
number M of photovoltaic power sources actually connectedDGSatisfies the following conditions: mDG≤MDGmax
Wherein M isDGmaxThe maximum access number of the photovoltaic power supplies is obtained;
the node power balance constraint specifically includes:
the node satisfies that the injection power is equal to the output power.
Specifically, the objective function is constructed in consideration of both the user economy and the grid economy.
For the user economy, the main content is the power generation income of the photovoltaic power supply and the cost of the use of the electric heating equipment, and if the power generation of the photovoltaic power supply can better reduce the power consumption expenditure of the user, the better the user economy is.
User expenditure Cuser＝(Weh×Cprice)+Cdevice-(Wpv×Cpricepv)；
Wherein, WpvFor photovoltaic power on-line, WehFor electric heating equipment，CpricepvFor photovoltaic on-line electricity prices, CpriceFor the residents to use the electricity price, CdeviceFor the purchase of electric heating and photovoltaic installations, CfeeAnd paying or receiving the sum of the electricity fees for the residents.
The above user expenditure CuserCan be simplified to Cuser＝Cdevice+Cfee；
Wherein, CdeviceFor the purchase of electric heating and photovoltaic installations, CfeeAnd paying or receiving the sum of the electricity fees for the residents.
For the economy of the power grid, the main content is the sum of the line new construction or reconstruction cost and the operation cost, the photovoltaic power generation investment and the operation cost and the reduced electricity purchasing cost.
Electric network expenditure Cgrid＝CL+CPV-Cd；
Wherein, CLSum of new or renovation costs and annual operating costs for the line, CPVFor photovoltaic power generation investment and annual operating costs, CdIn order to reduce the electricity purchasing cost.
The node voltage constraint specifically includes:
voltage V of node iiSatisfies the following conditions: vimin≤Vi≤Vimax；i∈Φ
Wherein, Vimin、VimaxLower and upper limits, respectively, of the voltage at node i; phi is a low-voltage distribution network node set;
the line transmission power constraint specifically includes:
transmission power S of line jjSatisfies the following conditions: i Sj|≤Sjmax；j∈Ω
Wherein S isjmaxThe upper limit of the transmission power of the jth line is shown, and omega is a branch set of the low-voltage distribution network;
the photovoltaic power access capacity constraint specifically comprises:
photovoltaic power supply access capacity P of node iDGiSatisfies the following conditions:
Figure BDA0002331731620000101
wherein, PLiIs the load of node i, s is the permeability of the photovoltaic power supply, ΩgSet of nodes, omega, for photovoltaic power accessLA load node set of the power distribution network;
number M of photovoltaic power sources actually connectedDGSatisfies the following conditions: mDG≤MDGmax
Wherein M isDGmaxThe maximum access number of the photovoltaic power supplies is obtained;
the node power balance constraint specifically includes:
the node satisfies that the injection power is equal to the output power.
In accordance with any of the above embodiments, in the method,
Figure BDA0002331731620000102
Figure BDA0002331731620000103
Figure BDA0002331731620000104
wherein k is1iFixing the annual cost coefficient of investment, k, for the photovoltaic power supply i2ijFor a fixed annual investment cost coefficient between node i and load point j, C1ijFor the new establishment of a line between node i and load point j, δijA binary decision variable between the node i and the load point j, if a new line between the node i and the load point j needs to be established, deltaijIs 1, if the line between the node i and the load point j does not need to be newly created, δijIs 0, VijIs the voltage difference between node i and load point j, ZijIs the impedance value between node i and coincidence point j, pf is the power factor, CeFor annual purchase of electricity, N, M and nPVRespectively the number of nodes, the number of load points and the number of feasible photovoltaic power supply installation points in the low-voltage distribution network CfiFor the investment cost of the photovoltaic power supply i, CriThe cost is upgraded for the line i,
Figure BDA0002331731620000105
maximum power, delta, of photovoltaic power source mountable for node iPViIs a binary decision variable of the ith node, delta when the ith node is installed in the photovoltaic power supplyPViIs 1, delta when the ith node is not provided with a photovoltaic power supplyPViIs 0, SpviIs the desired value of the output of the photovoltaic power supply i, pfPViIs the power factor of the photovoltaic power supply i.
Based on any one of the above embodiments, in the method, the using a genetic algorithm to perform optimization calculation on the topological graph and the planning model, and determining the access position and the access capacity of the photovoltaic power supply specifically includes:
setting parameters, namely setting the size of a population, selection probability, cross probability, mutation probability and the optimal storage number of the population;
randomly generating an initial population, carrying out chromosome coding, and carrying out iterative operation of a genetic algorithm by taking the target function as a fitness function until a stop criterion is met;
outputting the access position and the access capacity of the photovoltaic power supply in the result;
the chromosome code specifically comprises:
the method comprises the steps of dividing a chromosome into two parts, wherein one part is the access position and the access capacity of the photovoltaic power supply, the length of the chromosome is the same as the number of feasible installation points of the photovoltaic power supply, real number coding is adopted, the other part is whether a line needs to be upgraded and modified, the length of the chromosome is the same as the number of lines, and binary coding is adopted to indicate that the line is modified or not.
Specifically, a chromosome coding mode in the genetic algorithm is defined, wherein the chromosome coding mode refers to the fact that a problem to be solved is converted into a search space which can be calculated by the genetic algorithm, and the accuracy and the quality of the coding mode influence the accuracy degree of subsequent genetic operation and the solving efficiency. In the embodiment of the invention, one chromosome is composed of two parts, one part is used for representing the access position and the access capacity of a photovoltaic power supply, the length of the part of the chromosome is the same as the number of feasible installation points of the photovoltaic power supply, the value of a gene corresponding to the feasible installation point of each photovoltaic power supply is the numerical value of the access capacity of the part of the chromosome accessed to the photovoltaic power supply, and the part of the chromosome adopts real number coding; the other part is used for indicating whether the line needs to be upgraded and modified, the length of the chromosome of the part is the same as the number of the lines, binary coding is adopted, the value of the gene corresponding to each line is 0 or 1, and the line is not modified or modified respectively.
And then, performing optimization calculation of a genetic algorithm, firstly, performing parameter configuration, and setting the size, selection probability, cross probability, mutation probability and the optimal storage number of the population, wherein preferably, the genetic parameters are set as follows: the population size is M60, and the cross probability is Pc0.7, the mutation probability is PmThe number of optimally stored bits is 2, 0.05. Then, an initial population is randomly generated, and a large number of infeasible solutions can be generated in the calculation process due to the random generation mode, and the infeasible solutions cannot meet the inequality constraint conditions and should be removed in time. It is necessary to verify and screen the results obtained at each step. And carrying out chromosome coding on the original number in the initial population, and carrying out iterative operation of a genetic algorithm by taking the target function as a fitness function until a stopping criterion is met. And finally, outputting the access position and the access capacity of the photovoltaic power supply in the result. And screening and verifying all individuals through multiple iterations until all individuals in the population meet the requirements. In the optimization process, in the range specified by the fitness function, chromosome structures among individuals are crossed with each other, the chromosomes of the individuals are continuously varied, and local optimal solutions or infeasible solutions are removed by utilizing the screening function of the fitness function, so that the global optimal solution is finally obtained. In the calculation process, an optimal storage strategy is adopted, in order to keep the fitness of the inherited individuals in the population higher than that of the previous generation, the most elegant individual chromosomes in the previous generation are retained to the previous generation, the individuals with lower fitness are replaced, and the population is ensured to converge towards the optimal direction.
Fig. 2 is a schematic flow chart of a genetic algorithm provided in an embodiment of the present invention. As shown in fig. 2, after the optimization calculation of the genetic algorithm is started, original data is input first, the original data is randomly selected, then chromosome coding is performed on the original data to generate an initial population, then fitness function values of all individuals in the population are calculated, the fitness function is a target function in the above embodiment, then selection, mutation and cross operation are performed to generate a new generation population, whether the iteration stopping criterion is met or not is judged, if yes, the latest generation population is used as an output result, if not, the step of calculating the fitness function values of all the individuals in the population is returned, the step is continued downwards according to the flow until the stopping criterion is met, and the decoding and the output result are finally finished.
Based on any of the above embodiments, in the method, the stopping criterion specifically includes:
when the iteration times are larger than the preset maximum iteration times, stopping the iteration; alternatively, the first and second electrodes may be,
and stopping the iteration when the difference value between the new generation group and the previous generation group is smaller than a preset threshold value.
Specifically, there are two stopping criteria, one of which is to set a maximum iteration number, and when the iteration number reaches the maximum iteration number, stopping the iteration and outputting a result; and the other method is to calculate the difference between the new generation group and the previous generation group, stop iteration and output the result when the difference is smaller than a preset threshold value.
Based on any one of the above embodiments, an embodiment of the present invention provides a low-voltage distribution network coordination planning device including an electric heating device and a photovoltaic power supply, and fig. 3 is a schematic structural diagram of the low-voltage distribution network coordination planning device including an electric heating device and a photovoltaic power supply according to the embodiment of the present invention. As shown in fig. 3, the apparatus includes a topology unit 310, a planning unit 320, and an optimization unit 330, wherein,
the topology unit 310 is used for constructing a topology map of a low-voltage distribution network containing electric heating equipment and a photovoltaic power supply;
the planning unit 320 is configured to establish a planning model, take the highest comprehensive benefit of a user and a power supply enterprise as an optimization target, and provide the operation constraint of the low-voltage distribution network;
the optimizing unit 330 is configured to perform optimization calculation on the topological graph and the planning model by using a genetic algorithm, and determine an access position and an access capacity of the photovoltaic power supply.
According to the device provided by the embodiment of the invention, a topological graph of a low-voltage distribution network containing electric heating equipment and a photovoltaic power supply is constructed; establishing a planning model, taking the highest comprehensive benefit of users and power supply enterprises as an optimization target, providing the operation constraint of the low-voltage power distribution network, performing optimization calculation on the topological graph and the planning model by using a genetic algorithm, and determining the access position and the access capacity of the photovoltaic power supply, so that the constraint of physical parameters during the operation of the power grid is considered when the photovoltaic power supply is accessed into the low-voltage power grid, and the comprehensive benefit of the users and the power supply enterprises is also considered. Therefore, negative effects on safe operation of a power grid and reduction of economic benefits of the photovoltaic power supply caused by the fact that the photovoltaic power supply is connected to a low-voltage power distribution network containing electric heating equipment are avoided, and comprehensive benefits of users and power supply enterprises are improved.
In the apparatus according to any of the above embodiments, the topology unit is, in particular,
taking the electric heating equipment and the photovoltaic power supply as nodes in the topological graph;
numbering the nodes and the lines in the topological graph by using a depth-first search method;
and marking the capacity of the electric heating equipment on the node of the electric heating equipment.
In the apparatus according to any of the above embodiments, the planning unit is, in particular,
constructing an objective function, wherein the objective function is the minimum term of the electricity consumption expenditure of a user and the comprehensive cost of a power grid;
and constructing constraint conditions, wherein the constraint conditions comprise node voltage constraint, line transmission power constraint, photovoltaic power supply access capacity constraint and node power balance constraint.
In the device according to any of the above embodiments, the objective function is
min[Cuser,Cgrid]
Cuser＝(Weh×Cprice)+Cdevice-(Wpv×Cpricepv) Or Cuser＝Cdevice+Cfee
Cgrid＝CL+CPV-Cd
Wherein, WpvFor photovoltaic power on-line, WehElectric power consumption for electric heating apparatus, CpricepvFor photovoltaic on-line electricity prices, CpriceFor the residents to use the electricity price, CdeviceFor the purchase of electric heating and photovoltaic installations, CfeeSum of fees paid or charged for residents, CLSum of new or renovation costs and annual operating costs for the line, CPVFor photovoltaic power generation investment and annual operating costs, CdFor reduced electricity purchase costs;
the node voltage constraint specifically includes:
voltage V of node iiSatisfies the following conditions: vimin≤Vi≤Vimax；i∈Φ
Wherein, Vimin、VimaxLower and upper limits, respectively, of the voltage at node i; phi is a low-voltage distribution network node set;
the line transmission power constraint specifically includes:
transmission power S of line jjSatisfies the following conditions: i Sj|≤Sjmax；j∈Ω
Wherein S isjmaxThe upper limit of the transmission power of the jth line is shown, and omega is a branch set of the low-voltage distribution network;
the photovoltaic power access capacity constraint specifically comprises:
photovoltaic power supply access capacity P of node iDGiSatisfies the following conditions:
Figure BDA0002331731620000141
wherein, PLiIs the load of node i, s is the permeability of the photovoltaic power supply, ΩgSet of nodes, omega, for photovoltaic power accessLA load node set of the power distribution network;
number of photovoltaic power sources actually connectedQuantity MDGSatisfies the following conditions: mDG≤MDGmax
Wherein M isDGmaxThe maximum access number of the photovoltaic power supplies is obtained;
the node power balance constraint specifically includes:
the node satisfies that the injection power is equal to the output power.
In accordance with any of the above embodiments, in the apparatus,
Figure BDA0002331731620000142
Figure BDA0002331731620000143
Figure BDA0002331731620000144
wherein k is1iFixing the annual cost coefficient of investment, k, for the photovoltaic power supply i2ijFor a fixed annual investment cost coefficient between node i and load point j, C1ijFor the new establishment of a line between node i and load point j, δijA binary decision variable between the node i and the load point j, if a new line between the node i and the load point j needs to be established, deltaijIs 1, if the line between the node i and the load point j does not need to be newly created, δijIs 0, VijIs the voltage difference between node i and load point j, ZijIs the impedance value between node i and coincidence point j, pf is the power factor, CeFor annual purchase of electricity, N, M and nPVRespectively the number of nodes, the number of load points and the number of feasible photovoltaic power supply installation points in the low-voltage distribution network CfiFor the investment cost of the photovoltaic power supply i, CriThe cost is upgraded for the line i,
Figure BDA0002331731620000145
maximum power, delta, of photovoltaic power source mountable for node iPViIs a binary decision variable of the ith node, delta when the ith node is installed in the photovoltaic power supplyPViThe number of the carbon atoms is 1,delta when the ith node is not provided with a photovoltaic power supplyPViIs 0, SpviIs the desired value of the output of the photovoltaic power supply i, pfPViIs the power factor of the photovoltaic power supply i.
In the apparatus according to any of the above embodiments, the optimization unit is, in particular,
setting parameters, namely setting the size of a population, selection probability, cross probability, mutation probability and the optimal storage number of the population;
randomly generating an initial population, carrying out chromosome coding, and carrying out iterative operation of a genetic algorithm by taking the target function as a fitness function until a stop criterion is met;
outputting the access position and the access capacity of the photovoltaic power supply in the result;
the chromosome code specifically comprises:
the method comprises the steps of dividing a chromosome into two parts, wherein one part is the access position and the access capacity of the photovoltaic power supply, the length of the chromosome is the same as the number of feasible installation points of the photovoltaic power supply, real number coding is adopted, the other part is whether a line needs to be upgraded and modified, the length of the chromosome is the same as the number of lines, and binary coding is adopted to indicate that the line is modified or not.
Based on any one of the above embodiments, in the apparatus, the stopping criterion specifically includes:
when the iteration times are larger than the preset maximum iteration times, stopping the iteration; alternatively, the first and second electrodes may be,
and stopping the iteration when the difference value between the new generation group and the previous generation group is smaller than a preset threshold value.
Fig. 4 is a schematic entity structure diagram of an electronic device according to an embodiment of the present invention, and as shown in fig. 4, the electronic device may include: a processor (processor)401, a communication Interface (communication Interface)402, a memory (memory)403 and a communication bus 404, wherein the processor 401, the communication Interface 402 and the memory 403 complete communication with each other through the communication bus 404. The processor 401 may call a computer program stored in the memory 403 and operable on the processor 401 to execute the method for coordinately planning a low voltage distribution network including electric heating equipment and a photovoltaic power supply provided in the foregoing embodiments, for example, the method includes: constructing a topological graph of a low-voltage distribution network containing electric heating equipment and a photovoltaic power supply; establishing a planning model, taking the highest comprehensive benefit of users and power supply enterprises as an optimization target, and providing the operation constraint of the low-voltage distribution network; and performing optimization calculation on the topological graph and the planning model by using a genetic algorithm, and determining the access position and the access capacity of the photovoltaic power supply.
In addition, the logic instructions in the memory 403 may be implemented in the form of software functional units and stored in a computer readable storage medium when the software functional units are sold or used as independent products. Based on such understanding, the technical solutions of the embodiments of the present invention may be essentially implemented or make a contribution to the prior art, or may be implemented in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
An embodiment of the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program is implemented by a processor to execute the method for coordinately planning a low-voltage distribution network including electric heating equipment and a photovoltaic power supply provided in the foregoing embodiments, and for example, the method includes: constructing a topological graph of a low-voltage distribution network containing electric heating equipment and a photovoltaic power supply; establishing a planning model, taking the highest comprehensive benefit of users and power supply enterprises as an optimization target, and providing the operation constraint of the low-voltage distribution network; and performing optimization calculation on the topological graph and the planning model by using a genetic algorithm, and determining the access position and the access capacity of the photovoltaic power supply.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (10)

1. A low-voltage distribution network coordinated planning method containing electric heating equipment and a photovoltaic power supply is characterized by comprising the following steps:
constructing a topological graph of a low-voltage distribution network containing electric heating equipment and a photovoltaic power supply;
establishing a planning model, taking the highest comprehensive benefit of users and power supply enterprises as an optimization target, and providing the operation constraint of the low-voltage distribution network;
and performing optimization calculation on the topological graph and the planning model by using a genetic algorithm, and determining the access position and the access capacity of the photovoltaic power supply.
2. The method for the coordinated planning of the low-voltage distribution network including the electric heating equipment and the photovoltaic power supply according to claim 1, wherein the constructing of the topological graph of the low-voltage distribution network including the electric heating equipment and the photovoltaic power supply specifically comprises:
taking the electric heating equipment and the photovoltaic power supply as nodes in the topological graph;
numbering the nodes and the lines in the topological graph by using a depth-first search method;
and marking the capacity of the electric heating equipment on the node of the electric heating equipment.
3. The method according to claim 2, wherein the establishing of the planning model takes the highest comprehensive benefit of users and power supply enterprises as an optimization target and provides the operation constraint of the low-voltage distribution network, and specifically comprises the following steps:
constructing an objective function, wherein the objective function is the minimum term of the electricity consumption expenditure of a user and the comprehensive cost of a power grid;
and constructing constraint conditions, wherein the constraint conditions comprise node voltage constraint, line transmission power constraint, photovoltaic power supply access capacity constraint and node power balance constraint.
4. The method according to claim 3, wherein the objective function is
min[Cuser,Cgrid]
Cuser＝(Weh×Cprice)+Cdevice-(Wpv×Cpricepv) Or Cuser＝Cdevice+Cfee
Cgrid＝CL+CPV-Cd
Wherein, WpvFor photovoltaic power on-line, WehElectric power consumption for electric heating apparatus, CpricepvFor photovoltaic on-line electricity prices, CpriceFor the residents to use the electricity price, CdeviceFor the purchase of electric heating and photovoltaic installations, CfeeSum of fees paid or charged for residents, CLSum of new or renovation costs and annual operating costs for the line, CPVFor photovoltaic power generation investment and annual operating costs, CdFor reduced electricity purchase costs;
the node voltage constraint specifically includes:
voltage V of node iiSatisfies the following conditions: vimin≤Vi≤Vimax；i∈Φ
Wherein, Vimin、VimaxLower and upper limits, respectively, of the voltage at node i; phi is a low-voltage distribution network node set;
the line transmission power constraint specifically includes:
transmission power S of line jjSatisfies the following conditions: i Sj|≤Sjmax；j∈Ω
Wherein S isjmaxThe upper limit of the transmission power of the jth line is shown, and omega is a branch set of the low-voltage distribution network;
the photovoltaic power access capacity constraint specifically comprises:
photovoltaic power supply access capacity P of node iDGiSatisfies the following conditions:
Figure FDA0002331731610000021
wherein, PLiIs the load of node i, s is the permeability of the photovoltaic power supply, ΩgSet of nodes, omega, for photovoltaic power accessLA load node set of the power distribution network;
number M of photovoltaic power sources actually connectedDGSatisfies the following conditions: mDG≤MDGmax
Wherein M isDGmaxThe maximum access number of the photovoltaic power supplies is obtained;
the node power balance constraint specifically includes:
the node satisfies that the injection power is equal to the output power.
5. The method for the coordinated planning of a low-voltage distribution network comprising electric heating equipment and photovoltaic power supplies according to claim 4,
Figure FDA0002331731610000022
Figure FDA0002331731610000023
Figure FDA0002331731610000024
wherein k is1iFixing the annual cost coefficient of investment, k, for the photovoltaic power supply i2ijFor a fixed annual investment cost coefficient between node i and load point j, C1ijFor the new establishment of a line between node i and load point j, δijA binary decision variable between the node i and the load point j, if a new line between the node i and the load point j needs to be established, deltaijIs 1, if the line between the node i and the load point j does not need to be newly created, δijIs 0, VijIs the voltage difference between node i and load point j, ZijIs the impedance value between node i and coincidence point j, pf is the power factor, CeFor annual purchase of electricity, N, M and nPVRespectively the number of nodes, the number of load points and the number of feasible photovoltaic power supply installation points in the low-voltage distribution network CfiFor the investment cost of the photovoltaic power supply i, CriThe cost is upgraded for the line i,
Figure FDA0002331731610000031
maximum power, delta, of photovoltaic power source mountable for node iPViIs a binary decision variable of the ith node, delta when the ith node is installed in the photovoltaic power supplyPViIs 1, delta when the ith node is not provided with a photovoltaic power supplyPViIs 0, SpviIs the desired value of the output of the photovoltaic power supply i, pfPViIs the power factor of the photovoltaic power supply i.
6. The method for the coordinated planning of the low-voltage distribution network including electric heating equipment and photovoltaic power supplies according to any one of claims 3 to 5, wherein the optimal calculation is performed on the topological graph and the planning model by using a genetic algorithm to determine the access position and the access capacity of the photovoltaic power supply, and specifically comprises:
setting parameters, namely setting the size of a population, selection probability, cross probability, mutation probability and the optimal storage number of the population;
randomly generating an initial population, carrying out chromosome coding, and carrying out iterative operation of a genetic algorithm by taking the target function as a fitness function until a stop criterion is met;
outputting the access position and the access capacity of the photovoltaic power supply in the result;
the chromosome code specifically comprises:
the method comprises the steps of dividing a chromosome into two parts, wherein one part is the access position and the access capacity of the photovoltaic power supply, the length of the chromosome is the same as the number of feasible installation points of the photovoltaic power supply, real number coding is adopted, the other part is whether a line needs to be upgraded and modified, the length of the chromosome is the same as the number of lines, and binary coding is adopted to indicate that the line is modified or not.
7. The method for the coordinated planning of the low-voltage distribution network comprising electric heating equipment and photovoltaic power supplies according to claim 6, wherein the stopping criteria specifically include:
when the iteration times are larger than the preset maximum iteration times, stopping the iteration; alternatively, the first and second electrodes may be,
and stopping the iteration when the difference value between the new generation group and the previous generation group is smaller than a preset threshold value.
8. The utility model provides a contain electric heating equipment and photovoltaic power supply's low-voltage distribution network coordination planning device which characterized in that includes:
the topological unit is used for constructing a topological graph of a low-voltage distribution network containing electric heating equipment and a photovoltaic power supply;
the planning unit is used for establishing a planning model, taking the highest comprehensive benefit of users and power supply enterprises as an optimization target and providing the operation constraint of the low-voltage distribution network;
and the optimization unit is used for performing optimization calculation on the topological graph and the planning model by using a genetic algorithm and determining the access position and the access capacity of the photovoltaic power supply.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor, when executing the program, carries out the steps of the method for the coordinated planning of a low-voltage distribution network comprising an electric heating device and a photovoltaic power supply according to any one of claims 1 to 7.
10. A non-transitory computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method for the coordinated planning of a low-voltage distribution network comprising electric heating devices and photovoltaic power sources according to any one of claims 1 to 7.
CN201911338950.4A 2019-12-23 2019-12-23 Low-voltage distribution network coordinated planning method containing electric heating equipment and photovoltaic power supply Pending CN113095611A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201911338950.4A CN113095611A (en) 2019-12-23 2019-12-23 Low-voltage distribution network coordinated planning method containing electric heating equipment and photovoltaic power supply

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201911338950.4A CN113095611A (en) 2019-12-23 2019-12-23 Low-voltage distribution network coordinated planning method containing electric heating equipment and photovoltaic power supply

Publications (1)

Publication Number Publication Date
CN113095611A true CN113095611A (en) 2021-07-09

Family

ID=76662897

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201911338950.4A Pending CN113095611A (en) 2019-12-23 2019-12-23 Low-voltage distribution network coordinated planning method containing electric heating equipment and photovoltaic power supply

Country Status (1)

Country Link
CN (1) CN113095611A (en)

Citations (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103903073A (en) * 2014-04-23 2014-07-02 河海大学 Planning method and system for optimizing micro-grid containing distributed power sources and stored energy
WO2015196743A1 (en) * 2014-06-25 2015-12-30 国家电网公司 Active distribution network reconfiguration method and apparatus
CN108154272A (en) * 2017-12-29 2018-06-12 上海电力学院 A kind of increment distribution network planning method for adapting to increment distribution network service and decontroling
CN108256723A (en) * 2017-11-27 2018-07-06 国网河北省电力公司经济技术研究院 Coal changes the assessment of economic benefit method and terminal device for being electrically accessed power grid
CN108399505A (en) * 2018-03-12 2018-08-14 国网河北省电力有限公司经济技术研究院 Distributed photovoltaic power access capacity planing method and terminal device
CN109214593A (en) * 2018-10-19 2019-01-15 天津大学 A kind of active distribution network power supply capacity multi-objective assessment method
CN109245096A (en) * 2018-10-19 2019-01-18 天津大学 A kind of active distribution network net capability calculation method
CN109255558A (en) * 2018-10-31 2019-01-22 国家电网有限公司 A kind of site selecting method and system of heat storage electric boiler access power distribution network
CN109636058A (en) * 2018-12-24 2019-04-16 国网北京市电力公司 Power distribution network treating method and apparatus
CN110009122A (en) * 2018-12-27 2019-07-12 国网北京市电力公司 Family utilizes system capacity Optimization Scheduling and system with comprehensive energy of providing multiple forms of energy to complement each other
CN110110893A (en) * 2019-04-03 2019-08-09 国网新疆电力有限公司昌吉供电公司 The distribution network structure optimization method of extensive electric heating equipment access
CN110148964A (en) * 2019-05-27 2019-08-20 武汉理工大学 A kind of control method for the distributed photovoltaic power generation system changing electrical engineering towards coal
CN110245811A (en) * 2018-03-08 2019-09-17 国网新疆电力有限公司博尔塔拉供电公司 A kind of distribution network planning method

Patent Citations (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103903073A (en) * 2014-04-23 2014-07-02 河海大学 Planning method and system for optimizing micro-grid containing distributed power sources and stored energy
WO2015196743A1 (en) * 2014-06-25 2015-12-30 国家电网公司 Active distribution network reconfiguration method and apparatus
CN108256723A (en) * 2017-11-27 2018-07-06 国网河北省电力公司经济技术研究院 Coal changes the assessment of economic benefit method and terminal device for being electrically accessed power grid
CN108154272A (en) * 2017-12-29 2018-06-12 上海电力学院 A kind of increment distribution network planning method for adapting to increment distribution network service and decontroling
CN110245811A (en) * 2018-03-08 2019-09-17 国网新疆电力有限公司博尔塔拉供电公司 A kind of distribution network planning method
CN108399505A (en) * 2018-03-12 2018-08-14 国网河北省电力有限公司经济技术研究院 Distributed photovoltaic power access capacity planing method and terminal device
CN109245096A (en) * 2018-10-19 2019-01-18 天津大学 A kind of active distribution network net capability calculation method
CN109214593A (en) * 2018-10-19 2019-01-15 天津大学 A kind of active distribution network power supply capacity multi-objective assessment method
CN109255558A (en) * 2018-10-31 2019-01-22 国家电网有限公司 A kind of site selecting method and system of heat storage electric boiler access power distribution network
CN109636058A (en) * 2018-12-24 2019-04-16 国网北京市电力公司 Power distribution network treating method and apparatus
CN110009122A (en) * 2018-12-27 2019-07-12 国网北京市电力公司 Family utilizes system capacity Optimization Scheduling and system with comprehensive energy of providing multiple forms of energy to complement each other
CN110110893A (en) * 2019-04-03 2019-08-09 国网新疆电力有限公司昌吉供电公司 The distribution network structure optimization method of extensive electric heating equipment access
CN110148964A (en) * 2019-05-27 2019-08-20 武汉理工大学 A kind of control method for the distributed photovoltaic power generation system changing electrical engineering towards coal

Non-Patent Citations (16)

* Cited by examiner, † Cited by third party
Title
李伟;张帆;张磊;袁泽;周长城;王越;杨建华;: "计及电采暖类型差异的"煤改电"工程谐波分析与评估", 电网与清洁能源, no. 10 *
李伟;张帆;张磊;袁泽;周长城;王越;杨建华;: "计及电采暖类型差异的"煤改电"工程谐波分析与评估", 电网与清洁能源, no. 10, 25 October 2016 (2016-10-25) *
玄京岩;张艳;金成日;刘德鑫;崔洪菠;延东洙;: "分布式光伏电源接入容量对主动配电网的影响研究", 电器与能效管理技术, no. 01 *
玄京岩;张艳;金成日;刘德鑫;崔洪菠;延东洙;: "分布式光伏电源接入容量对主动配电网的影响研究", 电器与能效管理技术, no. 01, 15 January 2018 (2018-01-15) *
王宁;付蓉;黄校娟;应益强;: "有源配电网下多能互补协调优化策略研究", 电气应用, no. 06 *
王宁;付蓉;黄校娟;应益强;: "有源配电网下多能互补协调优化策略研究", 电气应用, no. 06, 20 March 2018 (2018-03-20) *
白牧可;唐巍;张璐;所丽;: "基于机会约束规划的DG与配电网架多目标协调规划", 电工技术学报, no. 10 *
白牧可;唐巍;张璐;所丽;: "基于机会约束规划的DG与配电网架多目标协调规划", 电工技术学报, no. 10, 26 October 2013 (2013-10-26) *
耿晓娜;刘俊德;范振亚;刘伟东;: "含充电站及光伏电源的配电网协调规划", 华北电力技术, no. 05, 25 May 2017 (2017-05-25), pages 1 - 8 *
耿晓娜;刘俊德;范振亚;刘伟东;: "含充电站及光伏电源的配电网协调规划", 华北电力技术, no. 05, pages 1 - 8 *
辛欣;张新慧;王龙;咸日常;孙桂花;: "含光伏电源的配电网规划研究", 山东电力技术, no. 02, 25 February 2016 (2016-02-25), pages 1 - 10 *
辛欣;张新慧;王龙;咸日常;孙桂花;: "含光伏电源的配电网规划研究", 山东电力技术, no. 02, pages 1 - 10 *
雷霞;唐文左;李逐云;何锦宇;刘群英;: "考虑区域综合能源系统优化运行的配电网扩展规划", 电网技术, no. 11 *
雷霞;唐文左;李逐云;何锦宇;刘群英;: "考虑区域综合能源系统优化运行的配电网扩展规划", 电网技术, no. 11, 5 November 2018 (2018-11-05) *
高泽;陈登明;杨建华;纪斌;冯小明;张靓;: "计及"煤改电"的农村低压配电网规划研究", 电工电气, no. 05 *
高泽;陈登明;杨建华;纪斌;冯小明;张靓;: "计及"煤改电"的农村低压配电网规划研究", 电工电气, no. 05, 15 May 2015 (2015-05-15) *

Similar Documents

Publication Publication Date Title
Ghadimi et al. PSO based fuzzy stochastic long-term model for deployment of distributed energy resources in distribution systems with several objectives
CN106532778B (en) Method for calculating maximum access capacity of distributed photovoltaic grid connection
CN106487005A (en) A kind of Electric power network planning method considering T-D tariff
CN108304972B (en) Active power distribution network frame planning method based on supply and demand interaction and DG (distributed generation) operation characteristics
Arasteh et al. Optimal allocation of photovoltaic/wind energy system in distribution network using meta-heuristic algorithm
Feng et al. Scheduling of short-term hydrothermal energy system by parallel multi-objective differential evolution
CN106803130B (en) Planning method for distributed power supply to be connected into power distribution network
CN109598377B (en) AC/DC hybrid power distribution network robust planning method based on fault constraint
Saraswat et al. A novel multi-zone reactive power market settlement model: A pareto-optimization approach
CN112784484A (en) Multi-objective optimization method and optimization system for regional comprehensive energy system
CN110783950B (en) Power distribution network node photovoltaic optimal configuration capacity determination method
CN111626594A (en) Power distribution network expansion planning method with multiple demand side resource collaboration
Zhang et al. Stochastic dynamic economic emission dispatch with unit commitment problem considering wind power integration
CN114580725A (en) Distributed photovoltaic wiring multi-objective optimization method and device based on genetic algorithm
CN110854891A (en) Power distribution network pre-disaster resource allocation method and system
CN113723807A (en) Energy storage and information system double-layer collaborative planning method, device and medium
Liu et al. Multi-objective mayfly optimization-based frequency regulation for power grid with wind energy penetration
CN109888817B (en) Method for carrying out position deployment and capacity planning on photovoltaic power station and data center
CN111881626B (en) Distribution network planning method for promoting DG (distributed generation) digestion
CN110048407B (en) Distributed energy power generation plan feasible region optimization analysis method
CN113095611A (en) Low-voltage distribution network coordinated planning method containing electric heating equipment and photovoltaic power supply
CN115994612A (en) Power distribution network operation optimization method and device based on business expansion planning and storage medium
CN109449951A (en) The method and relevant apparatus of reactive power optimization of power system under a kind of electricity market background
CN115411719A (en) Distributed power supply planning method based on source load uncertainty and voltage stability
CN114629125A (en) Flexible soft switch optimal configuration method, system, electronic 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