CN114421540A - Distributed pumped storage dispatching method based on virtual power plant - Google Patents

Distributed pumped storage dispatching method based on virtual power plant Download PDF

Info

Publication number
CN114421540A
CN114421540A CN202210197795.4A CN202210197795A CN114421540A CN 114421540 A CN114421540 A CN 114421540A CN 202210197795 A CN202210197795 A CN 202210197795A CN 114421540 A CN114421540 A CN 114421540A
Authority
CN
China
Prior art keywords
distributed
power
pumped storage
pumped
node
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
CN202210197795.4A
Other languages
Chinese (zh)
Inventor
刘军
吴赫君
徐晨博
陈鸿鑫
李凌阳
丁一
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 Zhejiang Electric Power Co Ltd
Original Assignee
Zhejiang University ZJU
Economic and Technological Research Institute of State Grid Zhejiang 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 Zhejiang University ZJU, Economic and Technological Research Institute of State Grid Zhejiang Electric Power Co Ltd filed Critical Zhejiang University ZJU
Priority to CN202210197795.4A priority Critical patent/CN114421540A/en
Publication of CN114421540A publication Critical patent/CN114421540A/en
Pending legal-status Critical Current

Links

Images

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
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03B—MACHINES OR ENGINES FOR LIQUIDS
    • F03B13/00—Adaptations of machines or engines for special use; Combinations of machines or engines with driving or driven apparatus; Power stations or aggregates
    • F03B13/06—Stations or aggregates of water-storage type, e.g. comprising a turbine and a pump
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/10—Geometric CAD
    • G06F30/18—Network design, e.g. design based on topological or interconnect aspects of utility systems, piping, heating ventilation air conditioning [HVAC] or cabling
    • 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
    • H02J3/28—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
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/40—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation wherein a plurality of decentralised, dispersed or local energy generation technologies are operated simultaneously
    • 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
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/20—Hydro 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
    • 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
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02E60/16—Mechanical energy storage, e.g. flywheels or pressurised fluids
    • 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)
  • Human Resources & Organizations (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Economics (AREA)
  • Theoretical Computer Science (AREA)
  • Strategic Management (AREA)
  • Power Engineering (AREA)
  • Health & Medical Sciences (AREA)
  • Marketing (AREA)
  • Geometry (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Tourism & Hospitality (AREA)
  • General Engineering & Computer Science (AREA)
  • General Business, Economics & Management (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Evolutionary Computation (AREA)
  • Operations Research (AREA)
  • Computational Mathematics (AREA)
  • Game Theory and Decision Science (AREA)
  • Mathematical Analysis (AREA)
  • Mathematical Optimization (AREA)
  • Pure & Applied Mathematics (AREA)
  • Computer Hardware Design (AREA)
  • Quality & Reliability (AREA)
  • Educational Administration (AREA)
  • Chemical & Material Sciences (AREA)
  • Combustion & Propulsion (AREA)
  • Mechanical Engineering (AREA)
  • Development Economics (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

The invention discloses a distributed pumped storage dispatching method based on a virtual power plant. The method comprises the steps that a plurality of distributed pumped storage units are aggregated into a virtual power plant to participate in power grid dispatching; establishing a distributed pumped storage upper-layer decision model and a power grid dispatching lower-layer optimization model based on a virtual power plant; and realizing model solution through the Countake condition, and obtaining the output of the thermal power generating unit of the power grid, the pumping state and the pumping power of the distributed pumped storage unit. The method provided by the invention considers the operation constraint of each pumped storage power station, the operation constraint of the thermal power generating unit and the power grid line capacity constraint, provides a method for the distributed pumped storage type small-capacity power supply to participate in power grid dispatching, and exploits the regulation potential of the distributed pumped storage type small-capacity power supply in the power grid operation.

Description

Distributed pumped storage dispatching method based on virtual power plant
Technical Field
The invention belongs to a power grid configuration control method in the field of power grid scheduling, and particularly relates to a distributed pumped storage scheduling method based on a virtual power plant.
Background
The pumped storage is used as a power supply which is flexible in adjustment and rapid in start and stop, auxiliary services such as peak shaving, frequency modulation and the like can be provided for a power grid, the power balance problem caused by uncertain output of power supplies such as wind power and photoelectricity is relieved, and the absorption and development of new energy are promoted. The pumped storage can be divided into large pumped storage and medium-small pumped storage (namely distributed pumped storage) according to installed capacity. The construction of large pumped storage is greatly influenced by factors such as geographical environment and the like, the site selection problem is increasingly difficult, and the construction period of the large pumped storage is long, so that the power grid is difficult to serve in a short term.
With the large-scale development and utilization of distributed energy resources and the rapid development of smart power grids in China, distributed pumped storage becomes a technology worth of wide popularization and application, has the advantages of flexible layout, short construction period and good adaptability, can be matched with other types of power supplies to play the characteristic of rapidly adjusting power, and achieves the purpose of guaranteeing the reliable power supply of cities and micro power grids. However, the capacity scale of the distributed pumped storage is small, so that the distributed pumped storage is difficult to participate in direct dispatching of a large-scale power grid, and the difficulty of a dispatcher in regulating and controlling the distributed pumped storage is large. Most of the research on the pumped storage energy at the present stage focuses on large pumped storage, and the small-capacity power supply of the distributed pumped storage is not analyzed and modeled, so that the distributed pumped storage is not favorable for exerting the adjusting capacity of the distributed pumped storage in the operation of a power grid.
Disclosure of Invention
In order to solve the problems in the background art, the invention provides a distributed pumped storage scheduling method based on a virtual power plant, which considers the operation constraints of a power grid, a thermal power unit and a distributed pumped storage power station, realizes the aggregation of distributed pumped storage by a virtual power plant mode, solves the problem that small-capacity power sources such as distributed pumped storage are difficult to participate in scheduling, respectively constructs a distributed pumped storage upper-layer decision model and a power grid scheduling lower-layer optimization model based on the virtual power plant, forms a double-layer optimization scheduling model, solves the models to obtain the output of the thermal power unit and the pumping power of the distributed pumped storage unit, and realizes the scheduling of the distributed pumped storage.
The technical scheme adopted by the invention is as follows:
the invention comprises the following steps:
step 1: acquiring load prediction data and power transmission line parameters of a power grid;
step 2: constructing a distributed pumped storage upper-layer decision model based on a virtual power plant;
and step 3: establishing a power grid dispatching lower-layer optimization model;
and 4, step 4: model conversion is carried out on a distributed pumped storage upper layer decision model and a power grid dispatching lower layer optimization model based on a virtual power plant by utilizing a Countque condition to obtain a distributed pumped storage participation power grid dispatching model, then the distributed pumped storage participation power grid dispatching model is solved according to load prediction data and grid frame parameters of a power grid, and the output of a thermal power unit of the power grid, the pumping state and the pumping power of the distributed pumped storage unit are obtained.
The step 2 specifically comprises the following steps:
aggregating a plurality of distributed pumped storage units in the power grid to obtain a virtual power plant, and determining the pumped water and the pumped water of each distributed pumped storage unit based on the virtual power plant and with the aim of maximizing all the distributed pumped storage benefits,Power generationState, thereby constructing a distributed pumped storage upper layer block based on a virtual power plantThe strategy model is characterized in that the objective function of the distributed pumped storage upper-layer strategy model is as follows:
Figure BDA0003527819370000021
wherein, R represents all the distributed pumping and storage benefits, and T is a scheduling time interval set in a scheduling period; NP is the number of the distributed pumped storage units; CIp,s()、CIg,s() Respectively a power generation benefit function and a pumping cost function of the distributed pumping energy storage unit s; ps,tGenerating power G of distributed pumped storage units s for a scheduling period ts,tFor a scheduling time period t, the pumping power of the distributed pumped storage unit s, p is the power generation state, g is the pumping state, s is the serial number of the distributed pumped storage unit, and t is the serial number of the scheduling time period;
the operation constraints of the distributed pumped storage upper decision model comprise:
1) force restraint:
usPs,min≤Ps,t≤usPs,max
Gs,t＝vsGs,e
us+vs＝1
wherein, Ps,minAnd Ps,maxRespectively the minimum output and the maximum output of the distributed pumped storage unit s; gs,eRated pumping power of the distributed pumped storage unit s; u. ofsAnd vsRespectively representing the power generation state and the pumping state u of the distributed pumped storage unit ssAnd vsAll are variables from 0 to 1;
2) and (4) library capacity constraint:
Figure BDA0003527819370000022
Figure BDA0003527819370000023
Figure BDA0003527819370000024
Figure BDA0003527819370000025
Figure BDA0003527819370000031
wherein the content of the first and second substances,
Figure BDA0003527819370000032
and
Figure BDA0003527819370000033
respectively representing the water storage capacities of an upper reservoir and a lower reservoir of a power station to which a distributed pumped storage unit s belongs in a scheduling time period t + 1;
Figure BDA0003527819370000034
and
Figure BDA0003527819370000035
respectively representing the water storage capacities of an upper reservoir and a lower reservoir of a power station to which a distributed pumped storage unit s belongs in a scheduling time period t; c. Cs,gAnd cs,pRespectively representing the pumping efficiency and the power generation efficiency of the distributed pumped storage unit s; Δ t represents the pumping time or the power generation time;
Figure BDA0003527819370000036
andV s URthe upper limit and the lower limit of the water storage capacity of the upper reservoir are respectively set;
Figure BDA0003527819370000037
V s LRthe upper limit and the lower limit of the water storage capacity of a lower reservoir of a power station to which the distributed pumped storage unit s belongs are respectively set;δ s URand
Figure BDA0003527819370000038
maximum storage capacity change values of the first time period and the last time period of each day of an upper reservoir of a power station to which the distributed pumped storage unit s belongs are respectively obtained;
Figure BDA0003527819370000039
representing the storage capacity of an upper reservoir of a power station to which the distributed pumped storage group s belongs at the expected end moment in the dispatching cycle;
Figure BDA00035278193700000310
and the storage capacity of the upper reservoir of the power station to which the distributed pumped storage group s belongs is expected at the initial moment in the dispatching cycle.
In the step 3, the optimization objective of the power grid dispatching lower-layer optimization model is that the total operation cost of all thermal power generating units in the power grid is the lowest, and the objective function is as follows:
Figure BDA00035278193700000311
f represents the total operation cost of all the thermal power generating units, and NG is a thermal power generating unit set; CI1,g() As a function of the cost of power generation, P, of the thermal power generating unit gg,tRepresenting the generated power of the thermal power generating unit g in a scheduling period t;
the operation constraint of the power grid dispatching lower layer optimization model comprises the following steps:
1) node energy balance constraints
Figure BDA00035278193700000312
Wherein, Pd,tActive load of a scheduling time interval t; NGiThermal power plant set NL representing node iiRepresenting the load set, NP, of a node iiDistributed pumped-storage aggregate, P, representing node is,tGenerated power for distributed pumped storage units s, Gs,tPumping power for distributed pumped storage units s, BijIs the imaginary part of the admittance between node i and node j; thetai,tFor scheduling period t node i electricityPhase of pressure, θj,tFor the phase of j voltage of a t node in a scheduling period, NI is a power grid node set, d represents a load, g represents a thermal power generating unit, and s represents a distributed pumped storage unit;
2) transmission line capacity constraints
Figure BDA00035278193700000313
Wherein the content of the first and second substances,
Figure BDA00035278193700000314
representing the maximum transmission capacity of the transmission line between the node i and the node j;
3) thermal power unit output constraint
Figure BDA00035278193700000315
Wherein the content of the first and second substances,
Figure BDA00035278193700000316
and
Figure BDA00035278193700000317
respectively representing the upper limit and the lower limit of the active output of the thermal power generating unit;
4) thermal power generating unit climbing restraint
Figure BDA0003527819370000041
Figure BDA0003527819370000042
Wherein the content of the first and second substances,
Figure BDA0003527819370000043
and
Figure BDA0003527819370000044
are respectively asMaximum upward and downward climbing speed, P, of thermal power generating unitg,t+1The generated power of the thermal power generating unit g is scheduled at the time period t + 1.
The objective function of the distributed pumped storage participation power grid scheduling model in the step 4 is the same as that of the distributed pumped storage upper decision model in the step 2;
the constraint conditions of the distributed pumped storage participation power grid scheduling model comprise operation constraints of a distributed pumped storage upper layer decision model and operation constraints of a power grid scheduling lower layer optimization model converted by applying a KKT condition;
the formula of the operation constraint of the power grid dispatching lower-layer optimization model converted by applying the KKT condition is as follows:
Figure BDA0003527819370000045
Figure BDA0003527819370000046
Figure BDA0003527819370000047
Figure BDA0003527819370000048
Figure BDA0003527819370000049
Figure BDA00035278193700000410
Figure BDA00035278193700000411
Figure BDA00035278193700000412
Figure BDA00035278193700000413
the method comprises the following steps that gamma is a Lagrange objective function value of a power grid scheduling lower-layer optimization model, and NG is a thermal power generating unit set; NGiThermal power plant set NL representing node iiRepresenting the load set, NP, of a node iiDistributed pumped-storage aggregation, CI, representing a node i1,g() As a function of the cost of power generation, P, of the thermal power generating unit gg,tRepresenting the generated power of the thermal power generating unit g in a scheduling period t; pd,tActive load of a scheduling time interval t; ps,tGenerated power for distributed pumped storage units s, Gs,tPumping power for distributed pumped storage units s, BijIs the imaginary part of the admittance between node i and node j; thetai,tFor scheduling the phase of the voltage at node i for a period of time, θj,tPhase of voltage at node j for a scheduling period of time;
Figure BDA00035278193700000414
representing the maximum transmission capacity of the transmission line between the node i and the node j;
Figure BDA00035278193700000415
and
Figure BDA0003527819370000051
respectively representing the upper limit and the lower limit of the active output of the thermal power generating unit;
Figure BDA0003527819370000052
and
Figure BDA0003527819370000053
maximum upward and downward climbing rates, P, of thermal power generating unitg,t+1The generated power of the thermal power generating unit g is scheduled at a time interval t + 1; NI is a grid node set, uI,tFor scheduling periodthe corresponding lagrangian multiplier is constrained by the t-node energy balance,
Figure BDA0003527819370000054
a Lagrange multiplier corresponding to the upper boundary of the transmission line capacity constraint in the scheduling period t,u l,tfor the lagrangian multiplier corresponding to the lower boundary of the power transmission line capacity constraint in the scheduling period t,
Figure BDA0003527819370000055
a Lagrange multiplier corresponding to the upper boundary of the output constraint of the thermal power generating unit in the scheduling period t,u g,tlagrange multiplier u corresponding to lower boundary of output constraint of thermal power generating unit in scheduling period tup,tLagrange multiplier u corresponding to climbing constraint on thermal power generating unit in scheduling period tdown,tAnd constraining a corresponding Lagrange multiplier for the climbing of the thermal power generating unit in the scheduling time period t.
The invention has the beneficial effects that:
1) the invention gives consideration to the optimal operation of the power grid and the distributed storage benefits, and provides a new idea for the distributed storage to participate in the power grid dispatching.
2) The invention considers the operation constraints of the distributed pumped storage and the thermal power generating units, coordinates and optimizes the output plans of the two units, excavates the action of the distributed pumped storage participating in peak clipping and valley filling of the power grid, and realizes the optimal operation of the whole power grid.
3) The invention realizes the aggregate management and scheduling decision of the distributed pumped storage energy through a virtual power plant form, solves the problem that the scattered power resources such as the distributed pumped storage energy are difficult to regulate and control, and provides a foundation for fully playing the regulating effect of the distributed pumped storage energy on the power grid.
Drawings
FIG. 1 is a flow chart of a method of the present invention;
FIG. 2 is a diagram of a 30 test node grid architecture according to an embodiment of the present invention;
FIG. 3 is a diagram of distributed pumped-storage power plant pumping power of an embodiment;
FIG. 4 is a daily variation graph of the storage capacity of the distributed pumped-storage upper reservoir of the embodiment;
fig. 5 is a diagram of the daily change of the capacity of the reservoir under the distributed pumped storage according to the embodiment.
Detailed Description
The invention is described in further detail below with reference to the following figures and specific embodiments:
as shown in fig. 1, the present invention comprises the steps of:
step 1: acquiring next-day load prediction data of a power grid and power transmission line parameters; the power grid mainly comprises a plurality of thermal power generating units, distributed pumped storage units, power transmission lines and loads. When a power grid model is established, a part of electrical equipment is collected to serve as one node of a power grid, all the power grid nodes are connected through a power transmission line, and a thermal power generating unit, a distributed pumped storage unit and a load are arranged in the node.
Step 2: constructing a distributed pumped storage upper-layer decision model based on a virtual power plant; the virtual power plant is formed by aggregating a plurality of distributed pumped storage units.
The step 2 specifically comprises the following steps:
aggregating a plurality of distributed pumped storage units in the power grid to obtain a virtual power plant, and determining the pumped water and the pumped water of each distributed pumped storage unit based on the virtual power plant and with the aim of maximizing all the distributed pumped storage benefits,Power generationAnd (3) establishing a distributed pumped storage upper layer decision model based on the virtual power plant, wherein the objective function of the distributed pumped storage upper layer decision model is as follows:
Figure BDA0003527819370000061
wherein, R represents all the distributed pumping and storage benefits, and T is a scheduling time interval set in a scheduling period; NP is the number of the distributed pumped storage units; CIp,s()、CIg,s() Respectively a power generation benefit function and a pumping cost function of the distributed pumping energy storage unit s; ps,tGenerating power G of distributed pumped storage units s for a scheduling period ts,tFor the water pumping power of the distributed pumped storage unit s in the scheduling time period t, p is the power generation state, and g is the water pumping stateS is the serial number of the distributed pumped storage unit, and t is the serial number of the scheduling time interval;
the operational constraints of the distributed pumped-storage upper layer decision model (i.e., the distributed pumped-storage group) include:
1) force restraint:
usPs,min≤Ps,t≤usPs,max
Gs,t＝vsGs,e
us+vs＝1
wherein, Ps,minAnd Ps,maxRespectively the minimum output and the maximum output of the distributed pumped storage unit s; gs,eRated pumping power of the distributed pumped storage unit s; u. ofsAnd vsRespectively representing the power generation state and the pumping state u of the distributed pumped storage unit ssAnd vsAll are variables from 0 to 1;
2) and (4) library capacity constraint:
Figure BDA0003527819370000062
Figure BDA0003527819370000063
Figure BDA0003527819370000064
Figure BDA0003527819370000065
Figure BDA0003527819370000066
wherein the content of the first and second substances,
Figure BDA0003527819370000067
and
Figure BDA0003527819370000068
respectively representing the water storage capacities of an upper reservoir and a lower reservoir of a power station to which a distributed pumped storage unit s belongs in a scheduling time period t + 1;
Figure BDA0003527819370000069
and
Figure BDA00035278193700000610
respectively representing the water storage capacities of an upper reservoir and a lower reservoir of a power station to which a distributed pumped storage unit s belongs in a scheduling time period t; c. Cs,gAnd cs,pRespectively representing the pumping efficiency and the power generation efficiency of the distributed pumped storage unit s; Δ t represents the pumping time or the power generation time;
Figure BDA00035278193700000611
andV s URthe upper limit and the lower limit of the water storage capacity of the upper reservoir are respectively set;
Figure BDA0003527819370000071
V s LRthe upper limit and the lower limit of the water storage capacity of a lower reservoir of a power station to which the distributed pumped storage unit s belongs are respectively set;δ s URand
Figure BDA0003527819370000072
maximum storage capacity change values of the first time period and the last time period of each day of an upper reservoir of a power station to which the distributed pumped storage unit s belongs are respectively obtained;
Figure BDA0003527819370000073
representing the storage capacity of an upper reservoir of a power station to which the distributed pumped storage group s belongs at the expected end moment in the dispatching cycle;
Figure BDA0003527819370000074
and the storage capacity of the upper reservoir of the power station to which the distributed pumped storage group s belongs is expected at the initial moment in the dispatching cycle.
And step 3: establishing a power grid dispatching lower-layer optimization model;
in the step 3, the optimization target of the power grid dispatching lower-layer optimization model is that the total operation cost of all thermal power generating units in the power grid is the lowest, and the objective function is as follows:
Figure BDA0003527819370000075
f represents the total operation cost of all the thermal power generating units, and NG is a thermal power generating unit set; CI1,g() As a function of the cost of power generation, P, of the thermal power generating unit gg,tRepresenting the generated power of the thermal power generating unit g in a scheduling period t;
the operation constraint of the power grid dispatching lower layer optimization model comprises the following steps:
1) node energy balance constraints
Figure BDA0003527819370000076
Wherein, Pd,tActive load of a scheduling time interval t; NGiThermal power plant set NL representing node iiRepresenting the load set, NP, of a node iiDistributed pumped-storage aggregate, P, representing node is,tGenerated power for distributed pumped storage units s, Gs,tPumping power for distributed pumped storage units s, BijIs the imaginary part of the admittance between node i and node j; thetai,tFor scheduling the phase of the voltage at node i for a period of time, θj,tFor the phase of j voltage of a t node in a scheduling period, NI is a power grid node set, d represents a load, g represents a thermal power generating unit, and s represents a distributed pumped storage unit;
2) transmission line capacity constraints
Figure BDA0003527819370000077
Wherein the content of the first and second substances,
Figure BDA0003527819370000078
indicating that the representation is located between node i and node jThe maximum transmission capacity of the transmission line;
3) thermal power unit output constraint
Figure BDA0003527819370000079
Wherein the content of the first and second substances,
Figure BDA00035278193700000710
and
Figure BDA00035278193700000711
respectively representing the upper limit and the lower limit of the active output of the thermal power generating unit;
4) thermal power generating unit climbing restraint
Figure BDA00035278193700000712
Figure BDA00035278193700000713
Wherein the content of the first and second substances,
Figure BDA00035278193700000714
and
Figure BDA00035278193700000715
maximum upward and downward climbing rates, P, of thermal power generating unitg,t+1The generated power of the thermal power generating unit g is scheduled at the time period t + 1.
And 4, step 4: model conversion is carried out on a distributed pumped storage upper layer decision model and a power grid dispatching lower layer optimization model based on a virtual power plant by utilizing a Kunstack condition (a KKT condition), a distributed pumped storage participation power grid dispatching model is obtained, then the distributed pumped storage participation power grid dispatching model is solved according to load prediction data and grid frame parameters of a power grid, and the output of a thermal power generating unit of the power grid, the pumping state and the pumping power of the distributed pumped storage unit are obtained.
The objective function of the distributed pumped storage participation power grid scheduling model in the step 4 is the same as that of the distributed pumped storage upper decision model in the step 2;
the constraint conditions of the distributed pumped storage participation power grid scheduling model comprise operation constraints of a distributed pumped storage upper layer decision model and operation constraints of a power grid scheduling lower layer optimization model converted by applying a KKT condition;
the formula of the operation constraint of the power grid dispatching lower-layer optimization model converted by applying the KKT condition is as follows:
Figure BDA0003527819370000081
Figure BDA0003527819370000082
Figure BDA0003527819370000083
Figure BDA0003527819370000084
Figure BDA0003527819370000085
Figure BDA0003527819370000086
Figure BDA0003527819370000087
Figure BDA0003527819370000088
Figure BDA0003527819370000089
the method comprises the following steps that gamma is a Lagrange objective function value of a power grid scheduling lower-layer optimization model, and NG is a thermal power generating unit set; NGiThermal power plant set NL representing node iiRepresenting the load set, NP, of a node iiDistributed pumped-storage aggregation, CI, representing a node i1,g() As a function of the cost of power generation, P, of the thermal power generating unit gg,tRepresenting the generated power of the thermal power generating unit g in a scheduling period t; pd,tActive load of a scheduling time interval t; ps,tGenerated power for distributed pumped storage units s, Gs,tPumping power for distributed pumped storage units s, BijIs the imaginary part of the admittance between node i and node j; thetai,tFor scheduling the phase of the voltage at node i for a period of time, θj,tPhase of voltage at node j for a scheduling period of time;
Figure BDA00035278193700000810
representing the maximum transmission capacity of the transmission line between the node i and the node j;
Figure BDA00035278193700000811
and
Figure BDA0003527819370000091
respectively representing the upper limit and the lower limit of the active output of the thermal power generating unit;
Figure BDA0003527819370000092
and
Figure BDA0003527819370000093
maximum upward and downward climbing rates, P, of thermal power generating unitg,t+1The generated power of the thermal power generating unit g is scheduled at a time interval t + 1; NI is a grid node set, uI,tFor the lagrangian multiplier corresponding to the energy balance constraint of the t node of the scheduling period,
Figure BDA0003527819370000094
a Lagrange multiplier corresponding to the upper boundary of the transmission line capacity constraint in the scheduling period t,u l,tfor the lagrangian multiplier corresponding to the lower boundary of the power transmission line capacity constraint in the scheduling period t,
Figure BDA0003527819370000095
a Lagrange multiplier corresponding to the upper boundary of the output constraint of the thermal power generating unit in the scheduling period t,u g,tlagrange multiplier u corresponding to lower boundary of output constraint of thermal power generating unit in scheduling period tup,tLagrange multiplier u corresponding to climbing constraint on thermal power generating unit in scheduling period tdown,tAnd (3) for a scheduling period t, adding the constraint to an operation constraint of a distributed pumped storage upper-layer decision model for a Lagrange multiplier corresponding to the ramp-down constraint of the thermal power generating unit, and converting the double-layer optimization problem into a single-layer optimization problem with balance constraint.
In this embodiment, data of a distributed pumped storage power station (that is, a distributed pumped storage group is set) in a certain province is adopted, the total installed capacity of the distributed pumped storage power station is 80MW, the average water head is 240m, and the upper reservoir capacity is adjustable: 20*104m3-87*104m3And the adjustable range of the reservoir capacity is as follows: 50*104m3-60*104m3The water pumping efficiency and the power generation efficiency are respectively 300MW/h m3And 240MW/h m3The system is provided with 3 distributed pumped storage power stations which participate in the day-ahead scheduling of a 30-node power grid and are respectively arranged at power grid nodes 2, 13 and 14 in the graph 2, and each time interval of the day-ahead scheduling is 1 h. In the embodiment, a CPLEX tool is used for solving to obtain daily pumped power and thermal power unit output plans of all distributed pumped storage power stations.
As can be seen from fig. 3, 4, and 5, the virtual power plant formed by the 3 distributed pumped storage power stations is characterized by a power generation state outside the load peak period, so as to achieve a certain peak clipping and valley filling effect, and meanwhile, due to the fact that the distributed pumped storage participates in scheduling, the power supply pressure of the thermal power generating unit in the load peak period is relieved. For 3 distributed pumped storage power stations, the pumping power of each power station in each time interval is not consistent, and the models of the distributed pumped storage power stations cannot be simply superposed due to different distribution positions of the distributed pumped storage power stations.

Claims (4)

1. A distributed pumped storage dispatching method based on a virtual power plant is characterized by comprising the following steps:
step 1: acquiring load prediction data and power transmission line parameters of a power grid;
step 2: constructing a distributed pumped storage upper-layer decision model based on a virtual power plant;
and step 3: establishing a power grid dispatching lower-layer optimization model;
and 4, step 4: model conversion is carried out on a distributed pumped storage upper layer decision model and a power grid dispatching lower layer optimization model based on a virtual power plant by utilizing a Countque condition to obtain a distributed pumped storage participation power grid dispatching model, then the distributed pumped storage participation power grid dispatching model is solved according to load prediction data and grid frame parameters of a power grid, and the output of a thermal power unit of the power grid, the pumping state and the pumping power of the distributed pumped storage unit are obtained.
2. The distributed pumped-storage dispatching method based on the virtual power plant as claimed in claim 1, wherein the step 2 is specifically:
aggregating a plurality of distributed pumped storage units in the power grid to obtain a virtual power plant, and determining the pumped water and the pumped water of each distributed pumped storage unit based on the virtual power plant and with the aim of maximizing all the distributed pumped storage benefits,Power generationAnd (3) establishing a distributed pumped storage upper layer decision model based on the virtual power plant, wherein the objective function of the distributed pumped storage upper layer decision model is as follows:
Figure FDA0003527819360000011
wherein, R represents all the distributed pumping and storage benefits, and T is a scheduling time interval set in a scheduling period; NP is the number of the distributed pumped storage units; CIp,s()、CIg,s() Respectively a power generation benefit function and a pumping cost function of the distributed pumping energy storage unit s; ps,tGenerating power G of distributed pumped storage units s for a scheduling period ts,tFor a scheduling time period t, the pumping power of the distributed pumped storage unit s, p is the power generation state, g is the pumping state, s is the serial number of the distributed pumped storage unit, and t is the serial number of the scheduling time period;
the operation constraints of the distributed pumped storage upper decision model comprise:
1) force restraint:
usPs,min≤Ps,t≤usPs,max
Gs,t＝vsGs,e
us+vs＝1
wherein, Ps,minAnd Ps,maxRespectively the minimum output and the maximum output of the distributed pumped storage unit s; gs,eRated pumping power of the distributed pumped storage unit s; u. ofsAnd vsRespectively representing the power generation state and the pumping state u of the distributed pumped storage unit ssAnd vsAll are variables from 0 to 1;
2) and (4) library capacity constraint:
Figure FDA0003527819360000021
Figure FDA0003527819360000022
Figure FDA0003527819360000023
Figure FDA0003527819360000024
Figure FDA0003527819360000025
wherein the content of the first and second substances,
Figure FDA0003527819360000026
and
Figure FDA0003527819360000027
respectively representing the water storage capacities of an upper reservoir and a lower reservoir of a power station to which a distributed pumped storage unit s belongs in a scheduling time period t + 1;
Figure FDA0003527819360000028
and
Figure FDA0003527819360000029
respectively representing the water storage capacities of an upper reservoir and a lower reservoir of a power station to which a distributed pumped storage unit s belongs in a scheduling time period t; c. Cs,gAnd cs,pRespectively representing the pumping efficiency and the power generation efficiency of the distributed pumped storage unit s; Δ t represents the pumping time or the power generation time;
Figure FDA00035278193600000210
andV s URthe upper limit and the lower limit of the water storage capacity of the upper reservoir are respectively set;
Figure FDA00035278193600000211
V s LRthe upper limit and the lower limit of the water storage capacity of a lower reservoir of a power station to which the distributed pumped storage unit s belongs are respectively set;δ s URand
Figure FDA00035278193600000212
each upper reservoir of the power station to which the distributed pumped storage unit belongsMaximum storage capacity variation values of the first and last time periods of the day;
Figure FDA00035278193600000213
representing the storage capacity of an upper reservoir of a power station to which the distributed pumped storage group s belongs at the expected end moment in the dispatching cycle;
Figure FDA00035278193600000214
and the storage capacity of the upper reservoir of the power station to which the distributed pumped storage group s belongs is expected at the initial moment in the dispatching cycle.
3. The distributed pumped-storage dispatching method based on the virtual power plant as claimed in claim 1, wherein: in the step 3, the optimization objective of the power grid dispatching lower-layer optimization model is that the total operation cost of all thermal power generating units in the power grid is the lowest, and the objective function is as follows:
Figure FDA00035278193600000215
f represents the total operation cost of all the thermal power generating units, and NG is a thermal power generating unit set; CI1,g() As a function of the cost of power generation, P, of the thermal power generating unit gg,tRepresenting the generated power of the thermal power generating unit g in a scheduling period t;
the operation constraint of the power grid dispatching lower layer optimization model comprises the following steps:
1) node energy balance constraints
Figure FDA00035278193600000216
Wherein, Pd,tActive load of a scheduling time interval t; NGiThermal power plant set NL representing node iiRepresenting the load set, NP, of a node iiDistributed pumped-storage aggregate, P, representing node is,tGenerated power for distributed pumped storage units s, Gs,tFor the pumping power of the distributed pumped-storage group s,Bijis the imaginary part of the admittance between node i and node j; thetai,tFor scheduling the phase of the voltage at node i for a period of time, θj,tFor the phase of j voltage of a t node in a scheduling period, NI is a power grid node set, d represents a load, g represents a thermal power generating unit, and s represents a distributed pumped storage unit;
2) transmission line capacity constraints
Figure FDA0003527819360000031
Wherein the content of the first and second substances,
Figure FDA0003527819360000032
representing the maximum transmission capacity of the transmission line between the node i and the node j;
3) thermal power unit output constraint
Figure FDA0003527819360000033
Wherein the content of the first and second substances,
Figure FDA0003527819360000034
and
Figure FDA0003527819360000035
respectively representing the upper limit and the lower limit of the active output of the thermal power generating unit;
4) thermal power generating unit climbing restraint
Figure FDA0003527819360000036
Figure FDA0003527819360000037
Wherein the content of the first and second substances,
Figure FDA0003527819360000038
and
Figure FDA0003527819360000039
maximum upward and downward climbing rates, P, of thermal power generating unitg,t+1The generated power of the thermal power generating unit g is scheduled at the time period t + 1.
4. The distributed pumped-storage scheduling method based on the virtual power plant according to claim 1, wherein the objective function of the distributed pumped-storage participation power grid scheduling model in the step 4 is the same as the objective function of the distributed pumped-storage upper decision model in the step 2;
the constraint conditions of the distributed pumped storage participation power grid scheduling model comprise operation constraints of a distributed pumped storage upper layer decision model and operation constraints of a power grid scheduling lower layer optimization model converted by applying a KKT condition;
the formula of the operation constraint of the power grid dispatching lower-layer optimization model converted by applying the KKT condition is as follows:
Figure FDA00035278193600000310
Figure FDA00035278193600000311
Figure FDA0003527819360000041
Figure FDA0003527819360000042
Figure FDA0003527819360000043
Figure FDA0003527819360000044
Figure FDA0003527819360000045
Figure FDA0003527819360000046
the method comprises the following steps that gamma is a Lagrange objective function value of a power grid scheduling lower-layer optimization model, and NG is a thermal power generating unit set; NGiThermal power plant set NL representing node iiRepresenting the load set, NP, of a node iiDistributed pumped-storage aggregation, CI, representing a node i1,g() As a function of the cost of power generation, P, of the thermal power generating unit gg,tRepresenting the generated power of the thermal power generating unit g in a scheduling period t; pd,tActive load of a scheduling time interval t; ps,tGenerated power for distributed pumped storage units s, Gs,tPumping power for distributed pumped storage units s, BijIs the imaginary part of the admittance between node i and node j; thetai,tFor scheduling the phase of the voltage at node i for a period of time, θj,tPhase of voltage at node j for a scheduling period of time;
Figure FDA0003527819360000047
representing the maximum transmission capacity of the transmission line between the node i and the node j;
Figure FDA0003527819360000048
and
Figure FDA0003527819360000049
respectively representing the upper limit and the lower limit of the active output of the thermal power generating unit;
Figure FDA00035278193600000410
and
Figure FDA00035278193600000411
maximum upward and downward climbing rates, P, of thermal power generating unitg,t+1The generated power of the thermal power generating unit g is scheduled at a time interval t + 1; NI is a grid node set, uI,tFor the lagrangian multiplier corresponding to the energy balance constraint of the t node of the scheduling period,
Figure FDA00035278193600000412
lagrange multiplier u corresponding to the upper boundary of the transmission line capacity constraint in the scheduling period tl,tFor the lagrangian multiplier corresponding to the lower boundary of the power transmission line capacity constraint in the scheduling period t,
Figure FDA00035278193600000413
a Lagrange multiplier corresponding to the upper boundary of the output constraint of the thermal power generating unit in the scheduling period t,u g,tlagrange multiplier u corresponding to lower boundary of output constraint of thermal power generating unit in scheduling period tup,tLagrange multiplier u corresponding to climbing constraint on thermal power generating unit in scheduling period tdown,tAnd constraining a corresponding Lagrange multiplier for the climbing of the thermal power generating unit in the scheduling time period t.
CN202210197795.4A 2022-03-02 2022-03-02 Distributed pumped storage dispatching method based on virtual power plant Pending CN114421540A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210197795.4A CN114421540A (en) 2022-03-02 2022-03-02 Distributed pumped storage dispatching method based on virtual power plant

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210197795.4A CN114421540A (en) 2022-03-02 2022-03-02 Distributed pumped storage dispatching method based on virtual power plant

Publications (1)

Publication Number Publication Date
CN114421540A true CN114421540A (en) 2022-04-29

Family

ID=81260717

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210197795.4A Pending CN114421540A (en) 2022-03-02 2022-03-02 Distributed pumped storage dispatching method based on virtual power plant

Country Status (1)

Country Link
CN (1) CN114421540A (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115062835A (en) * 2022-06-10 2022-09-16 山东大学 Active power distribution network distributed resource optimization scheduling method based on virtual power plant
CN117114330A (en) * 2023-08-31 2023-11-24 湖北清江水电开发有限责任公司 Pumped storage and cascade hydropower joint scheduling method based on virtual power plant

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115062835A (en) * 2022-06-10 2022-09-16 山东大学 Active power distribution network distributed resource optimization scheduling method based on virtual power plant
CN117114330A (en) * 2023-08-31 2023-11-24 湖北清江水电开发有限责任公司 Pumped storage and cascade hydropower joint scheduling method based on virtual power plant

Similar Documents

Publication Publication Date Title
CN110417048B (en) Direct-current power grid transmitting and receiving end combined peak regulation optimization method considering source grid load constraint
Cheng et al. An MILP-based model for short-term peak shaving operation of pumped-storage hydropower plants serving multiple power grids
CN103151803B (en) Method for optimizing wind power system-contained unit and backup configuration
CN109256799B (en) New energy power system optimal scheduling method based on sample entropy
CN106786799B (en) Power stepped power generation plan optimization method for direct current connecting line
WO2023065113A1 (en) Flexibility demand quantification and coordination optimization method for wind-solar-water multi-energy complementary system
CN108092324B (en) AGC control system and control method for wind power participating in peak shaving frequency modulation
CN114421540A (en) Distributed pumped storage dispatching method based on virtual power plant
CN108039737B (en) Source-grid-load coordinated operation simulation system
Yuan et al. Cross-regional integrated transmission of wind power and pumped-storage hydropower considering the peak shaving demands of multiple power grids
US12015275B2 (en) Hybrid power plant
CN103226735A (en) Wind power segmentation-based electric power system optimal scheduling method
Mladenov et al. Characterisation and evaluation of flexibility of electrical power system
CN110867907B (en) Power system scheduling method based on multi-type power generation resource homogenization
CN110808613A (en) Method for improving wind power utilization rate by using hybrid energy storage
CN111641233A (en) Electric power system day-based flexible peak regulation method considering new energy and load uncertainty
CN103490421B (en) Regional power grid direct regulating pumped storage power station group short period multi-power-grid load distribution method
CN115114854A (en) Two-stage self-organizing optimization aggregation method and system for distributed resources of virtual power plant
CN108448655B (en) Passive power grid wide-area power generation control method and system
CN117649119A (en) VCG theory-based clean energy carbon emission reduction value evaluation method
CN106339773B (en) Sensitivity-based constant volume planning method for distributed power supply of active power distribution network
CN116933493A (en) Method, device and storage medium for calculating carbon emission based on time sequence production simulation
CN116454944A (en) Energy storage device optimal configuration method and system based on random production simulation
CN110909959A (en) Wind power operation risk-considering multi-energy complementary power system robust optimization method
CN116316865A (en) Full-link coordination planning optimization method for high-proportion new energy regional power 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
TA01 Transfer of patent application right

Effective date of registration: 20221102

Address after: 310002 Shuicheng building, No. 1, Nanfu Road, Shangcheng District, Hangzhou, Zhejiang

Applicant after: STATE GRID ZHEJIANG ECONOMIC Research Institute

Address before: 310008 Shuicheng building, No.1 Nanfu Road, Shangcheng District, Hangzhou City, Zhejiang Province

Applicant before: STATE GRID ZHEJIANG ECONOMIC Research Institute

Applicant before: ZHEJIANG University

TA01 Transfer of patent application right