CN117410991B - Power balancing method, system, equipment and storage medium for distributed resources - Google Patents

Power balancing method, system, equipment and storage medium for distributed resources Download PDF

Info

Publication number
CN117410991B
CN117410991B CN202311725940.2A CN202311725940A CN117410991B CN 117410991 B CN117410991 B CN 117410991B CN 202311725940 A CN202311725940 A CN 202311725940A CN 117410991 B CN117410991 B CN 117410991B
Authority
CN
China
Prior art keywords
power
capacity
load
coefficient
balance
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202311725940.2A
Other languages
Chinese (zh)
Other versions
CN117410991A (en
Inventor
杨洁
舒东胜
李亚馨
徐敬友
姜世公
赵红生
夏方舟
邵非凡
叶高翔
杨子立
郑子健
彭文彦
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.)
Economic and Technological Research Institute of State Grid Hubei Electric Power Co Ltd
Original Assignee
Economic and Technological Research Institute of State Grid Hubei Electric Power Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Economic and Technological Research Institute of State Grid Hubei Electric Power Co Ltd filed Critical Economic and Technological Research Institute of State Grid Hubei Electric Power Co Ltd
Priority to CN202311725940.2A priority Critical patent/CN117410991B/en
Publication of CN117410991A publication Critical patent/CN117410991A/en
Application granted granted Critical
Publication of CN117410991B publication Critical patent/CN117410991B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • 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
    • H02J3/007—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources
    • H02J3/0075—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources for providing alternative feeding paths between load and source according to economic or energy efficiency considerations, e.g. economic dispatch
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L55/00—Arrangements for supplying energy stored within a vehicle to a power network, i.e. vehicle-to-grid [V2G] arrangements
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06315—Needs-based resource requirements planning or analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • 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
    • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
    • H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
    • H02J3/144—Demand-response operation of the power transmission or distribution network
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • H02J3/322—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means the battery being on-board an electric or hybrid vehicle, e.g. vehicle to grid arrangements [V2G], power aggregation, use of the battery for network load balancing, coordinated or cooperative battery charging
    • 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]

Landscapes

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

Abstract

A power balance method, a system, equipment and a storage medium for distributed resources are provided, the credible coefficients of various resource participation items in the stage are determined based on the stage where the distributed resources are located, a power balance model is established based on the credible coefficients, and the power balance operation is realized by calculating the newly increased power transformation capacity requirement of a power distribution network based on the power balance model; in the application of the design, different stages in the distributed resource are fully considered, the credible coefficients of different emphasis are obtained according to the operation requirements of the different stages, the newly increased requirements are calculated to realize power balance, the problem of active regulation and control influence of the distributed resource is fully considered, and the design can be simply and intuitively applied to actual planning work and has better accuracy.

Description

Power balancing method, system, equipment and storage medium for distributed resources
Technical Field
The invention relates to a power balancing means, belongs to the technical field of power grids, and particularly relates to a power balancing method, a power balancing system, power balancing equipment and a power balancing storage medium for distributed resources.
Background
The traditional power distribution network power balance mainly considers the condition of the maximum load scene, which results in high redundancy of the capacity of the planned power distribution network equipment and poor economical efficiency, in addition, the traditional power balance does not consider the influence of distributed resource operation regulation and control on load peaks and the characteristics of distributed resources, the participation degree of the power distribution network power balance is estimated only by simple experience values, and the method is too extensive.
At present, active regulation measures of distributed resources are hardly considered in electric power and electric quantity balance research of an active power distribution network, and a concept of flexible balance is partially researched, namely, a traditional unidirectional electric power balance mode of power supply tracking load is expanded into a balance mode of load storage interaction of a source network, the load is matched with the power supply through response of a demand side in a specific period, but related market mechanisms and supporting platforms of China are not perfect, the mode is still in a prospective and conception stage, and research on electric power and electric quantity balance does not form a unified standard, and is difficult to directly apply to actual planning work, and a simple and visual practical electric power and electric quantity balance method is lacked.
The disclosure of this background section is only intended to increase the understanding of the general background of the application and should not be taken as an admission or any form of suggestion that this information forms the prior art already known to a person of ordinary skill in the art.
Disclosure of Invention
The invention aims to overcome the defects and problems in the prior art and provide a simple, visual and accurate power balancing method, system, equipment and storage medium for distributed resources.
In order to achieve the above object, the technical solution of the present invention is: a method of power balancing for distributed resources, comprising:
based on the stage of the distributed resource, determining the credibility coefficient of each resource participation item in the stage; and establishing a power balance model based on the trusted coefficient, and calculating the newly increased power transformation capacity requirement of the power distribution network through the power balance model so as to realize power balance operation.
The expression of the power balance model is as follows:
；
wherein:for load power, +.>For power supply power +.>For the whole social load>For clean energy installation capacity,/->For a large user load of 110kV and above, < >>Direct supply load of 35kV and 20kV for 220kV transformer substation, < >>Is a conventional power supply of 35kV and below, < + >>For the energy storage capacity, < >>Charging facility capacity for electric vehicle V2G, < >>For demand side response load, +.>To send out the extra-zone power, < > and->To deliver in-zone power; load power balance and power supply power balance are different running modes; />For clean energy installation capacity confidence coefficient, +.>For the energy storage installation capacity trusted coefficient, +.>The reliable coefficient of the capacity of the electric vehicle V2G charging facility is +.>The load reliability coefficient is responded to for the demand side.
The expression of the new power transformation capacity increasing requirement of the power distribution network is as follows:
；
wherein:new requirement for power transformation capacity under load power balance, < > for>New requirement for power transformation capacity under power balance of power supply, < > for>For the volume ratio, +.>The total capacity of the public transformer is the current situation.
The stage where the distributed resource is located comprises at least one of a stage of lack of the distributed resource, a stage of uncertain peak clipping effect of the distributed resource and a stage of large-scale application of energy storage.
The credibility coefficient of each resource participation item comprises a clean energy installation capacity credibility coefficientTrusted coefficient of energy storage capacity>Electric automobile V2G charging facility capacity trusted coefficient +.>Demand side response load reliability factor +.>；
For the distributed starvation phase:
if the operation mode is load power balance, then、/>、/>The value is 0, & lt + & gt>An adjustable margin for the rated power of the stored energy;
if the operation mode is that the power supply power is balanced, then、/>、/>The value is 1, & lt + & gt>An adjustable margin for the rated power of the stored energy;
for the distributed resource peak clipping effect uncertainty stage, determining a trusted coefficient by at least one of the following ways;
if the operation mode is load power balance, thenThe value is 20 percent, and the patients are in the condition of being assy>An adjustable margin for the rated power of the capacity of the energy storage installation, < >>For 50% of the rated capacity of the electric vehicle V2G charging installation, the charge is added up>Accounting for 3% of the rated power of the response load on the demand side and not accounting for the saturated load;
if the operation mode is that the power supply power is balanced, thenThe value is 80 percent, and the patients are in the condition of being assy>An adjustable margin for the rated power of the capacity of the energy storage installation, < >>5 for rated power of capacity of electric automobile V2G charging facility0% count in->Accounting for 3% of the rated power of the response load on the demand side and not accounting for the saturated load;
the method comprises the steps of obtaining the number of substations and the capacity thereof under the influence of distributed resources by constructing a probability planning model taking the annual cost of a target substation as a target and the number of substations and the number of feeder lines as variables, taking the difference between the number of substations and the capacity thereof under the influence of the distributed resources and the number of substations and the capacity thereof under the influence of non-distributed resources as a new power transformation capacity increasing requirement, and then performing back-pushing according to the definition of a power balance model and a trusted coefficient to obtain the trusted coefficient;
the probability planning model has the following expression:
；
wherein:annual cost of transformer substation>Annual investment cost for transformer station construction>Annual investment cost for feeder construction,/-for>Annual cost for feeder operation loss->Annual cost for feeder reliability loss +.>The total number of outgoing lines of the transformer substation is calculated,load capacity for feeder, < >>For the load power factor, < >>For the load rate of the line, +.>For maximum load rate under line safe operating conditions, < >>For the range of the radius of the power supply of each substation, < >>The maximum allowable value of the power supply radius range of each transformer substation is set;
for the energy storage large-scale application stage:
if the operation mode is load power balance, then、/>The value is 0, & lt + & gt>For an adjustable margin of the rated power of the stored energy, +.>The value is 100%;
if the operation mode is that the power supply power is balanced, thenThe value is 1, & lt + & gt>The value is 0, & lt + & gt>For an adjustable margin of the rated power of the stored energy, +.>The value is 100%.
A power balancing system for distributed resources, comprising:
the trusted coefficient acquisition module is used for determining the trusted coefficients of various resource participation items in the stage based on the stage in which the distributed resource is located;
the power balance model construction module is used for building a power balance model based on the trusted coefficient;
and the transformation capacity newly-increased demand confirmation module is used for calculating the transformation capacity newly-increased demand of the power distribution network based on the power balance model so as to realize power balance operation.
The power balance model construction module is used for constructing a power balance model as follows:
；
wherein:for load power, +.>For power supply power +.>For the whole social load>For clean energy installation capacity,/->For a large user load of 110kV and above, < >>Direct supply load of 35kV and 20kV for 220kV transformer substation, < >>Is a conventional power supply of 35kV and below, < + >>For the energy storage capacity, < >>Charging facility capacity for electric vehicle V2G, < >>For demand side response load, +.>To send out the extra-zone power, < > and->To deliver in-zone power; load power balance and power supply power balance are different running modes; />For clean energy installation capacity confidence coefficient, +.>For the energy storage installation capacity trusted coefficient, +.>The reliable coefficient of the capacity of the electric vehicle V2G charging facility is +.>Responding to the load credibility coefficient for the demand side;
the power transformation capacity newly-increased demand confirmation module calculates the power transformation capacity newly-increased demand through the following calculation steps:
；
wherein:new requirement for power transformation capacity under load power balance, < > for>Is a power source power levelNew power transformation capacity increasing requirement under balance +.>For the volume ratio, +.>The total capacity of the public transformer is the current situation.
The trusted coefficient acquisition module is used for acquiring the trusted coefficient in the following way;
the distributed resource stage comprises a distributed resource deficiency stage, a distributed resource peak clipping effect uncertainty stage and an energy storage large-scale application stage; the credibility coefficient of each resource participation item comprises a clean energy installation capacity credibility coefficientTrusted coefficient of energy storage capacity>Electric automobile V2G charging facility capacity trusted coefficient +.>Demand side response load reliability factor +.>；
For the distributed starvation phase:
if the operation mode is load power balance, then、/>、/>The value is 0, & lt + & gt>An adjustable margin for the rated power of the stored energy;
if the operation mode is power supply powerBalance then、/>、/>The value is 1, & lt + & gt>An adjustable margin for the rated power of the stored energy;
for the distributed resource peak clipping effect uncertainty stage, determining a trusted coefficient by at least one of the following ways;
if the operation mode is load power balance, thenThe value is 20 percent, and the patients are in the condition of being assy>An adjustable margin for the rated power of the capacity of the energy storage installation, < >>For 50% of the rated capacity of the electric vehicle V2G charging installation, the charge is added up>Accounting for 3% of the rated power of the response load on the demand side and not accounting for the saturated load;
if the operation mode is that the power supply power is balanced, thenThe value is 80 percent, and the patients are in the condition of being assy>An adjustable margin for the rated power of the capacity of the energy storage installation, < >>For 50% of the rated capacity of the electric vehicle V2G charging installation, the charge is added up>Accounting for 3% of the rated power of the response load on the demand side and not accounting for the saturated load;
the method comprises the steps of obtaining the number of substations and the capacity thereof under the influence of distributed resources by constructing a probability planning model taking the annual cost of a target substation as a target and the number of substations and the number of feeder lines as variables, taking the difference between the number of substations and the capacity thereof under the influence of the distributed resources and the number of substations and the capacity thereof under the influence of non-distributed resources as a new power transformation capacity increasing requirement, and performing back-pushing according to the definition of a power balance model and a trusted coefficient to obtain the trusted coefficient;
the probability planning model has the following expression:
；
wherein:annual cost of transformer substation>Annual investment cost for transformer station construction>Annual investment cost for feeder construction,/-for>Annual cost for feeder operation loss->Annual cost for feeder reliability loss +.>The total number of outgoing lines of the transformer substation is calculated,load capacity for feeder, < >>For the load power factor, < >>For the load rate of the line, +.>For maximum load rate under line safe operating conditions, < >>For the range of the radius of the power supply of each substation, < >>The maximum allowable value of the power supply radius range of each transformer substation is set;
for the energy storage large-scale application stage:
if the operation mode is load power balance, then、/>The value is 0, & lt + & gt>For an adjustable margin of the rated power of the stored energy, +.>The value is 100%;
if the operation mode is that the power supply power is balanced, thenThe value is 1, & lt + & gt>The value is 0, & lt + & gt>For an adjustable margin of the rated power of the stored energy, +.>The value is 100%.
A power balancing apparatus for distributed resources, the apparatus comprising a processor and a memory;
the memory is used for storing computer program codes and transmitting the computer program codes to the processor;
the processor is configured to perform the power balancing method for distributed resources according to instructions in the computer program code.
A power balancing storage medium for distributed resources, having stored thereon a computer program which, when executed by a processor, implements the steps of the power balancing method for distributed resources.
Compared with the prior art, the invention has the beneficial effects that:
in the power balance method, the system, the equipment and the storage medium aiming at the distributed resources, the credible coefficients of various resource participation items in the stage are determined based on the stage where the distributed resources are positioned, a power balance model is established based on the credible coefficients, and the newly increased power transformation capacity requirement of the power distribution network is calculated through the power balance model so as to realize power balance operation; in the application of the design, different stages in the distributed resource are fully considered, the credible coefficients of different emphasis are obtained according to the operation requirements of the different stages, the newly increased requirements are calculated to realize power balance, the problem of active regulation and control influence of the distributed resource is fully considered, and the design can be simply and intuitively applied to actual planning work and has better accuracy.
Drawings
FIG. 1 is a schematic diagram of the method steps of the present invention.
Fig. 2 is a table of power balance calculation for a 110KV distribution network according to example 2 of the present invention.
Fig. 3 is a schematic diagram of the system structure of embodiment 3 in the present invention.
Fig. 4 is a schematic view of the apparatus structure of embodiment 4 in the present invention.
In the figure: the system comprises a trusted coefficient acquisition module 1, a power balance model construction module 2, a transformation capacity new demand confirmation module 3, a processor 4, a memory 5 and computer program codes 51.
Detailed Description
The invention is described in further detail below with reference to the accompanying drawings and detailed description.
Example 1:
referring to fig. 1, a power balancing method for a distributed resource, the distributed resource comprising: a distributed resource deficiency stage, a distributed resource peak clipping effect uncertainty stage and an energy storage large-scale application stage;
the distributed resource deficiency phase: defined as the development stage of distributed resource output and adjustable controllable capacity less than 10% of maximum load;
the distributed resource peak clipping effect uncertainty stage: the method is characterized by comprising the steps of defining main peak clipping measures that energy storage of the distributed resource cannot become a load power curve or a limited peak value of a power output curve of the distributed resource, and the distributed resource accounts for over 10 percent;
the energy storage large-scale application stage comprises the following steps: the method is defined as a main peak clipping measure that the distributed resource stores energy in a large scale into a load power curve or a limited peak value of a power output curve of the distributed resource, or the energy storage cost of the distributed resource is reduced to be lower than the cost of corresponding load clipping or power discarding risks, and other peak clipping and valley filling measures are used in a development stage replaced by the large-scale energy storage.
For the above stage, first, basic data of various resource participation items in the distributed resource, such as the installed capacity of the power supply, the saturated load size and the like, can be determined by the existing load resource prediction method, and then, the power balance is realized by adjusting the balance between two different operation modes.
The two operation modes are respectively as follows: the running modes of the power grid equipment under the condition of maximum forward and reverse load rates respectively correspond to the running mode or the scene of maximum numerical value of the power grid equipment after the output of the distributed power supply is subtracted and the running mode or the scene of maximum numerical value of the power grid equipment after the output of the distributed power supply is subtracted, such as the running mode of heavy load in the dead water period and light load in the rich water period, and the corresponding power balance is respectively defined as load power balance and power balance, for example: if the power balance value of the heavy-load power supply in the dead water period is 0, the power balance value of the load power supply is the maximum; the small load in the high water period has the power balance value of 1 and the load power balance value of minimum, and the small load belongs to one of typical operation modes.
The power balance of the distribution network is mainly to determine power requirements, namely newly-increased equipment capacity, and mainly comprises 110kV power transformation capacity requirements and 10kV medium-voltage feeder line scale requirements, so that a power balance model considering various resources is constructed for two types of power requirements, and corresponding power balance methods, namely a traditional method, a risk method and an initiative method are respectively provided for a distributed resource development stage to evaluate the degree of the various resources participating in power balance, and the specific steps comprise the following steps:
based on the stage of the distributed resource, determining the credibility coefficient of each resource participation item in the stage;
the trusted coefficient refers to: the ratio of the capacity or load size of the distributed resources participating in power balance to the installed capacity or total load; the credibility coefficient of each resource participation item comprises a clean energy installation capacity credibility coefficientTrusted coefficient of energy storage capacity>Electric automobile V2G charging facility capacity trusted coefficient +.>Demand side response load reliability factor +.>；
Further, the method specifically comprises the following steps:
aiming at a distributed resource deficiency stage;
the method is mainly aimed at a distributed resource deficiency stage, is simple, practical and relatively determined, but the obtained planning scheme tends to be conservative, and the equipment utilization rate is low, but in the distributed resource deficiency stage, the uncertainty is low, and the balance result is small in difference from the actual situation, so that the method is more applicable;
the credible coefficient is determined according to a traditional power balance method, and the credible coefficient is calculated according to the situation that the distributed power supply has high power when the load is low or the power supply has low power when the load is high exceeds the constraint condition of a power grid, so that the power balance under various typical operation modes is calculated;
wherein: clean energy installation capacity credible coefficientElectric automobile V2G charging facility capacity trusted coefficient +.>Demand side response load reliability factor +.>All the distributed resources are considered to be fully counted or not counted, namely the value in the load power balance is 0, the value in the power balance is 1, and the credibility coefficient of the energy storage installed capacity is>The value is counted according to the adjustable margin of the rated power, for example: 70-100%;
a stage of uncertainty of peak clipping effect of the distributed resource;
the method considers the influence of flexible resource regulation on electric power and electric quantity balance or the influence of operation on planning, can reduce the investment cost of a power grid under the condition of moderately increasing power failure and power abandoning risks, is suitable for the condition that the power output uncertainty of part of distributed resources has larger influence on load peaks before large-scale application of energy storage, and specifically comprises at least one of the following modes, wherein the first mode is to be determined by combining with the actual condition, and is suitable for engineering practice; the second model is needed to be established, is suitable for scientific research, is not friendly to engineering personnel, and can be selected according to the requirement in actual operation.
Calculating in detail based on the credible output or credible load under a large number of different typical operation modes, and obtaining the value range by statistics;
further, the value range includes the following:
1. the trusted force refers to: a distributed power supply has a minimum power balance for a load or a maximum power balance for a power supply at a power output level within a certain probability;
2. the trusted load refers to: the new load has the load level that can be reached with the highest load power balance or the lowest power balance for the power supply within a certain probability.
For load power balance, the higher the user reliability requirement is, the smaller the trusted output force is and the larger the trusted load is;
for power supply power balance, the higher the reliability requirement of power supply output, the larger the reliability and the smaller the reliability load.
Wherein: clean energy installation capacity credible coefficientAiming at two operation modes of load power balance and power balance, respectively counting according to 20% and 80%; energy storage installation capacity trusted coefficient->The value is calculated according to the adjustable margin of rated power, for example, 70-100%, the credible coefficient of the capacity of the V2G charging facility of the electric automobile>The demand-side response load reliability factor is calculated according to the reliability factor of the rated power, for example, 50 +.>Taking into account the trusted load factor according to its rated power and not taking into account the saturated load, for example 3%;
the method comprises the steps of considering reliability risks caused by uncertainty of a distributed power supply, converting the reliability risks into reliability loss cost, combining power grid construction investment operation cost, obtaining the number of substations and capacity of the substations under the influence of distributed resources by constructing a probability planning model with the minimum annual cost of a target substation and the number of feeder lines as variables, taking the difference between the number of the substations and the capacity of the substations under the influence of the distributed resources and the number of the substations and the capacity of the substations without the influence of the distributed resources as a new power transformation capacity increasing requirement, and then reversely pushing according to the definition of an electric power balance model and the reliability coefficient to obtain the reliability coefficient;
the probability planning model has the following expression:
；
wherein:annual cost of transformer substation>Annual investment cost for transformer station construction>Annual investment cost for feeder construction,/-for>Annual cost for feeder operation loss->Annual cost for feeder reliability loss +.>The total number of outgoing lines of the transformer substation is calculated,load capacity for feeder, < >>For the load power factor, < >>For the load rate of the line, +.>For maximum load rate under line safe operating conditions, < >>For the range of the radius of the power supply of each substation, < >>And the maximum allowable value of the range of the available power radius of each transformer substation is obtained.
Aiming at the energy storage large-scale application stage;
the method is based on the judgment that the main peak clipping measure or the energy storage cost of large-scale energy storage to be a limited peak value of a load power curve or a distributed power supply output curve is reduced to be lower than the corresponding cut load or power discarding risk cost, and the method does not relate to reliable output and reliable load which are difficult to determine in consideration of the certainty of the energy storage peak clipping effect of the indispensable elements of the novel power distribution system, so that the method is suitable for the energy storage large-scale application stage.
It does not consider the credible coefficient of the installed capacity of clean energyReliability coefficient of capacity of electric vehicle V2G charging facility>The value of the energy storage device is 0 in load power balance, and the value of the energy storage device is 1 in power supply power balance, and the energy storage device capacity credibility coefficient is +.>The value is calculated according to the adjustable margin of rated power, such as 70-100%, the demand side response load credibility coefficient +.>The value is 100%.
The method emphasizes the fusion of planning and operation, so that the randomness risk of power balance is greatly reduced due to the certainty of the energy storage peak clipping effect, and the equipment utilization rate is effectively improved.
Based on the credible coefficient and different running modes of the power distribution network, establishing a power balance model;
further, the expression of the power balance model is as follows:
；
wherein:for load power, +.>For power supply power +.>For the whole social load>For clean energy installation capacity,/->For a large user load of 110kV and above, < >>Direct supply load of 35kV and 20kV for 220kV transformer substation, < >>Is a conventional power supply of 35kV and below, < + >>For the energy storage capacity, < >>Charging facility capacity for electric vehicle V2G, < >>For demand side response load, +.>To send out the extra-zone power, < > and->To deliver in-zone power; load power balance and power supply power balance are different running modes;
wherein:for clean energy installation capacity confidence coefficient, +.>For the energy storage installation capacity trusted coefficient, +.>The reliable coefficient of the capacity of the electric vehicle V2G charging facility is +.>The load reliability coefficient is responded to for the demand side.
And calculating the newly increased demand of the transformation capacity of the power distribution network through a power balance model so as to realize power balance operation.
Further, the expression of the new power transformation capacity requirement of the power distribution network is as follows:
；
wherein:new requirement for power transformation capacity under load power balance, < > for>New requirement for power transformation capacity under power balance of power supply, < > for>For the volume ratio, +.>The total capacity of the public transformer is the current situation.
Example 2:
in this embodiment, an example calculation is performed using 110KV distribution network power balance as an example.
Referring to fig. 2, for facilitating calculation, first, basic data of various resource participation items in a distributed resource is obtained through an existing load prediction method to form a power balance table, and then the basic data is processed according to different methods to determine the credibility coefficient of each resource;
for the traditional method of the distributed resource deficiency stage, the items of serial numbers 2, 7 and 8 in the load power balance are set to 0 or the maximum and other operation modes, and the items of serial number 2 and serial numbers 7 and 8 in the power balance are set to the maximum.
The confidence coefficients of the practical method for the distributed resource peak clipping effect uncertainty stage are seen in table 1.
An active method aiming at the energy storage large-scale application stage comprises the following steps: in the load power balance, the items of serial numbers 2 and 7 are set to 0, the items of serial numbers 6 and 8 are counted in 70-100% and 100% respectively, in the power balance of the power supply, the serial number 2 is the largest, the serial number 7 is 0, and the items of serial numbers 6 and 8 are counted in 70-100% and 100% respectively.
The loads are then added according to the "+", "-" symbols in the table, the load power balance calculation formula being as follows:
；
；
；
wherein:the new added variable capacity under load power balance; />Maximum power transformation capacity requirement under load power balance; />The total capacity of the transformer is balanced for the current load power; />Maximum load under load power balance; />Is a full social load under load power balance; />The sum of items numbered 2-10;
the power balance calculation formula of the power supply is as follows:
；
；
；
wherein:the power supply is a new added variable capacity under the power balance of the power supply; />Maximum power transformation capacity requirement under power supply power balance; />The total capacity of the transformer under the current power balance of the power supply; />To increase the demand under the power balance of the power supplyIs a power supply capacity of (a); />Is a social load under the balance of power supply and electric power; />The sum of items numbered 2-10; the formula is as follows:
；
wherein:maximum load of 110kV set, +.>To require a new power supply capacity.
Finally according to a certain volume-to-load ratioAnd calculating the total capacity requirement of the 110kV public power transformation, wherein the power transformation capacity required to be increased is the difference between the power transformation requirement of the power balance result and the existing power transformation capacity, and the power transformation capacity required to be increased is 0 if the power balance result is smaller than the existing power transformation capacity.
Example 3:
referring to fig. 3, a power balancing system for distributed resources, comprising:
the trusted coefficient acquisition module 1 is used for determining the trusted coefficients of various resource participation items in the stage based on the stage in which the distributed resource is located;
further, the trusted coefficient obtaining module 1 is configured to obtain a trusted coefficient in the following manner;
the distributed resource stage comprises a distributed resource deficiency stage, a distributed resource peak clipping effect uncertainty stage and an energy storage large-scale application stage; the credibility coefficient of each resource participation item comprises a clean energy installation capacity credibility coefficientTrusted coefficient of energy storage capacity>Electric automobile V2G charging facility capacity trusted coefficient +.>Demand side response load reliability factor +.>；
For the distributed starvation phase:
if the operation mode is load power balance, then、/>、/>The value is 0, & lt + & gt>An adjustable margin for the rated power of the stored energy;
if the operation mode is that the power supply power is balanced, then、/>、/>The value is 1, & lt + & gt>An adjustable margin for the rated power of the stored energy;
for the distributed resource peak clipping effect uncertainty stage, determining a trusted coefficient by at least one of the following ways;
if the operation mode isLoad power balanceThe value is 20 percent, and the patients are in the condition of being assy>An adjustable margin for the rated power of the capacity of the energy storage installation, < >>For 50% of the rated capacity of the electric vehicle V2G charging installation, the charge is added up>Accounting for 3% of the rated power of the response load on the demand side and not accounting for the saturated load;
if the operation mode is that the power supply power is balanced, thenThe value is 80 percent, and the patients are in the condition of being assy>An adjustable margin for the rated power of the capacity of the energy storage installation, < >>For 50% of the rated capacity of the electric vehicle V2G charging installation, the charge is added up>Accounting for 3% of the rated power of the response load on the demand side and not accounting for the saturated load;
the method comprises the steps of obtaining the number of substations and the capacity thereof under the influence of distributed resources by constructing a probability planning model taking the annual cost of a target substation as a target and the number of substations and the number of feeder lines as variables, taking the difference between the number of substations and the capacity thereof under the influence of the distributed resources and the number of substations and the capacity thereof under the influence of non-distributed resources as a new power transformation capacity increasing requirement, and performing back-pushing according to the definition of a power balance model and a trusted coefficient to obtain the trusted coefficient;
the probability planning model has the following expression:
；
wherein:annual cost of transformer substation>Annual investment cost for transformer station construction>Annual investment cost for feeder construction,/-for>Annual cost for feeder operation loss->Annual cost for feeder reliability loss +.>The total number of outgoing lines of the transformer substation is calculated,load capacity for feeder, < >>For the load power factor, < >>For the load rate of the line, +.>For maximum load rate under line safe operating conditions, < >>For the range of the radius of the power supply of each substation, < >>The most power supply radius range of each transformer substationA large allowable value;
for the energy storage large-scale application stage:
if the operation mode is load power balance, then、/>The value is 0, & lt + & gt>For an adjustable margin of the rated power of the stored energy, +.>The value is 100%;
if the operation mode is that the power supply power is balanced, thenThe value is 1, & lt + & gt>The value is 0, & lt + & gt>For an adjustable margin of the rated power of the stored energy, +.>The value is 100%.
The power balance model construction module 2 is used for building a power balance model based on the trusted coefficient;
further, the power balance model construction module 2 constructs a power balance model as follows:
；
wherein:for load power, +.>For power supply power +.>For the whole social load>For clean energy installation capacity,/->For a large user load of 110kV and above, < >>Direct supply load of 35kV and 20kV for 220kV transformer substation, < >>Is a conventional power supply of 35kV and below, < + >>For the energy storage capacity, < >>Charging facility capacity for electric vehicle V2G, < >>For demand side response load, +.>To send out the extra-zone power, < > and->To deliver in-zone power; load power balance and power supply power balance are different running modes;
wherein:for clean energy installation capacity confidence coefficient, +.>For the energy storage installation capacity trusted coefficient, +.>The reliable coefficient of the capacity of the electric vehicle V2G charging facility is +.>Responding to the load credibility coefficient for the demand side;
and the transformation capacity newly-increased demand confirmation module 3 is used for calculating the transformation capacity newly-increased demand of the power distribution network based on the power balance model so as to realize power balance operation.
Further, the transformation capacity added demand confirmation module 3 calculates the transformation capacity added demand according to the following calculation formula:
；
wherein:new requirement for power transformation capacity under load power balance, < > for>New requirement for power transformation capacity under power balance of power supply, < > for>For the volume ratio, +.>The total capacity of the public transformer is the current situation.
Example 4:
referring to fig. 4, a power balancing device for distributed resources, the device comprising a processor 4 and a memory 5;
the memory 5 is used for storing computer program code 51 and for transmitting the computer program code 51 to the processor 4;
the processor 4 is configured to perform the power balancing method for distributed resources according to instructions in the computer program code 51.
Example 5:
a power balancing storage medium for distributed resources, having stored thereon a computer program which, when executed by a processor, implements the power balancing method for distributed resources.
In general, the computer instructions to implement the methods of the present invention may be carried in any combination of one or more computer-readable storage media. The non-transitory computer-readable storage medium may include any computer-readable medium, except the signal itself in temporary propagation.
The computer readable storage medium can be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAn), a read-only memory (ROn), an erasable programmable read-only memory (EKROn or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROn), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The computer program code for carrying out operations of the present invention may be written in one or more programming languages, or combinations thereof, including an object oriented programming language such as Java, snalltalk, C ++ and conventional procedural programming languages, such as the "C" language or similar programming languages, particularly Kython languages suitable for neural network computing and TensorFlow, kyTorch-based platform frameworks may be used. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any number of types of networks, including a Local Area Network (LAN) or a Wide Area Network (WAN), or be connected to an external computer (for example, through the Internet using an Internet service provider).
The foregoing apparatus and non-transitory computer readable storage medium may refer to a specific description of a power balancing method and beneficial effects for distributed resources, and will not be repeated herein.
The above description is merely of preferred embodiments of the present invention, and the scope of the present invention is not limited to the above embodiments, but all equivalent modifications or variations according to the present disclosure will be within the scope of the claims.

Claims (6)

1. A method of power balancing for distributed resources, comprising:
based on the stage of the distributed resource, determining the credibility coefficient of each resource participation item in the stage; establishing a power balance model based on the trusted coefficient, and calculating new power transformation capacity increasing requirements of the power distribution network through the power balance model to realize power balance operation;
the trusted coefficient refers to: the ratio of the capacity or load size of the distributed resources participating in power balance to the installed capacity or total load; the credibility coefficients of the various resource participation items comprise a clean energy installation capacity credibility coefficient a, an energy storage installation capacity credibility coefficient b, an electric automobile V2G charging facility capacity credibility coefficient c and a demand side response load credibility coefficient d;
the expression of the power balance model is as follows:
wherein: p (P) L For load power, P G For power supply power, P 1 For the whole social load, P 2 To clean the energy installation capacity, P 3 For a large user load of 110kV and above, P 4 Direct-supply load of 35kV and 20kV of 220kV transformer substation, P 5 Is a conventional power supply of 35kV and below, P 6 To store the capacity of the machine, P 7 Charging facility capacity, P, for electric vehicle V2G 8 To respond to load on demand side, P 9 To send out off-zone power, P 10 To deliver in-zone power; load power balance and power supply power balance are different running modes; a is a clean energy installation capacity credibility coefficient, b is an energy storage installation capacity credibility coefficient, c is an electric vehicle V2G charging facility capacity credibility coefficient, and d is a demand side response load credibility coefficient;
the stage where the distributed resource is located comprises at least one of a stage of lack of the distributed resource, a stage of uncertain peak clipping effect of the distributed resource and a stage of large-scale application of energy storage;
for the distributed starvation phase:
if the operation mode is load power balance, the values of a, c and d are 0, and b is the adjustable margin of the rated power of the stored energy;
if the operation mode is power balance of the power supply, the values of a, c and d are 1, and b is the adjustable margin of the rated power of the stored energy;
for the distributed resource peak clipping effect uncertainty stage, determining a trusted coefficient by at least one of the following ways;
if the operation mode is that the load power supply is balanced, the value of a is 20%, b is the adjustable margin of the rated power of the capacity of the energy storage installation, c is 50% of the rated power of the capacity of the electric vehicle V2G charging facility, d is 3% of the rated power of the response load of the demand side and does not account for the saturated load;
if the operation mode is that the power supply power is balanced, the value of a is 80%, b is the adjustable margin of the rated power of the capacity of the energy storage installation, c is 50% of the rated power of the capacity of the electric vehicle V2G charging facility, d is 3% of the rated power of the response load of the demand side and does not account for the saturated load;
the method comprises the steps of obtaining the number of substations and the capacity thereof under the influence of distributed resources by constructing a probability planning model taking the annual cost of a target substation as a target and the number of substations and the number of feeder lines as variables, taking the difference between the number of substations and the capacity thereof under the influence of the distributed resources and the number of substations and the capacity thereof under the influence of non-distributed resources as a new power transformation capacity increasing requirement, and performing back-pushing according to the definition of a power balance model and a trusted coefficient to obtain the trusted coefficient;
the probability planning model has the following expression:
wherein: c (C) Z For annual cost of transformer substation, C BT Annual investment cost for transformer station construction, C XT For the annual investment cost of feeder line construction, C XS For annual cost of feeder line operation loss C XK Annual cost for feeder reliability loss, N F The total number of outgoing lines of the transformer substation S L，max For the feeder line bearing capacity, cos theta is the load power factor, eta is the load rate of the line, eta max For maximum load rate under line safe operating conditions, R X For the power supply radius range of each transformer substation, R max The maximum allowable value of the power supply radius range of each transformer substation is set;
for the energy storage large-scale application stage:
if the operation mode is load power balance, the values of a and c are 0, b is the adjustable margin of the rated power of the stored energy, and d is 100%;
if the operation mode is that the power supply power is balanced, the value of a is 1, the value of c is 0, b is the adjustable margin of the rated power of the stored energy, and the value of d is 100%.
2. A method of power balancing for distributed resources as claimed in claim 1, wherein:
the expression of the new power transformation capacity increasing requirement of the power distribution network is as follows:
wherein: ΔS L To increase the demand for the transformation capacity under the load power balance, delta S G The new requirement for the variable capacity under the power balance of the power supply is that K is the capacity-to-load ratio, S 0 The total capacity of the public transformer is the current situation.
3. A power balancing system for distributed resources, comprising:
the trusted coefficient acquisition module (1) is used for determining the trusted coefficient of each type of resource participation item in the stage based on the stage where the distributed resource is located;
the power balance model construction module (2) is used for building a power balance model based on the trusted coefficient;
the power transformation capacity newly-increased demand confirming module (3) is used for calculating the power transformation capacity newly-increased demand of the power distribution network based on the power balance model so as to realize power balance operation;
the power balance model construction module (2) is used for constructing a power balance model as follows:
wherein: p (P) L For load power, P G For power supply power, P 1 For the whole social load, P 2 To clean the energy installation capacity, P 3 For a large user load of 110kV and above, P 4 Direct-supply load of 35kV and 20kV of 220kV transformer substation, P 5 Is a conventional power supply of 35kV and below, P 6 To store the capacity of the machine, P 7 Charging facility capacity, P, for electric vehicle V2G 8 To respond to load on demand side, P 9 To send out off-zone power, P 10 To deliver in-zone power; load power balance and power supply power balance are different running modes; a is a clean energy installation capacity credibility coefficient, b is an energy storage installation capacity credibility coefficient, c is an electric vehicle V2G charging facility capacity credibility coefficient, and d is a demand side response load credibility coefficient;
the trusted coefficient acquisition module (1) is used for acquiring the trusted coefficient in the following way;
the distributed resource stage comprises a distributed resource deficiency stage, a distributed resource peak clipping effect uncertainty stage and an energy storage large-scale application stage; the credibility coefficients of the various resource participation items comprise a clean energy installation capacity credibility coefficient a, an energy storage installation capacity credibility coefficient b, an electric automobile V2G charging facility capacity credibility coefficient c and a demand side response load credibility coefficient d;
for the distributed starvation phase:
if the operation mode is load power balance, the values of a, c and d are 0, and b is the adjustable margin of the rated power of the stored energy;
if the operation mode is power balance of the power supply, the values of a, c and d are 1, and b is the adjustable margin of the rated power of the stored energy;
for the distributed resource peak clipping effect uncertainty stage, determining a trusted coefficient by at least one of the following ways;
if the operation mode is that the load power supply is balanced, the value of a is 20%, b is the adjustable margin of the rated power of the capacity of the energy storage installation, c is 50% of the rated power of the capacity of the electric vehicle V2G charging facility, d is 3% of the rated power of the response load of the demand side and does not account for the saturated load;
if the operation mode is that the power supply power is balanced, the value of a is 80%, b is the adjustable margin of the rated power of the capacity of the energy storage installation, c is 50% of the rated power of the capacity of the electric vehicle V2G charging facility, d is 3% of the rated power of the response load of the demand side and does not account for the saturated load;
the method comprises the steps of obtaining the number of substations and the capacity thereof under the influence of distributed resources by constructing a probability planning model taking the annual cost of a target substation as a target and the number of substations and the number of feeder lines as variables, taking the difference between the number of substations and the capacity thereof under the influence of the distributed resources and the number of substations and the capacity thereof under the influence of non-distributed resources as a new power transformation capacity increasing requirement, and performing back-pushing according to the definition of a power balance model and a trusted coefficient to obtain the trusted coefficient;
the probability planning model has the following expression:
wherein: c (C) Z For annual cost of transformer substation, C BT Annual investment cost for transformer station construction, C XT For the annual investment cost of feeder line construction, C XS For annual cost of feeder line operation loss C XK Annual cost for feeder reliability loss, N F The total number of outgoing lines of the transformer substation S L，max For the feeder line bearing capacity, cos theta is the load power factor, eta is the load rate of the line, eta max For maximum load rate under line safe operating conditions, R X For the power supply radius range of each transformer substation, R max The maximum allowable value of the power supply radius range of each transformer substation is set;
for the energy storage large-scale application stage:
if the operation mode is load power balance, the values of a and c are 0, b is the adjustable margin of the rated power of the stored energy, and d is 100%;
if the operation mode is that the power supply power is balanced, the value of a is 1, the value of c is 0, b is the adjustable margin of the rated power of the stored energy, and the value of d is 100%.
4. A power balancing system for distributed resources as claimed in claim 3, wherein:
the transformation capacity newly-increased demand confirmation module (3) calculates the transformation capacity newly-increased demand through the following calculation steps:
wherein: ΔS L To increase the demand for the transformation capacity under the load power balance, delta S G The new requirement for the variable capacity under the power balance of the power supply is that K is the capacity-to-load ratio, S 0 The total capacity of the public transformer is the current situation.
5. A power balancing apparatus for distributed resources, comprising:
the device comprises a processor (4) and a memory (5);
-said memory (5) is adapted to store computer program code (51) and to transmit said computer program code (51) to said processor (4);
the processor (4) is configured to perform the power balancing method for distributed resources according to claim 1 or 2 according to instructions in the computer program code (51).
6. A power balancing storage medium for distributed resources, having stored thereon a computer program, characterized by: the computer program, when executed by a processor, implements the power balancing method for distributed resources of claim 1 or 2.
CN202311725940.2A 2023-12-15 2023-12-15 Power balancing method, system, equipment and storage medium for distributed resources Active CN117410991B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202311725940.2A CN117410991B (en) 2023-12-15 2023-12-15 Power balancing method, system, equipment and storage medium for distributed resources

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202311725940.2A CN117410991B (en) 2023-12-15 2023-12-15 Power balancing method, system, equipment and storage medium for distributed resources

Publications (2)

Publication Number Publication Date
CN117410991A CN117410991A (en) 2024-01-16
CN117410991B true CN117410991B (en) 2024-03-01

Family

ID=89500388

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202311725940.2A Active CN117410991B (en) 2023-12-15 2023-12-15 Power balancing method, system, equipment and storage medium for distributed resources

Country Status (1)

Country Link
CN (1) CN117410991B (en)

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2007110809A (en) * 2005-10-12 2007-04-26 Tokyo Electric Power Co Inc:The Support system and support method for determining condition when distributed power supply is linked to distribution network
CA2819169A1 (en) * 2012-06-18 2013-12-18 Hatch Ltd. Systems, methods and controllers for control of power distribution devices and systems
CN109658285A (en) * 2018-12-21 2019-04-19 深圳供电局有限公司 A kind of appraisal procedure and system of regional distribution network power transformation capacity demand
CN112241923A (en) * 2020-09-21 2021-01-19 国网冀北电力有限公司秦皇岛供电公司 Distribution network power balance method based on source-load equivalent external characteristics of comprehensive energy system
CN114142536A (en) * 2021-12-03 2022-03-04 国家电网有限公司西北分部 Multi-type unit coordination method considering capacity reserve
CN114386700A (en) * 2022-01-13 2022-04-22 广东电网有限责任公司 Power transmission system planning method and device considering wind-light correlation and storage medium

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2007110809A (en) * 2005-10-12 2007-04-26 Tokyo Electric Power Co Inc:The Support system and support method for determining condition when distributed power supply is linked to distribution network
CA2819169A1 (en) * 2012-06-18 2013-12-18 Hatch Ltd. Systems, methods and controllers for control of power distribution devices and systems
CN109658285A (en) * 2018-12-21 2019-04-19 深圳供电局有限公司 A kind of appraisal procedure and system of regional distribution network power transformation capacity demand
CN112241923A (en) * 2020-09-21 2021-01-19 国网冀北电力有限公司秦皇岛供电公司 Distribution network power balance method based on source-load equivalent external characteristics of comprehensive energy system
CN114142536A (en) * 2021-12-03 2022-03-04 国家电网有限公司西北分部 Multi-type unit coordination method considering capacity reserve
CN114386700A (en) * 2022-01-13 2022-04-22 广东电网有限责任公司 Power transmission system planning method and device considering wind-light correlation and storage medium

Also Published As

Publication number Publication date
CN117410991A (en) 2024-01-16

Similar Documents

Publication Publication Date Title
Abdelrazek et al. Integrated PV capacity firming and energy time shift battery energy storage management using energy-oriented optimization
CN104951899A (en) Multi-time-scale optimal scheduling method for power distribution company containing large-scale renewable energy sources
WO2013097489A1 (en) Real-time power control method and system for megawatt battery energy storage power station
WO2022242020A1 (en) Method and system for optimizing direct current transmission limit within planning period
CN109325621B (en) Park energy internet two-stage optimal scheduling control method
Hartmann et al. Methodologies for storage size determination for the integration of wind power
CN105356494B (en) The reliability calculation method of multiterminal VSC-HVDC grid-connected systems
Nguyen et al. Effective power dispatch capability decision method for a wind‐battery hybrid power system
CN102904248B (en) Electric power system dispatching method based on wind electricity output uncertainty aggregation
CN109873498A (en) A kind of low-voltage network comprehensive power quality monitoring method containing distributed photovoltaic
CN117410991B (en) Power balancing method, system, equipment and storage medium for distributed resources
Yao et al. Optimal day‐ahead coordination on wind‐pumped‐hydro system by using multiobjective multistage model
CN117239754A (en) Carbon emission considered wind-light-fire multi-power coordination operation method and system
CN111835003A (en) Method and system for calculating theoretical line loss of medium-voltage distribution network in real time under multi-power-supply power supply
CN203233179U (en) Wind power prediction system
CN201145946Y (en) Device for monitoring wind, light electrification parameter network
Kazemi et al. A novel method for estimating wind turbines power output based on least square approximation
Wang et al. Research on power-supply cost of regional power system under carbon-peak target
CN115622081A (en) New energy distribution and storage frequency modulation method and system
CN115411748A (en) Frequency adjusting method, device and system for photovoltaic power generation system
Song et al. Power grid planning based on differential abandoned wind rate
Kim Impact of electric vehicles on peak load reduction
CN109840614B (en) Transformer optimal equipment utilization rate control method based on life cycle cost
Zhan et al. Real-time active power control method of regional power grid considering wind power fluctuations under cps framework
Liang et al. Optimal control of battery for grid-connected wind-storage system

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