CN109508894A - Power System Planning method is coordinated in the storage of one provenance net lotus - Google Patents
Power System Planning method is coordinated in the storage of one provenance net lotus Download PDFInfo
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Abstract
本发明提供一种源网荷储协调电力系统规划方法，包括建立电源建设成本模型、电网建设成本模型、需求侧资源利用成本模型、储能装机成本模型、系统运行成本模型和系统排放成本模型；根据上述各模型，以规划期内系统总成本最小为目标，建立包括目标函数和约束条件的优化模型；根据优化模型，获取规划期内各年份各区域的：各类电源的装机容量、各类需求响应容量、各类储能的装机容量、各类能效电厂的装机容量，以及规划期内各年份各条跨区输电通道的容量。本发明利用数学模型求解得电源、电网、负荷侧资源与储能的优化发展方案，保证各类资源的协调，能够实现系统整体最优。
The present invention provides a power system planning method for coordination of source, network, load and storage, including establishing a power supply construction cost model, a power grid construction cost model, a demand-side resource utilization cost model, an energy storage installation cost model, a system operation cost model and a system emission cost model; According to the above models, with the goal of minimizing the total cost of the system during the planning period, an optimization model including the objective function and constraints is established; Demand response capacity, installed capacity of various types of energy storage, installed capacity of various energy efficiency power plants, and the capacity of each cross-regional transmission channel in each year during the planning period. The invention solves the optimal development plan of the power source, the power grid, the load-side resources and the energy storage by using the mathematical model, ensures the coordination of various resources, and can realize the overall optimization of the system.
Description
Technical field
The present invention relates to power supply system technical field, in particular to Power System Planning method is coordinated in provenance net lotus storage.
Background technique
Power System Planning aims to solve the problem that development and construction scheme of the electric system within following a period of time.According to routine
Cognitive frame, electric system are made of Generation Side, grid side, load side three parts.Conventional electric power systems organization usually only focuses on
The development program of power supply and power grid passes through allotment because traditional power balance mode is that workload demand is considered as fixed constant
Generating set goes to meet workload demand.As smart grid the relevant technologies gradually develop, electricity constantly mature with Power Market Construction
Power workload demand has certain controllability, and power supply and demand balance can be actively engaged in a manner of demand response.Therefore,
In Future Power System, load side resource can play the effect equivalent with source side resource to a certain extent, should be included in
Power System Planning considers scope.
In addition, as energy storage technology in recent years reaches its maturity, in the big rule of the fluctuations generation of electricity by new energy such as wind-powered electricity generation, photovoltaic power generation
In the case that mould develops, energy storage will play a significant role in Future Power System.The role that energy storage can both play the part of power supply (is put
Electricity), role's (charging) of load can be played the part of again.But meanwhile energy storage both non-power (not having real power capacity),
Non- load (certain practical use can not be converted electrical energy into) again.Therefore, in following power system architectures, energy storage should
As the 4th piece of component part of electric system for being listed in power supply, power grid, load.Therefore when carrying out Power System Planning, having must
Energy storage and power supply, power grid, load are considered as a whole, research and development source net lotus stores up coordinated planning method.
The main deficiency of existing Power System Planning technology is: power supply, power grid, load side resource, energy storage is not implemented
Plan as a whole optimization planning.Power supply and Electric Power Network Planning are coordinated consideration by some scholars, realize source net coordinated planning.Some scholars will be born
Lotus side resource, which is included in power supply or Electric Power Network Planning, to be accounted for, or the resource of three parts is considered together, realizes source net lotus association
Adjust planning.By the planning of energy storage, with distributed generation resource or power distribution network etc., certain element combines some scholars.But it has not yet to see
To all kinds of resource coordinations of the source side of electric system, grid side, load side, energy storage side are considered, the planning mould of Unified Solution
Type method.
Summary of the invention
The present invention provides a kind of source net lotus storage coordination Power System Planning method at least partly solving above-mentioned technical problem.
In a first aspect, the present invention, which provides provenance net lotus storage, coordinates Power System Planning method, comprising:
Establish power construction cost model, power grid construction cost model, Demand-side utilization of resources cost model, energy storage installation
Cost model, system operation cost model and system discharge costs model；Wherein, the power construction cost model is project period
The installed capacity of all kinds of power supplys in interior each region of each time and the relational model of interior power construction cost project period, the power grid construction
The relationship mould of cost model power grid construction cost for the capacity of the transregional passway for transmitting electricity of each time each item in project period and in project period
Type, the Demand-side utilization of resources cost model be project period in each time each region all kinds of energy efficiency power plants installed capacity,
In project period all kinds of demand response capacity in each time each region and in project period Demand-side utilization of resources cost relational model,
The installed capacity and energy storage in project period that the energy storage installation cost model is all kinds of energy storage in each time each region in project period
The relational model for cost of installing, the system operation cost model are that the installation of all kinds of power supplys in each time each region in project period is held
The relational model of system operation cost in amount and project period, the system discharge costs model are each time each region in project period
The installed capacity of all kinds of power supplys and in project period discharge costs relational model；
According to power construction cost model, power grid construction cost model, Demand-side utilization of resources cost model, energy storage installation
Cost model, system operation cost model, system discharge costs model, and using the minimization of total system cost in project period as target,
Establish the Optimized model including objective function and constraint condition；
According to the Optimized model, the installed capacity of all kinds of power supplys in each time each region in project period is obtained, in project period
The capacity of the transregional passway for transmitting electricity of each time each item, the installed capacity of all kinds of energy efficiency power plants in each time each region, rule in project period
Draw the phase in each time each region all kinds of demand response capacity and in project period all kinds of energy storage in each time each region installation hold
Amount.
Preferably, the power construction cost model is
In formula, A1For interior power construction cost project period；T is the serial number in time in project period；T is the time in project period
Number, for precognition value；R is region serial number；S is power supply type serial number；ΩrTo plan related regional ensemble；ΩsFor power supply kind
Class set；Css,r,tFor the installed capacity of the s kind power supply type of the region t r；Css,r,t-1For the s of the region t-1 r
The installed capacity of kind power supply type；Qs,r,tFor the retired capacity of the s kind power supply type of the region t r, for precognition value；PsFor
The unit capacity installation cost of s kind power supply, for precognition value；R is discount rate, for precognition value.
Preferably, the power grid construction cost model is
In formula: A2For power grid construction cost in project period；G is passway for transmitting electricity serial number；ΩsFor transregional passway for transmitting electricity set；
Cgg,tFor the capacity of the g articles passway for transmitting electricity of t；Cgg,t-1For the capacity of the g articles passway for transmitting electricity of t-1；PgIt is defeated for the g articles
The unit dilatation cost of electric channel, for precognition value.
Preferably, the Demand-side utilization of resources cost model is
In formula: A3For Demand-side utilization of resources cost in project period；E is energy efficiency power plant type serial number；ΩeFor energy efficiency power plant
Type set；Cee,r,tFor the installed capacity of the e kind energy efficiency power plant of the region t r；Cee,r,t-1It is the of the region t-1 r
The installed capacity of e kind energy efficiency power plant；PeFor the unit dilatation cost of e class energy efficiency power plant, for precognition value；D is demand response class
Type serial number, ΩdFor demand response type set；Cdd,r,tFor the d class demand response capacity of the region t r；EdIt is needed for d class
The incentive cost of response is sought, for precognition value.
Preferably, the energy storage installation cost model is
In formula: A4For energy storage installation cost in project period；C is energy storage type serial number；ΩcFor energy storage type set；Ccc,r,t
For the installed capacity of the c class energy storage of the region t r；Ccc,r,t-1For the installed capacity of the c class energy storage of the region t-1 r；
PcFor the unit dilatation cost of c class energy storage, for precognition value.
Preferably, the system operation cost model is
In formula: A5For system operation cost in project period；HsFor the annual utilization hours of s kind power supply；EsFor s kind power supply
Unit quantity of electricity operating cost, for precognition value.
Preferably, the system discharge costs model is
In formula: A6For system discharge costs in project period；Mcs,tFor the carbon emission coefficient of t s kind power supply, for precognition
Value；PrCFor unit carbon emission cost, for precognition value.
Preferably, the objective function is
F=min (A1+A2+A3+A4+A5+A6)
In formula: F is system synthesis sheet in project period.
Preferably, the constraint condition includes each region power balance constraint, the constraint of each region electric quantity balancing, transmission line capability
Constraint, transmission of electricity Constraint, the constraint of each district system peak modulation capacity, the constraint of each district system regulations speed, power extension scale
Constraint, passway for transmitting electricity extension scale restriction, energy efficiency power plant extension scale restriction, demand response scale restriction, energy storage extend scale
Constraint, renewable energy goal constraint, CO2 emission constraint, sulfur dioxide (SO2) emissions constraint and discharged nitrous oxides constraint.
Preferably, each region power balance is constrained to
In formula: Confs,rFor s kind power supply region r peak load contribute confidence coefficient, for precognition value；Ωr2xFor region r
The passway for transmitting electricity set of outside power transmission；Ptg,tFor the power value of the channel t g conveying；Ωx2rIt is powered defeated from the external world for region r
Electric channel set；lgFor the transmission of electricity line loss per unit of channel g, for precognition value；lrFor transmission and distribution line loss rate average in the r of region, for precognition
Value；η is reserve factor, for precognition value；Fpr,tFor the peak load predicted value of the region t r, for precognition value；βeFor efficiency electricity
The peak load simultaneity factor of factory, for precognition value；βdFor the peak load simultaneity factor of demand response, for precognition value；
Each region electric quantity balancing is constrained to
In formula: Etg,tFor the charge value of the channel t g conveying；Fer,tThe electrical demand predicted value of the region t r is pre-
Know value；HeFor the annual utilization hours of e kind energy efficiency power plant, for precognition value；HdIt is pre- for the annual utilization hours of d kind demand response
Know value；πcFor the efficiency for charge-discharge of c class energy storage, for precognition value；HcFor the annual utilization hours of c kind energy storage, for precognition value；
The Transmission Capacity Constraints are
Ptg,t≤Cgg,t；
The transmission of electricity Constraint is
Etg,t≤Cgg,t·Hg
In formula: HgHourage is utilized for the year maximum of channel g, for precognition value；
Each district system peak modulation capacity is constrained to
In formula: Ωspr、Ωdpr、ΩeprRespectively with power supply, the demand response, energy storage set of peak modulation capacity；μs,rFor area
The average peak regulation depth of domain r s class power supply, for precognition value；λr,tIt is pre- for the load peak-valley ratio predicted value of the region t r
Know value；ΩsvFor fluctuation power supply set；υsFor the peak regulation service demand factor of s class power supply, for precognition value；
Each district system regulations speed is constrained to
In formula: δs,r、δd,r、δd,cRespectively region r s class power supply, d class demand response resource, c class energy storage go out
Power regulations speed, for precognition value；δl,rFor region r unit time workload demand change rate, for precognition value；
The power extension scale restriction is
0≤Css,r,t-Css,r,t-1+Qs,r,t≤Csms,r,t
In formula: Csms,r,tMaximum for the region t r s class power supply builds capacity, for precognition value；
The passway for transmitting electricity extends scale restriction
0≤Cgg,t-Cgg,t-1≤Cgmg,t
In formula: Cgmg,tFor the largest extension capacity of the g articles passway for transmitting electricity of t, for precognition value；
The energy efficiency power plant extends scale restriction
0≤Cee,r,t-Cee,r,t-1≤Ceme,r,t
In formula: Ceme,r,tFor the largest extension capacity of the region t r e class energy efficiency power plant, for precognition value；
The demand response scale restriction is
Cdd,r,t≤Cdmd,r,t
In formula: Cdmd,r,tFor the maximum capacity of the region t r d class demand response, for precognition value；
The energy storage extends scale restriction
0≤Ccc,r,t-Ccc,r,t-1≤Ccmc,r,t
In formula: Ccmc,r,tFor the largest extension capacity of the region t r c class energy storage, for precognition value；
The renewable energy goal constraint is
In formula: ΩsrFor renewable power supply type set；αr,tIt is pre- for region r t renewable power supply accounting target value
Know value；
The CO2 emission is constrained to
In formula: Ecr,tFor the region r t electric system CO2 emission upper limit, for precognition value；
The sulfur dioxide (SO2) emissions are constrained to
In formula: Mss,tFor the sulfur dioxide (SO2) emissions coefficient of t s kind power supply, for precognition value；Esr,tFor region r t
The electric system sulfur dioxide (SO2) emissions upper limit, for precognition value；
The discharged nitrous oxides are constrained to
In formula: Mns,tFor the discharged nitrous oxides coefficient of t s kind power supply, for precognition value；Enr,tFor region r t
The electric system discharged nitrous oxides upper limit, for precognition value.
As shown from the above technical solution, the embodiment of the present invention solves current electric power system source net lotus storage coordinated planning model
The problem of method lacks, using mathematical model can estimate simultaneously obtains power supply, power grid, load side resource and the optimization of energy storage are sent out
Exhibition scheme, and guarantee that the development of all kinds of resources is mutually coordinated, it can be realized system total optimization.
Detailed description of the invention
Fig. 1 is that Power System Planning method Integral Thought figure is coordinated in the provenance net lotus storage that one embodiment of the invention provides；
Fig. 2 is the flow chart that Power System Planning method is coordinated in the provenance net lotus storage that one embodiment of the invention provides；
Fig. 3 a- Fig. 3 f is that power source planning result of the present invention shows interface；
Fig. 4 a- Fig. 4 e is that Electric Power Network Planning result of the present invention shows interface；
Fig. 5 a- Fig. 5 d is that electric load side resource planning result of the present invention shows interface；
Fig. 6 a and Fig. 6 b are that energy storage program results of the present invention show interface.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
I.e. considering electric power system source net lotus as a whole stores up the interactive relation of all kinds of resources in systems to general thought of the invention
With systematic electricity is balanced, electric quantity balancing, peak regulation balance etc. contribution function, by using in operational research mathematical optimization model
Means, solve Power System Planning optimization problem, obtain all kinds of power supplys, power grid, load side resource and energy storage programme.Source
The storage of net lotus coordinates Power System Planning method Integral Thought figure and sees attached drawing 1, wherein the source net lotus of electric system stores up coordinated planning packet
Include the dilatation of source side resource development, the dilatation of grid side resource development, the resource development dilatation of workload demand side and energy storage side resource hair
Spread holds, and realizes unified optimization.
Fig. 2 is the flow chart that Power System Planning method is coordinated in the provenance net lotus storage that one embodiment of the invention provides.
Power System Planning method is coordinated in provenance net lotus storage as shown in Figure 2, comprising:
S201, power construction cost model, power grid construction cost model, Demand-side utilization of resources cost model, storage are established
Can install cost model, system operation cost model and system discharge costs model；Wherein, the power construction cost model is
The relational model of the installed capacity of all kinds of power supplys in each time each region and interior power construction cost project period, the electricity in project period
Net construction cost model is the capacity of the transregional passway for transmitting electricity of each time each item and power grid construction cost in project period in project period
Relational model, the Demand-side utilization of resources cost model are the installation of all kinds of energy efficiency power plants in each time each region in project period
Capacity, in project period all kinds of demand response capacity in each time each region and in project period Demand-side utilization of resources cost relationship
Model, energy storage installation cost model are in project period in the installed capacity and project period of all kinds of energy storage in each time each region
The relational model of energy storage installation cost, the system operation cost model are the dress of all kinds of power supplys in each time each region in project period
Machine capacity and in project period system operation cost relational model, the system discharge costs model be project period in each time it is each
The installed capacity of all kinds of power supplys in region and in project period discharge costs relational model；
S202, according to power construction cost model, power grid construction cost model, Demand-side utilization of resources cost model, storage
Can install cost model, system operation cost model, system discharge costs model, and be with the minimization of total system cost in project period
Target establishes the Optimized model including objective function and constraint condition；
S203, according to the Optimized model, obtain installed capacity, the planning of all kinds of power supplys in each time each region in project period
The capacity of the transregional passway for transmitting electricity of each time each item in phase, the installation of all kinds of energy efficiency power plants in each time each region is held in project period
Amount, in project period all kinds of demand response capacity in each time each region and in project period all kinds of energy storage in each time each region dress
Machine capacity.
The embodiment of the present invention solves the problems, such as current electric power system source net lotus storage coordinated planning model method missing, utilizes
Mathematical model can estimate simultaneously obtain power supply, power grid, load side resource and the optimized development scheme of energy storage, and guarantee all kinds of
The development of resource is mutually coordinated, can be realized system total optimization.
It should be noted that the embodiment of the present invention first has to execute following step:
(1) power technology characteristic is analyzed
Analyze coal electricity, pneumoelectric, nuclear power, water power, land wind-powered electricity generation, photovoltaic power generation, photo-thermal power generation, offshore wind farm, biomass fermentation
Unit capacity the installation cost, unit quantity of electricity operating cost, annual utilization hours, emission factor of all kinds of power supplys such as electricity, water-storage
(corresponding CO2 emission coefficient, sulfur dioxide (SO2) emissions coefficient, discharged nitrous oxides coefficient), peak regulation depth, power output adjust speed
Rate etc. compiles the relevant technologies economic parameters.
(2) electric power network technique characteristic is analyzed
The unit dilatation cost of each transregional passway for transmitting electricity, year maximum are analyzed using hourage, transmission of electricity line loss per unit etc., is collected whole
Manage the relevant technologies economic parameters.
(3) analysis load side resources technology characteristic
Analyze incentive cost, the power output regulations speed, annual utilization hours, peak load simultaneity factor of demand response, Yi Jineng
Dilatation cost, the annual utilization hours, peak load simultaneity factor for imitating power plant, compile the relevant technologies economic parameters.
(4) energy storage technology characteristic is analyzed
The unit dilatation cost of typical energy storage technology, annual utilization hours, efficiency for charge-discharge, power output regulations speed etc. are analyzed,
Compile the relevant technologies economic parameters.
(5) requirement forecasting is carried out
The electricity needs in region each in project period is predicted with electrical demand, arranges in project period each region year by year most
Big predicted load and electrical demand predict Value Data.
(6) benchmark annual data is arranged
A calendar year currently just to have terminated arranges standard year power system development situation as planning standard year,
Initial state (initial state i.e. as each model) as planning.
(7) scene parameter is set
For uncertain factor main in project period and development policies parameter, typical index is chosen, carries out parameter
Setting, using the main differentiation as different scenes, such as the energy transition target (corresponding " renewable power supply accounting target value ", " electric power
The system CO2 emission upper limit ", " the electric system sulfur dioxide (SO2) emissions upper limit ", " the electric system discharged nitrous oxides upper limit ")
Deng.
Based on above-mentioned, as a kind of preferred embodiment, the power construction cost model is
In formula, A1For interior power construction cost project period；T is the serial number in time in project period；T is the time in project period
Number, for precognition value；R is region serial number；S is power supply type serial number；ΩrTo plan related regional ensemble；ΩsFor power supply kind
Class set；Css,r,tFor the installed capacity of the s kind power supply type of the region t r；Css,r,t-1For the s of the region t-1 r
The installed capacity of kind power supply type；Qs,r,tFor the retired capacity of the s kind power supply type of the region t r, for precognition value；PsFor
The unit capacity installation cost of s kind power supply, for precognition value；R is discount rate, for precognition value.
Based on above-mentioned, as a kind of preferred embodiment, the power grid construction cost model is
In formula: A2For power grid construction cost in project period；G is passway for transmitting electricity serial number；ΩsFor transregional passway for transmitting electricity set；
Cgg,tFor the capacity of the g articles passway for transmitting electricity of t；Cgg,t-1For the capacity of the g articles passway for transmitting electricity of t-1；PgIt is defeated for the g articles
The unit dilatation cost of electric channel, for precognition value.
Based on above-mentioned, as a kind of preferred embodiment, the Demand-side utilization of resources cost model is
In formula: A3For Demand-side utilization of resources cost in project period；E is energy efficiency power plant type serial number；ΩeFor energy efficiency power plant
Type set；Cee,r,tFor the installed capacity of the e kind energy efficiency power plant of the region t r；Cee,r,t-1It is the of the region t-1 r
The installed capacity of e kind energy efficiency power plant；PeFor the unit dilatation cost of e class energy efficiency power plant, for precognition value；D is demand response class
Type serial number, ΩdFor demand response type set；Cdd,r,tFor the d class demand response capacity of the region t r；EdIt is needed for d class
The incentive cost of response is sought, for precognition value.
Based on above-mentioned, as a kind of preferred embodiment, the energy storage installation cost model is
In formula: A4For energy storage installation cost in project period；C is energy storage type serial number；ΩcFor energy storage type set；Ccc,r,t
For the installed capacity of the c class energy storage of the region t r；Ccc,r,t-1For the installed capacity of the c class energy storage of the region t-1 r；
PcFor the unit dilatation cost of c class energy storage, for precognition value.
Based on above-mentioned, as a kind of preferred embodiment, the system operation cost model is
In formula: A5For system operation cost in project period；HsFor the annual utilization hours of s kind power supply；EsFor s kind power supply
Unit quantity of electricity operating cost, for precognition value.
Based on above-mentioned, as a kind of preferred embodiment, the system discharge costs model is
In formula: A6For system discharge costs in project period；Mcs,tFor the carbon emission coefficient of t s kind power supply, for precognition
Value；PrCFor unit carbon emission cost, for precognition value.
Based on above-mentioned, as a kind of preferred embodiment, the objective function is
F=min (A1+A2+A3+A4+A5+A6)
In formula: F is system synthesis sheet in project period.
In a specific embodiment, the constraint condition includes each region power balance constraint, each region electric quantity balancing
Constraint, Transmission Capacity Constraints, transmission of electricity Constraint, each district system peak modulation capacity constraint, each district system regulations speed constraint,
Power extension scale restriction, passway for transmitting electricity extension scale restriction, energy efficiency power plant extension scale restriction, demand response scale restriction,
Energy storage extends scale restriction, renewable energy goal constraint, CO2 emission constraint, sulfur dioxide (SO2) emissions constraint and nitrogen oxidation
Object exhaust emission constraint.
As a kind of preferred embodiment, each region power balance is constrained to
In formula: Confs,rFor s kind power supply region r peak load contribute confidence coefficient, for precognition value；Ωr2xFor region r
The passway for transmitting electricity set of outside power transmission；Ptg,tFor the power value of the channel t g conveying；Ωx2rIt is powered defeated from the external world for region r
Electric channel set；lgFor the transmission of electricity line loss per unit of channel g, for precognition value；lrFor transmission and distribution line loss rate average in the r of region, for precognition
Value；η is reserve factor, for precognition value；Fpr,tFor the peak load predicted value of the region t r, for precognition value；βeFor efficiency electricity
The peak load simultaneity factor of factory, for precognition value；βdFor the peak load simultaneity factor of demand response, for precognition value；
The constraint is meant to ensure that each region power supply and demand balance, and it considers transregional transmission of electricity and transmission of electricity line losses.
Each region electric quantity balancing is constrained to
In formula: Etg,tFor the charge value of the channel t g conveying；Fer,tThe electrical demand predicted value of the region t r is pre-
Know value；HeFor the annual utilization hours of e kind energy efficiency power plant, for precognition value；HdIt is pre- for the annual utilization hours of d kind demand response
Know value；πcFor the efficiency for charge-discharge of c class energy storage, for precognition value；HcFor the annual utilization hours of c kind energy storage, for precognition value；
The constraint is meant to ensure that each region electricity equilibrium of supply and demand, and it considers transregional transmission of electricity and transmission of electricity line losses.
The Transmission Capacity Constraints are
Ptg,t≤Cgg,t；
The constraint is meant to ensure that each channel transmission power is no more than its channel capacity.
The transmission of electricity Constraint is
Etg,t≤Cgg,t·Hg
In formula: HgHourage is utilized for the year maximum of channel g, for precognition value；
The constraint is meant to ensure that each channel year conveying electricity is no more than its year maximum trnamission capacity.
Each district system peak modulation capacity is constrained to
In formula: Ωspr、Ωdpr、ΩeprRespectively with power supply, the demand response, energy storage set of peak modulation capacity；μs,rFor area
The average peak regulation depth of domain r s class power supply, for precognition value；λr,tIt is pre- for the load peak-valley ratio predicted value of the region t r
Know value；ΩsvFor fluctuation power supply set；υsFor the peak regulation service demand factor of s class power supply, for precognition value；
Total peak capacity of all kinds of flexibility resources of system must meet load fluctuation and uncertain power supply goes out fluctuation pair
The demand of peak capacity.
Each district system regulations speed is constrained to
In formula: δs,r、δd,r、δd,cRespectively region r s class power supply, d class demand response resource, c class energy storage go out
Power regulations speed, for precognition value；δl,rFor region r unit time workload demand change rate, for precognition value；
Total regulations speed of all kinds of flexibility resources of system must meet load fluctuation and uncertain power supply goes out fluctuation pair
The demand of system regulations speed.
The power extension scale restriction is
0≤Css,r,t-Css,r,t-1+Qs,r,t≤Csms,r,t
In formula: Csms,r,tMaximum for the region t r s class power supply builds capacity, for precognition value；
Consider the factors such as all kinds of endowment of resources in each region, power construction speed, all kinds of power extension scales in each region are set
Constraint.
The passway for transmitting electricity extends scale restriction
0≤Cgg,t-Cgg,t-1≤Cgmg,t
In formula: Cgmg,tFor the largest extension capacity of the g articles passway for transmitting electricity of t, for precognition value；
Consider that the factors such as transmission of electricity corridor, line construction speed can be used, each passway for transmitting electricity extension scale restriction is set.
The energy efficiency power plant extends scale restriction
0≤Cee,r,t-Cee,r,t-1≤Ceme,r,t
In formula: Ceme,r,tFor the largest extension capacity of the region t r e class energy efficiency power plant, for precognition value；
The demand response scale restriction is
Cdd,r,t≤Cdmd,r,t
In formula: Cdmd,r,tFor the maximum capacity of the region t r d class demand response, for precognition value；
The energy storage extends scale restriction
0≤Ccc,r,t-Ccc,r,t-1≤Ccmc,r,t
In formula: Ccmc,r,tFor the largest extension capacity of the region t r c class energy storage, for precognition value；
The renewable energy goal constraint is
In formula: ΩsrFor renewable power supply type set；αr,tIt is pre- for region r t renewable power supply accounting target value
Know value；
According to the target that each region sets Renewable Energy Development, which is set.
The CO2 emission is constrained to
In formula: Ecr,tFor the region r t electric system CO2 emission upper limit, for precognition value；
To reduce electric system carbon emission, promotes the development of electric system low-carbon, system CO2 emission total amount is set
Upper limit constraint.
The sulfur dioxide (SO2) emissions are constrained to
In formula: Mss,tFor the sulfur dioxide (SO2) emissions coefficient of t s kind power supply, for precognition value；Esr,tFor region r t
The electric system sulfur dioxide (SO2) emissions upper limit, for precognition value；
To reduce electric system pollutant emission, the setting system sulfur dioxide (SO2) emissions total amount upper limit is constrained.
The discharged nitrous oxides are constrained to
In formula: Mns,tFor the discharged nitrous oxides coefficient of t s kind power supply, for precognition value；Enr,tFor region r t
The electric system discharged nitrous oxides upper limit, for precognition value.
To reduce electric system pollutant emission, the setting system discharged nitrous oxides total amount upper limit is constrained.
All kinds of power supplys in each time each region, power grid, load side in the project period that the present invention is solved according to Optimized model
The development scale of resource and energy storage carries out data preparation, the main indicator of analysis designer's concern, all kinds of resource hairs in such as each region
Each region total size of exhibition scale accounting, key water non-leap year class resource etc..In addition, can further calculate according to actual needs
Correlated results, such as each region carbon dioxide and pollutant emission level, electric system Construction and operation cost are planned to other.With
For each area source net lotus storage coordinated planning of China, part typical calculation result is as shown in attached drawing 3a to attached drawing 6b.
Above-mentioned load side, Demand-side, workload demand side are same meaning.
The present invention can be implemented as some or all equipment or dress for executing method as described herein
Set program (for example, computer program and computer program product).Such realization program of the invention, which can store, to be calculated
On machine readable medium, or it may be in the form of one or more signals.Such signal can be from internet website
Downloading obtains, and is perhaps provided on the carrier signal or is provided in any other form.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and ability
Field technique personnel can be designed alternative embodiment without departing from the scope of the appended claims.In the claims,
Any reference symbol between parentheses should not be configured to limitations on claims.Word "comprising" does not exclude the presence of not
Element or step listed in the claims.Word "a" or "an" located in front of the element does not exclude the presence of multiple such
Element.The present invention can be by means of including the hardware of several different elements and being come by means of properly programmed computer real
It is existing.In the unit claims listing several devices, several in these devices can be through the same hardware branch
To embody.The use of word first, second, and third does not indicate any sequence.These words can be explained and be run after fame
Claim.
Embodiment of above is only suitable for illustrating the present invention, and not limitation of the present invention, in relation to the common of technical field
Technical staff can also make a variety of changes and modification without departing from the spirit and scope of the present invention, therefore all
Equivalent technical solution also belongs to scope of the invention, and scope of patent protection of the invention should be defined by the claims.
Claims (10)
1. Power System Planning method is coordinated in provenance net lotus storage characterized by comprising
Establish power construction cost model, power grid construction cost model, Demand-side utilization of resources cost model, energy storage installation cost
Model, system operation cost model and system discharge costs model；Wherein, the power construction cost model is each in project period
The installed capacity of all kinds of power supplys in time each region and the relational model of interior power construction cost project period, the power grid construction cost
The relational model of model power grid construction cost for the capacity of the transregional passway for transmitting electricity of each time each item in project period and in project period, institute
Stating Demand-side utilization of resources cost model is the installed capacity of all kinds of energy efficiency power plants in each time each region, project period in project period
All kinds of demand response capacity in interior each region of each time and in project period Demand-side utilization of resources cost relational model, the storage
The cost model that can install be project period in each time each region all kinds of energy storage installed capacity and in project period energy storage installation at
This relational model, the system operation cost model are the installed capacity of all kinds of power supplys in each time each region and rule in project period
The relational model of system operation cost in the phase of drawing, the system discharge costs model are all kinds of electricity in each time each region in project period
The installed capacity in source and in project period discharge costs relational model；
According to power construction cost model, power grid construction cost model, Demand-side utilization of resources cost model, energy storage installation cost
Model, system operation cost model, system discharge costs model, and using the minimization of total system cost in project period as target, it establishes
Optimized model including objective function and constraint condition；
According to the Optimized model, the installed capacity of all kinds of power supplys in each time each region, each year in project period in project period are obtained
The capacity of each transregional passway for transmitting electricity of item of part, the installed capacity of all kinds of energy efficiency power plants in each time each region, project period in project period
All kinds of demand response capacity in interior each region of each time and in project period all kinds of energy storage in each time each region installed capacity.
2. the method according to claim 1, wherein the power construction cost model is
In formula, A1For interior power construction cost project period；T is the serial number in time in project period；T is year number in project period, is pre-
Know value；R is region serial number；S is power supply type serial number；ΩrTo plan related regional ensemble；ΩsFor power type set；
Css,r,tFor the installed capacity of the s kind power supply type of the region t r；Css,r,t-1For the s kind power supply class of the region t-1 r
The installed capacity of type；Qs,r,tFor the retired capacity of the s kind power supply type of the region t r, for precognition value；PsFor s kind power supply
Unit capacity install cost, for precognition value；R is discount rate, for precognition value.
3. according to the method described in claim 2, it is characterized in that, the power grid construction cost model is
In formula: A2For power grid construction cost in project period；G is passway for transmitting electricity serial number；ΩsFor transregional passway for transmitting electricity set；Cgg,tFor
The capacity of the g articles passway for transmitting electricity of t；Cgg,t-1For the capacity of the g articles passway for transmitting electricity of t-1；PgFor the g articles passway for transmitting electricity
Unit dilatation cost, for precognition value.
4. according to the method described in claim 3, it is characterized in that, the Demand-side utilization of resources cost model is
In formula: A3For Demand-side utilization of resources cost in project period；E is energy efficiency power plant type serial number；ΩeFor energy efficiency power plant type
Set；Cee,r,tFor the installed capacity of the e kind energy efficiency power plant of the region t r；Cee,r,t-1For the e kind of the region t-1 r
The installed capacity of energy efficiency power plant；PeFor the unit dilatation cost of e class energy efficiency power plant, for precognition value；D is demand response type sequence
Number, ΩdFor demand response type set；Cdd,r,tFor the d class demand response capacity of the region t r；EdIt is rung for d class demand
The incentive cost answered, for precognition value.
5. according to the method described in claim 4, it is characterized in that, energy storage installation cost model is
In formula: A4For energy storage installation cost in project period；C is energy storage type serial number；ΩcFor energy storage type set；Ccc,r,tFor t
The installed capacity of the c class energy storage in year region r；Ccc,r,t-1For the installed capacity of the c class energy storage of the region t-1 r；PcIt is
The unit dilatation cost of c class energy storage, for precognition value.
6. according to the method described in claim 5, it is characterized in that, the system operation cost model is
In formula: A5For system operation cost in project period；HsFor the annual utilization hours of s kind power supply；EsFor the list of s kind power supply
Position electricity operating cost, for precognition value.
7. according to the method described in claim 6, it is characterized in that, the system discharge costs model is
In formula: A6For system discharge costs in project period；Mcs,tFor the carbon emission coefficient of t s kind power supply, for precognition value；
PrCFor unit carbon emission cost, for precognition value.
8. the method according to the description of claim 7 is characterized in that the objective function is
F=min (A1+A2+A3+A4+A5+A6)
In formula: F is system synthesis sheet in project period.
9. according to the method described in claim 8, it is characterized in that, the constraint condition include the constraint of each region power balance,
Each region electric quantity balancing constraint, Transmission Capacity Constraints, transmission of electricity Constraint, the constraint of each district system peak modulation capacity, each region system
System regulations speed constraint, energy efficiency power plant extension scale restriction, needs power extension scale restriction, passway for transmitting electricity extension scale restriction
Ask response scale restriction, energy storage extension scale restriction, renewable energy goal constraint, CO2 emission constraint, sulfur dioxide
Exhaust emission constraint and discharged nitrous oxides constraint.
10. according to the method described in claim 9, it is characterized in that, each region power balance is constrained to
In formula: Confs,rFor s kind power supply region r peak load contribute confidence coefficient, for precognition value；Ωr2xIt is outside for region r
The passway for transmitting electricity set of power transmission；Ptg,tFor the power value of the channel t g conveying；Ωx2rIt is logical from extraneous powered transmission of electricity for region r
Road set；lgFor the transmission of electricity line loss per unit of channel g, for precognition value；lrFor transmission and distribution line loss rate average in the r of region, for precognition value；η is
Reserve factor, for precognition value；Fpr,tFor the peak load predicted value of the region t r, for precognition value；βeMost for energy efficiency power plant
Big load simultaneity factor, for precognition value；βdFor the peak load simultaneity factor of demand response, for precognition value；
Each region electric quantity balancing is constrained to
In formula: Etg,tFor the charge value of the channel t g conveying；Fer,tThe electrical demand predicted value of the region t r, for precognition
Value；HeFor the annual utilization hours of e kind energy efficiency power plant, for precognition value；HdFor the annual utilization hours of d kind demand response, for precognition
Value；πcFor the efficiency for charge-discharge of c class energy storage, for precognition value；HcFor the annual utilization hours of c kind energy storage, for precognition value；
The Transmission Capacity Constraints are
Ptg,t≤Cgg,t；
The transmission of electricity Constraint is
Etg,t≤Cgg,t·Hg
In formula: HgHourage is utilized for the year maximum of channel g, for precognition value；
Each district system peak modulation capacity is constrained to
In formula: Ωspr、Ωdpr、ΩeprRespectively with power supply, the demand response, energy storage set of peak modulation capacity；μs,rFor region r
The average peak regulation depth of s class power supply, for precognition value；λr,tFor the load peak-valley ratio predicted value of the region t r, for precognition
Value；ΩsvFor fluctuation power supply set；υsFor the peak regulation service demand factor of s class power supply, for precognition value；
Each district system regulations speed is constrained to
In formula: δs,r、δd,r、δd,cRespectively region r s class power supply, d class demand response resource, c class energy storage power output tune
Rate is saved, for precognition value；δl,rFor region r unit time workload demand change rate, for precognition value；
The power extension scale restriction is
0≤Css,r,t-Css,r,t-1+Qs,r,t≤Csms,r,t
In formula: Csms,r,tMaximum for the region t r s class power supply builds capacity, for precognition value；
The passway for transmitting electricity extends scale restriction
0≤Cgg,t-Cgg,t-1≤Cgmg,t
In formula: Cgmg,tFor the largest extension capacity of the g articles passway for transmitting electricity of t, for precognition value；
The energy efficiency power plant extends scale restriction
0≤Cee,r,t-Cee,r,t-1≤Ceme,r,t
In formula: Ceme,r,tFor the largest extension capacity of the region t r e class energy efficiency power plant, for precognition value；
The demand response scale restriction is
Cdd,r,t≤Cdmd,r,t
In formula: Cdmd,r,tFor the maximum capacity of the region t r d class demand response, for precognition value；
The energy storage extends scale restriction
0≤Ccc,r,t-Ccc,r,t-1≤Ccmc,r,t
In formula: Ccmc,r,tFor the largest extension capacity of the region t r c class energy storage, for precognition value；
The renewable energy goal constraint is
In formula: ΩsrFor renewable power supply type set；αr,tFor region r t renewable power supply accounting target value, for precognition
Value；
The CO2 emission is constrained to
In formula: Ecr,tFor the region r t electric system CO2 emission upper limit, for precognition value；
The sulfur dioxide (SO2) emissions are constrained to
In formula: Mss,tFor the sulfur dioxide (SO2) emissions coefficient of t s kind power supply, for precognition value；Esr,tFor region r t electric power
The system sulfur dioxide (SO2) emissions upper limit, for precognition value；
The discharged nitrous oxides are constrained to
In formula: Mns,tFor the discharged nitrous oxides coefficient of t s kind power supply, for precognition value；Enr,tFor region r t electric power
The system discharged nitrous oxides upper limit, for precognition value.
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Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110570026A (en) * | 2019-08-21 | 2019-12-13 | 国网江苏省电力有限公司经济技术研究院 | Grid system construction planning method, computer equipment and storage medium |
| CN110866647A (en) * | 2019-11-18 | 2020-03-06 | 华润智慧能源有限公司 | User side energy storage control method, device, equipment and storage medium |
| CN112070397A (en) * | 2020-09-08 | 2020-12-11 | 国网能源研究院有限公司 | Multi-power-supply expansion optimization decision method and system for coordinated operation of non-fossil energy power generation |
| CN112366695A (en) * | 2020-10-30 | 2021-02-12 | 国网冀北电力有限公司 | Social cost-based new energy optimal proportion determination method and device |
| CN112734593A (en) * | 2020-12-24 | 2021-04-30 | 国网北京市电力公司 | Power distribution network planning method |
| CN113436028A (en) * | 2020-03-23 | 2021-09-24 | 电力规划总院有限公司 | Power resource analysis method and device |
| CN114186792A (en) * | 2021-11-15 | 2022-03-15 | 国网江苏省电力有限公司 | Energy grid planning method based on double-carbon target |
| CN115204624A (en) * | 2022-06-24 | 2022-10-18 | 国网山东省电力公司青岛市即墨区供电公司 | Regional power distribution method and system based on lowest cost algorithm |
| CN117314708A (en) * | 2023-10-26 | 2023-12-29 | 南方电网能源发展研究院有限责任公司 | Carbon emission reduction system optimization method, device, equipment, medium and program product |
| CN120073718A (en) * | 2025-04-27 | 2025-05-30 | 北京国电通网络技术有限公司 | Power supply optimal configuration method, system and equipment based on source network load coordination interaction |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106600104A (en) * | 2016-11-07 | 2017-04-26 | 国网江苏省电力公司 | Evaluation method for evaluating energy efficiency of integrated energy system |
| CN107528385A (en) * | 2017-06-29 | 2017-12-29 | 国网辽宁省电力有限公司 | A kind of source net lotus control method for coordinating and system based on new energy consumption |
| CN107591830A (en) * | 2017-10-23 | 2018-01-16 | 国网能源研究院有限公司 | A kind of electricity power planing method for being suitable to Renewable Energy Development at high proportion |
| CN107633333A (en) * | 2017-10-16 | 2018-01-26 | 国家电网公司 | The source lotus storage method for optimizing scheduling and system of flexible transformer station's regional power grid |
-
2018
- 2018-11-27 CN CN201811425662.8A patent/CN109508894A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106600104A (en) * | 2016-11-07 | 2017-04-26 | 国网江苏省电力公司 | Evaluation method for evaluating energy efficiency of integrated energy system |
| CN107528385A (en) * | 2017-06-29 | 2017-12-29 | 国网辽宁省电力有限公司 | A kind of source net lotus control method for coordinating and system based on new energy consumption |
| CN107633333A (en) * | 2017-10-16 | 2018-01-26 | 国家电网公司 | The source lotus storage method for optimizing scheduling and system of flexible transformer station's regional power grid |
| CN107591830A (en) * | 2017-10-23 | 2018-01-16 | 国网能源研究院有限公司 | A kind of electricity power planing method for being suitable to Renewable Energy Development at high proportion |
Non-Patent Citations (2)
| Title |
|---|
| 张宁等: "面向2050年的中国电力发展展望", 《中国能源》 * |
| 曾顺奇等: "考虑源网荷储协调优化的主动配电网网架规划", 《南方电网技术》 * |
Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110570026A (en) * | 2019-08-21 | 2019-12-13 | 国网江苏省电力有限公司经济技术研究院 | Grid system construction planning method, computer equipment and storage medium |
| CN110866647A (en) * | 2019-11-18 | 2020-03-06 | 华润智慧能源有限公司 | User side energy storage control method, device, equipment and storage medium |
| CN113436028A (en) * | 2020-03-23 | 2021-09-24 | 电力规划总院有限公司 | Power resource analysis method and device |
| CN113436028B (en) * | 2020-03-23 | 2023-04-28 | 电力规划总院有限公司 | Power resource analysis method and device |
| CN112070397A (en) * | 2020-09-08 | 2020-12-11 | 国网能源研究院有限公司 | Multi-power-supply expansion optimization decision method and system for coordinated operation of non-fossil energy power generation |
| CN112366695B (en) * | 2020-10-30 | 2023-10-24 | 国网冀北电力有限公司 | Method and device for determining optimal proportion of new energy based on social cost |
| CN112366695A (en) * | 2020-10-30 | 2021-02-12 | 国网冀北电力有限公司 | Social cost-based new energy optimal proportion determination method and device |
| CN112734593A (en) * | 2020-12-24 | 2021-04-30 | 国网北京市电力公司 | Power distribution network planning method |
| CN112734593B (en) * | 2020-12-24 | 2024-07-30 | 国网北京市电力公司 | Power distribution network planning method |
| CN114186792A (en) * | 2021-11-15 | 2022-03-15 | 国网江苏省电力有限公司 | Energy grid planning method based on double-carbon target |
| CN115204624A (en) * | 2022-06-24 | 2022-10-18 | 国网山东省电力公司青岛市即墨区供电公司 | Regional power distribution method and system based on lowest cost algorithm |
| CN117314708A (en) * | 2023-10-26 | 2023-12-29 | 南方电网能源发展研究院有限责任公司 | Carbon emission reduction system optimization method, device, equipment, medium and program product |
| CN120073718A (en) * | 2025-04-27 | 2025-05-30 | 北京国电通网络技术有限公司 | Power supply optimal configuration method, system and equipment based on source network load coordination interaction |
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Application publication date: 20190322 |