CN116247719A - Micro-grid two-stage robust optimal configuration method based on ladder carbon transaction - Google Patents

Micro-grid two-stage robust optimal configuration method based on ladder carbon transaction Download PDF

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CN116247719A
CN116247719A CN202211511759.7A CN202211511759A CN116247719A CN 116247719 A CN116247719 A CN 116247719A CN 202211511759 A CN202211511759 A CN 202211511759A CN 116247719 A CN116247719 A CN 116247719A
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power
micro
stage
constraint
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刘伟
苗桂喜
连勇
王鑫
元亮
杨增
窦宪鹤
王静
席晟哲
孙浩然
闫娇
赵悠悠
王丽晔
郑惠瀛
崔哲芳
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Anyang Power Supply Co of State Grid Henan Electric Power Co Ltd
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Anyang Power Supply Co of State Grid Henan Electric Power Co Ltd
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    • 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
    • C—CHEMISTRY; METALLURGY
    • C25—ELECTROLYTIC OR ELECTROPHORETIC PROCESSES; APPARATUS THEREFOR
    • C25B—ELECTROLYTIC OR ELECTROPHORETIC PROCESSES FOR THE PRODUCTION OF COMPOUNDS OR NON-METALS; APPARATUS THEREFOR
    • C25B1/00—Electrolytic production of inorganic compounds or non-metals
    • C25B1/01—Products
    • C25B1/02—Hydrogen or oxygen
    • C25B1/04—Hydrogen or oxygen by electrolysis of water
    • C—CHEMISTRY; METALLURGY
    • C25—ELECTROLYTIC OR ELECTROPHORETIC PROCESSES; APPARATUS THEREFOR
    • C25B—ELECTROLYTIC OR ELECTROPHORETIC PROCESSES FOR THE PRODUCTION OF COMPOUNDS OR NON-METALS; APPARATUS THEREFOR
    • C25B15/00—Operating or servicing cells
    • C25B15/02—Process control or regulation
    • C25B15/023—Measuring, analysing or testing during electrolytic production
    • C—CHEMISTRY; METALLURGY
    • C25—ELECTROLYTIC OR ELECTROPHORETIC PROCESSES; APPARATUS THEREFOR
    • C25B—ELECTROLYTIC OR ELECTROPHORETIC PROCESSES FOR THE PRODUCTION OF COMPOUNDS OR NON-METALS; APPARATUS THEREFOR
    • C25B9/00—Cells or assemblies of cells; Constructional parts of cells; Assemblies of constructional parts, e.g. electrode-diaphragm assemblies; Process-related cell features
    • C25B9/60—Constructional parts of cells
    • C25B9/65—Means for supplying current; Electrode connections; Electric inter-cell connections
    • 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
    • H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
    • 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
    • H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • 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
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02M—APPARATUS FOR CONVERSION BETWEEN AC AND AC, BETWEEN AC AND DC, OR BETWEEN DC AND DC, AND FOR USE WITH MAINS OR SIMILAR POWER SUPPLY SYSTEMS; CONVERSION OF DC OR AC INPUT POWER INTO SURGE OUTPUT POWER; CONTROL OR REGULATION THEREOF
    • H02M3/00—Conversion of dc power input into dc power output
    • H02M3/02—Conversion of dc power input into dc power output without intermediate conversion into ac
    • H02M3/04—Conversion of dc power input into dc power output without intermediate conversion into ac by static converters
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02M—APPARATUS FOR CONVERSION BETWEEN AC AND AC, BETWEEN AC AND DC, OR BETWEEN DC AND DC, AND FOR USE WITH MAINS OR SIMILAR POWER SUPPLY SYSTEMS; CONVERSION OF DC OR AC INPUT POWER INTO SURGE OUTPUT POWER; CONTROL OR REGULATION THEREOF
    • H02M7/00—Conversion of ac power input into dc power output; Conversion of dc power input into ac power output
    • H02M7/02—Conversion of ac power input into dc power output without possibility of reversal
    • H02M7/04—Conversion of ac power input into dc power output without possibility of reversal by static converters
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02M—APPARATUS FOR CONVERSION BETWEEN AC AND AC, BETWEEN AC AND DC, OR BETWEEN DC AND DC, AND FOR USE WITH MAINS OR SIMILAR POWER SUPPLY SYSTEMS; CONVERSION OF DC OR AC INPUT POWER INTO SURGE OUTPUT POWER; CONTROL OR REGULATION THEREOF
    • H02M7/00—Conversion of ac power input into dc power output; Conversion of dc power input into ac power output
    • H02M7/42—Conversion of dc power input into ac power output without possibility of reversal
    • H02M7/44—Conversion of dc power input into ac power output without possibility of reversal by static converters
    • 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
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/22—The renewable source being solar energy
    • H02J2300/24—The renewable source being solar energy of photovoltaic origin
    • 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
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/28—The renewable source being wind 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
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/30—The power source being a fuel cell
    • 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

Abstract

The invention relates to a micro-grid two-stage robust optimal configuration method based on ladder carbon transaction, which comprises the following steps: s1, constructing a micro-grid model comprising a fan, a photovoltaic cell, a diesel generator and a hydrogen energy storage device based on an output model of a distributed power supply; s2, constructing a two-stage robust optimization configuration model, wherein the first stage takes the minimum annual comprehensive cost of the micro-grid as an optimization target, and the second stage introduces a ladder carbon transaction mechanism and takes the minimum sum of annual running cost and annual carbon transaction cost of the micro-grid as the optimization target; the fluctuation range of the load power is described by adopting a base uncertainty set U; and S3, solving the two-stage robust optimal configuration model by adopting a column sum constraint method C & CG to obtain a micro-grid optimal configuration result. Compared with the prior art, the configuration capacity of the renewable energy source power supply and the flexibility of the micro-grid are improved, and the carbon emission of the micro-grid is reduced by constructing the two-stage optimization configuration model of the hydrogen-containing energy storage micro-grid which introduces the ladder carbon transaction mechanism.

Description

Micro-grid two-stage robust optimal configuration method based on ladder carbon transaction
Technical Field
The invention relates to the field of micro-grid planning, in particular to a micro-grid two-stage robust optimal configuration method based on ladder carbon transaction.
Background
In recent years, hydrogen energy, which is clean and environment-friendly and can be stored for a long time, is considered to be introduced into a micro-grid as chemical energy storage. The optimal configuration of the micro-grid is the key point of the design of the micro-grid, and the reasonable configuration can improve the utilization efficiency and the electric energy quality of each power supply in the hydrogen-containing storage micro-grid. At present, the research of introducing a hydrogen energy storage system into the micro-grid optimal configuration is relatively few.
The wind-solar energy storage coupling hydrogen production system has larger influence on factors, and the efficiency of the hydrogen storage device has influence on the unit hydrogen production cost. The utilization of the electrical hydrogen conversion device to use hydrogen energy as a flexible resource can improve the flexibility of the micro-grid.
However, the research on the hydrogen-containing energy storage micro-grid in the prior art is not deep enough, the influence of carbon transaction on the optimal configuration of the hydrogen-containing energy storage micro-grid is not fully considered, the flexibility is still insufficient, and the carbon emission of the micro-grid is still high.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide the two-stage robust optimal configuration method for the micro-grid based on the ladder carbon transaction, which has the advantages of low carbon emission, high flexibility and high reliability.
The aim of the invention can be achieved by the following technical scheme:
the invention provides a micro-grid two-stage robust optimal configuration method based on ladder carbon transaction, which comprises the following steps:
step S1, constructing a micro-grid model comprising a fan, a photovoltaic cell, a diesel generator and a hydrogen energy storage device based on an output model of a distributed power supply;
s2, constructing a two-stage robust optimal configuration model, wherein the first stage takes the minimum annual value comprehensive cost of the micro-grid as an optimal target, and the second stage introduces a step carbon transaction mechanism and takes the minimum sum of annual operation cost and annual carbon transaction cost of the micro-grid as the optimal target; the fluctuation range of the load power is described by adopting a base uncertainty set U;
and S3, solving the two-stage robust optimal configuration model by adopting a column and constraint method C & CG to obtain a microgrid optimal configuration result.
Preferably, the hydrogen energy storage device in the step S1 is used for coping with randomness and uncertainty of the output of the fan and the photovoltaic cell in the micro-grid by adopting a hydrogen production-storage-generation system; the energy conversion in the hydrogen production process and the mathematical expression corresponding to the power generation process are as follows:
W ec ＝P ec η ec λ
in which W is ec The amount of hydrogen produced for the electrolyzer; p (P) ec The electrolysis power of the electrolytic tank; η (eta) ec Is the electrolysis efficiency of the electrolytic cell; lambda is the amount of hydrogen generated by electroelectrolysis per degree;
Figure BDA0003969394630000021
wherein P is fc Output electric power of the fuel cell; w (W) fc The amount of hydrogen consumed for the fuel cell; η (eta) fc Is the efficiency of the fuel cell; mu is the amount of hydrogen needed to produce electricity per degree; subscript t is time;
Figure BDA0003969394630000022
wherein E is hst,t And E is hst,t+1 The hydrogen storage amount is respectively the hydrogen storage amount at the time t and the time t+1 of the hydrogen storage tank;
Figure BDA0003969394630000023
and->
Figure BDA0003969394630000024
The efficiency of hydrogen storage and hydrogen release of the hydrogen storage tank respectively; />
Figure BDA0003969394630000025
And->
Figure BDA0003969394630000026
The hydrogen storage amount and the hydrogen release amount are respectively.
Preferably, the output model of the fan in step S1 is:
Figure BDA0003969394630000027
wherein P is WT The output power of the fan, v is the wind speed, v F The wind speed is cut out for the fan; v C Cutting in wind speed for the fan; v R The rated wind speed of the fan is set; p (P) WT,N Rated power of the wind turbine generator;
the output mathematical model of the photovoltaic cell is as follows:
P PV ＝P STC G[1+k PV (T c -25)]/1000
wherein P is PV Is photovoltaic output; k (k) PV A power temperature coefficient; g is irradiance; p (P) STC Maximum test power under standard test conditions; t (T) c The working temperature of the photovoltaic cell;
the output model of the diesel generator is as follows:
f DE ＝αP DE +βP DE,rated
wherein f DE The fuel consumption of the diesel generator in unit time; p (P) DE 、P DE,rated The output power and the nominal power of the diesel engine are respectively; alpha and beta are slope and intercept coefficients in the fuel consumption-power curve.
Preferably, the two-stage robust optimization configuration model in step S2 is specifically: the first-stage optimization is capacity configuration optimization, and aims at minimizing annual comprehensive cost of a micro-grid and the like; the second stage of optimization is system operation optimization, and aims at minimizing the sum of annual operation cost and annual carbon transaction cost of the micro-grid; transmitting the capacity configuration result obtained by optimizing the first stage to the second stage, transmitting the solved optimal operation result to the first stage according to the known equipment capacity by the second stage, and iterating to obtain the optimal configuration result;
the equivalent model mathematical expression of the two-stage robust optimal configuration model is as follows:
Figure BDA0003969394630000031
wherein C is int Is an objective function of the first stage; n is a decision variable of the first stage, including the capacity of each device of the micro-grid; u is an uncertain variable comprising wind-light output and load power of the micro-grid, wherein the fluctuation range of the load power is described by adopting a base uncertainty set U; c (C) ope The method is characterized in that the method is an objective function of the second stage, x and y are decision variables of the second stage, and x is a 0/1 state variable of each output device; y is the time sequence output of each output device;
preferably, the first stage optimization model in the two-stage robust optimization configuration model is specifically:
objective function:
Figure BDA0003969394630000032
wherein r is WT 、r PV 、r G 、r H The years of discount of a blower, a photovoltaic cell, a diesel engine and a hydrogen energy storage device respectively; c WT.int 、c PV.int 、c G.int 、c i.int The unit investment cost of the blower, the photovoltaic cell, the diesel engine and the hydrogen energy storage device are respectively; ρ is the discount rate; p (P) PV.max 、P WT.max 、P G.max And E is i.max The unit capacities of the photovoltaic device, the fan, the diesel engine and the hydrogen energy storage device are respectively; n (N) WT 、N PV 、N DE 、N i Respectively a fan, a photovoltaic, a diesel engine and hydrogen energy storageThe configuration quantity of the device is a decision variable of the first stage; n represents a hydrogen energy storage device, comprising an electrolytic cell, a fuel cell and a hydrogen storage tank;
the constraint condition is number constraint, and the expression is:
Figure BDA0003969394630000033
wherein N is s,WT 、N s,PV 、N s,DE 、N s,FC 、N s,EC 、N s,HST The number of fans, photovoltaic panels, diesel generators, fuel cells, electrolytic tanks and hydrogen storage tanks installed for each scene respectively;
Figure BDA0003969394630000034
Figure BDA0003969394630000035
the maximum number of fans, photovoltaic panels, diesel generators, fuel cells, electrolyzer and hydrogen storage tanks allowed to be installed for each scenario, respectively.
Preferably, the second-stage optimization model of the two-stage robust optimization configuration model is specifically:
objective function:
Figure BDA0003969394630000041
in the method, in the process of the invention,
Figure BDA0003969394630000042
respectively M total operating costs of the devices, < >>
Figure BDA0003969394630000043
The step carbon transaction cost is determined according to a step carbon transaction mechanism;
the constraint conditions include power balance constraint, electrolyzer power constraint, fuel cell power constraint, hydrogen storage tank constraint, distributed power supply output constraint and grid interaction constraint.
Preferably, the step carbon transaction mechanism is: dividing the carbon emission into a plurality of sections, wherein the more the carbon emission is, the higher the carbon trade price is, the greater the carbon trade cost is, and the corresponding stepped carbon trade cost is
Figure BDA0003969394630000044
The expression is:
Figure BDA0003969394630000045
Figure BDA0003969394630000046
in the method, in the process of the invention,
Figure BDA0003969394630000047
for the step carbon trade cost, the positive value indicates that the system needs to purchase carbon emission rights, and the negative value indicates that the system sells the carbon emission rights to gain benefits; />
Figure BDA0003969394630000048
A trade price for carbon; e (E) G Carbon emissions for the system; l is the carbon emission interval length; lambda is the price increase amplitude of the carbon trade; beta de Carbon emission intensity beta of unit electric quantity of diesel generator grid,gf The carbon emission intensity of thermal power of outsourcing unit electric quantity; p (P) de Power per unit electric quantity of diesel generator, P grid,buy Maximum power for the micro grid to purchase electricity from the large grid.
Preferably, the constraint conditions comprise power balance constraint, electrolyzer power constraint, fuel cell power constraint, hydrogen storage tank constraint, distributed power supply output constraint and grid interaction constraint, and the specific expression is:
1) Power balance constraint:
P WT (t)+P PV (t)+P H (t)+P DE (t)+P grid (t)＝P L (t)
wherein P is WT 、P PV 、P H 、P DE 、P grid 、P L The output power of the fan, the output power of the photovoltaic cell, the power supply power of the hydrogen energy storage device, the output power of the diesel engine, the power supply power of the micro-grid and the total output power are respectively;
2) Cell power constraint:
Figure BDA0003969394630000049
wherein P is EC 、
Figure BDA00039693946300000410
Respectively the electrolysis power and the maximum electrolysis power of the electrolytic tank;
3) Fuel cell power constraint:
Figure BDA0003969394630000051
wherein: p (P) FC 、
Figure BDA0003969394630000052
The output power and the maximum output power of the fuel cell respectively;
4) Hydrogen storage tank restraint:
S ht,min ≤S ht (t)≤S ht,max
S ht (t 0 )＝S ht (t N )
wherein S is ht (t) is the capacity state of the hydrogen storage tank at the time t, S ht,min ≤S ht (t)≤S ht,max The hydrogen storage tank capacity minimum value and the hydrogen storage tank capacity maximum value are respectively; s is S ht (t 0 ) And S is ht (t N ) Respectively the beginning t of the scheduling period 0 Last t N A capacity state of the hydrogen storage tank;
5) Distributed power supply output constraint:
the inequality constraint of the generated power of a fan, a photovoltaic generator and a diesel generator:
Figure BDA0003969394630000053
wherein P is pv 、P wt 、P de The power generation power of the fan, the photovoltaic and the diesel generator respectively, P pv,N 、P wt,N 、P de,N The maximum power of the fan, the photovoltaic and the diesel generator are respectively;
6) Grid interaction constraint:
-P grid,sell ≤P grid ≤P grid,buy
wherein P is grid,sell Maximum power sum P for selling electricity to large grid for micro grid grid,buy Maximum power for the micro grid to purchase electricity from the large grid.
Preferably, in the step S2, the fluctuation range of the load power is described by a radix uncertainty set U, and the specific expression is:
Figure BDA0003969394630000054
wherein u is L l (t) and u L u (t) load fluctuation u respectively L Lower and upper limits of (t); deltau L max (t) is the fluctuation deviation of the maximum load power; Γ -shaped structure t Is an uncertainty parameter for the period t.
Preferably, the step S3 specifically includes: and decomposing the two-stage robust optimization configuration model into a main problem and a sub problem by adopting a column and constraint method C & CG, and obtaining a micro-grid optimization configuration result through CPLEX Solver iteration solution.
Compared with the prior art, the invention has the following advantages:
1) The hydrogen-containing energy storage micro-grid optimal configuration model constructed by the invention can improve the configuration capacity of a renewable energy source and reduce the carbon emission of the micro-grid by introducing a carbon transaction mechanism;
2) The configuration model for introducing the ladder carbon transaction has better carbon control effect than the configuration model for introducing the common carbon transaction;
3) The fluctuation of the load is considered in the two-stage robust optimal configuration model building process of the micro-grid, the optimal configuration of the micro-grid can be considered under the least unfavorable and running conditions of the micro-grid, and the reliability of the micro-grid can be better ensured as a configuration result.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a block diagram of a micro-grid;
FIG. 3 is annual weather data of the location of the micro grid; fig. 3a is a schematic diagram of a change of wind speed with time, and fig. 3b is a schematic diagram of a change of illumination intensity with time.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
Examples
As shown in fig. 1, the present embodiment provides a two-stage robust optimization configuration method for a micro-grid based on ladder carbon transaction, which includes the following steps:
step S1, constructing a micro-grid model comprising a fan, a photovoltaic cell, a diesel generator and a hydrogen energy storage device based on an output model of a distributed power supply;
s2, constructing a two-stage robust optimal configuration model, wherein the first stage takes the minimum annual value comprehensive cost of the micro-grid as an optimal target, and the second stage introduces a step carbon transaction mechanism and takes the minimum sum of annual operation cost and annual carbon transaction cost of the micro-grid as the optimal target; the fluctuation range of the load power is described by adopting a base uncertainty set U;
and S3, solving the two-stage robust optimal configuration model by adopting a column and constraint method C & CG to obtain a microgrid optimal configuration result.
Next, the method of the present invention will be described in detail.
1. Establishing a micro-grid model
1.1 blower output model
Fan output power P wt The expression with wind speed v is as follows:
Figure BDA0003969394630000071
wherein P is WT The output power of the fan, v is the wind speed, v F The wind speed is cut out for the fan; v C Cutting in wind speed for the fan; v R The rated wind speed of the fan is m/s; p (P) WT,N Rated power of the wind turbine generator system is kW.
1.2 photovoltaic cell output model
For the convenience of calculation and modeling, the generated power of the photovoltaic cell is considered to be only related to the ambient temperature and the illumination radiation intensity, and the expression is as follows:
P PV ＝P STC G[1+k PV (T c -25)]/1000 (2)
wherein: k (k) pv Power temperature coefficient,%/K, is-0.4; g is irradiance, W/m 2 ；P pv Is photovoltaic output, kW; p (P) STC Maximum test power, kW, under standard test conditions; t (T) c Is the operating temperature of the photovoltaic cell.
1.3 Diesel generator output model
Diesel fuel consumption can be described by the following expression:
f DE ＝αP DE +βP DE,rated (3)
wherein: f (f) DE The fuel consumption of the diesel generator per unit time is L/h; p (P) DE 、P DE,rated The output power and the nominal power of the diesel engine are respectively kW; α and β are slope and intercept coefficients in the fuel consumption-power curve, L/(kWh), and α= 0.08415 and β= 0.2461 are chosen in this example.
1.4 Hydrogen energy storage device output model
Unlike conventional energy storage systems, the present embodiment employs a hydrogen production-storage-generation system to cope with randomness and uncertainty of wind power and photovoltaic cell output power in the microgrid. The energy conversion and power generation process in the hydrogen production process can be represented by the following expression:
W ec ＝P ec η ec λ (4)
wherein: w (W) ec The amount of hydrogen produced for the electrolyzer; p (P) ec The electrolysis power of the electrolytic tank; η (eta) ec Is the electrolysis efficiency of the electrolytic cell; lambda is the amount of hydrogen produced by electrowinning per degree Nm 3 /kWh。
Figure BDA0003969394630000072
Wherein: p (P) fc Output electric power of the fuel cell; w (W) fc The amount of hydrogen consumed for the fuel cell; η (eta) fc Is the efficiency of the fuel cell; mu is the amount of hydrogen required per degree of electricity produced, nm 3 /kWh。
Figure BDA0003969394630000073
Wherein: e (E) hst,t And E is hst,t+1 The hydrogen storage amount is respectively the hydrogen storage amount at the time t and the time t+1 of the hydrogen storage tank;
Figure BDA0003969394630000074
and->
Figure BDA0003969394630000075
The efficiency of hydrogen storage and hydrogen release of the hydrogen storage tank respectively; />
Figure BDA0003969394630000081
And->
Figure BDA0003969394630000082
The hydrogen storage amount and the hydrogen release amount are respectively.
2. Design method
A trading mechanism for realizing carbon emission reduction by buying and selling carbon emission quota is considered in the process of optimizing configuration of the micro-grid. The step carbon trade divides the carbon emission into a plurality of intervals, and the more the carbon emission is, the higher the carbon trade price and the greater the carbon trade cost. The concrete calculation model of the ladder carbon transaction cost is as follows:
Figure BDA0003969394630000083
Figure BDA0003969394630000084
in the method, in the process of the invention,
Figure BDA0003969394630000085
for the step carbon trade cost, the positive value indicates that the system needs to purchase carbon emission rights, and the negative value indicates that the system sells the carbon emission rights to gain benefits; />
Figure BDA0003969394630000086
A trade price for carbon; e (E) G Carbon emissions for the system; l is the carbon emission interval length; lambda is the price increase amplitude of the carbon trade; beta de Carbon emission intensity beta of unit electric quantity of diesel generator grid,gf The carbon emission intensity of thermal power of outsourcing unit electric quantity; p (P) de Power per unit electric quantity of diesel generator, P grid,buy Maximum power for the micro grid to purchase electricity from the large grid.
In order to closely relate planning and operation, a two-stage robust optimal configuration model is constructed which accounts for micro-grid planning and operation. The double-layer optimization model constructed in the method comprises two optimization tasks, wherein the upper-layer optimization is capacity configuration optimization, and aims at minimizing annual comprehensive cost of a micro-grid and the like; the lower layer is optimized for system operation, and aims at minimizing the sum of annual operation cost and annual carbon transaction cost of the micro-grid. And transmitting the capacity configuration result of the upper layer to the lower layer, and transmitting the solved optimal operation result to the upper layer according to the known equipment capacity by the lower layer, and iterating the upper layer and the lower layer to obtain the optimal configuration result. The equivalent model is shown in the following formula:
Figure BDA0003969394630000087
wherein C is int Is an objective function of the first stage; n is a decision variable of the first stage, including the capacity of each device of the micro-grid; u is an uncertain variable comprising wind-light output and load power of the micro-grid, wherein the fluctuation range of the load power is described by adopting a base uncertainty set U; c (C) ope The method is characterized in that the method is an objective function of the second stage, x and y are decision variables of the second stage, and x is a 0/1 state variable of each output device; y is the time sequence output of each output device.
The first-stage optimization model in the two-stage robust optimization configuration model is specifically:
objective function:
Figure BDA0003969394630000091
wherein r is WT 、r PV 、r G 、r H The years of discount of a blower, a photovoltaic cell, a diesel engine and a hydrogen energy storage device respectively; c WT.int 、c PV.int 、c G.int 、c i.int The unit investment cost of the blower, the photovoltaic cell, the diesel engine and the hydrogen energy storage device are respectively; ρ is the discount rate; p (P) PV.max 、P WT.max 、P G.max And E is i.max The unit capacities of the photovoltaic device, the fan, the diesel engine and the hydrogen energy storage device are respectively; n (N) WT 、N PV 、N DE 、N i The configuration quantity of the fans, the photovoltaic power generation device, the diesel engine and the hydrogen energy storage device is a decision variable in the first stage; n represents a hydrogen energy storage device, comprising an electrolytic cell, a fuel cell and a hydrogen storage tank;
the constraint condition is number constraint, and the expression is:
Figure BDA0003969394630000092
wherein N is s,WT 、N s,PV 、N s,DE 、N s,FC 、N s,EC 、N s,HST The number of fans, photovoltaic panels, diesel generators, fuel cells, electrolytic tanks and hydrogen storage tanks installed for each scene respectively;
Figure BDA0003969394630000093
Figure BDA0003969394630000094
the maximum number of fans, photovoltaic panels, diesel generators, fuel cells, electrolyzer and hydrogen storage tanks allowed to be installed for each scenario, respectively.
Describing the fluctuation range of the load power in the constructed radix uncertainty set U:
Figure BDA0003969394630000095
wherein: u (u) L l (t) and u L u (t) is the lower and upper limits, respectively, of load fluctuation; deltau L max (t) is the fluctuation deviation of the maximum load power; Γ -shaped structure t Is an uncertainty parameter for the period t.
The fluctuation of the load is considered in the two-stage robust optimal configuration model building process of the micro-grid, the optimal configuration of the micro-grid can be considered under the least unfavorable and running conditions of the micro-grid, and the reliability of the micro-grid can be better ensured as a configuration result.
The second-stage optimization model in the two-stage robust optimization configuration model is specifically:
objective function:
Figure BDA0003969394630000096
in the method, in the process of the invention,
Figure BDA0003969394630000097
respectively M total operating costs of the devices, < >>
Figure BDA0003969394630000098
The step carbon transaction cost is determined according to a step carbon transaction mechanism;
the constraint conditions comprise power balance constraint, electrolyzer power constraint, fuel cell power constraint, hydrogen storage tank constraint, distributed power supply output constraint and power grid interaction constraint, which are respectively as follows:
1) Power balance constraint:
P WT (t)+P PV (t)+P H (t)+P DE (t)+P grid (t)＝P L (t)
wherein P is WT 、P PV 、P H 、P DE 、P grid 、P L The output power of the fan, the output power of the photovoltaic cell, the power supply power of the hydrogen energy storage device, the output power of the diesel engine, the power supply power of the micro-grid and the total output power are respectively;
2) Cell power constraint:
Figure BDA0003969394630000101
wherein P is EC 、
Figure BDA0003969394630000102
Respectively the electrolysis power and the maximum electrolysis power of the electrolytic tank;
3) Fuel cell power constraint:
Figure BDA0003969394630000103
wherein: p (P) FC 、
Figure BDA0003969394630000104
Respectively the output power and the maximum output of the fuel cellOutputting power;
4) Hydrogen storage tank restraint:
S ht,min ≤S ht (t)≤S ht,max
S ht (t 0 )＝S ht (t N )
wherein S is ht (t) is the capacity state of the hydrogen storage tank at the time t, S ht,min ≤S ht (t)≤S ht,max The hydrogen storage tank capacity minimum value and the hydrogen storage tank capacity maximum value are respectively; s is S ht (t 0 ) And S is ht (t N ) Respectively the beginning t of the scheduling period 0 Last t N A capacity state of the hydrogen storage tank;
5) Distributed power supply output constraint:
the inequality constraint of the generated power of a fan, a photovoltaic generator and a diesel generator:
Figure BDA0003969394630000105
wherein P is pv 、P wt 、P de The power generation power of the fan, the photovoltaic and the diesel generator respectively, P pv,N 、P wt,N 、P de,N The maximum power of the fan, the photovoltaic and the diesel generator are respectively;
6) Grid interaction constraint:
-P grid,sell ≤P grid ≤P grid,buy
wherein P is grid,sell Maximum power sum P for selling electricity to large grid for micro grid grid,buy Maximum power for the micro grid to purchase electricity from the large grid.
The model decomposes the two-stage robust optimization configuration model into a main problem and a sub problem according to the adoption of a column and constraint method C & CG, and obtains a micro-grid optimization configuration result through CPLEX Solver iteration solution.
3. Calculation case analysis
As shown in fig. 2, the micro-grid includes a blower, a photovoltaic cell, a diesel generator, and a hydrogen storage device. In this embodiment, a certain place is selected as a research object, and weather data of the place with an hour as a time scale is used for performing optimal configuration of the wind, light, hydrogen and diesel micro-grid, as shown in fig. 3.
The energy supply equipment of the micro-grid comprises a fan, a photovoltaic cell, a diesel generator and a hydrogen energy storage device, wherein the hydrogen energy storage device comprises an electrolytic tank, a hydrogen storage tank and a fuel cell. Other power supply related parameter specifications are shown in table 1.
TABLE 1
Figure BDA0003969394630000111
Protocol comparative analysis
The following analyzes the optimized configuration results of different schemes.
TABLE 2
Scheme for the production of a semiconductor device 1 2 3
Photovoltaic/stand 242 337 278
Blower/table 38 50 48
Diesel engine/bench 2 1 2
Fuel cell/each 37 32 47
Hydrogen storage tanks/tanks 2 2 2
Electrolytic cell/cell 26 32 28
Annual investment cost/yuan 1477649 1684268 1558649
Annual carbon emissions/kg 1189165 855840 989165
Scheme 1 is an optimal configuration without considering carbon transactions; scheme 2 is an optimized configuration that takes into account conventional carbon transactions; scheme 3, consider the optimal configuration of the ladder carbon transaction. Therefore, the influence of carbon transaction on the optimal configuration of the micro-grid is considered, so that the power generation cost of purchasing power from the grid and generating power by a diesel engine is increased, and the construction capacity of the wind turbine generator set and the photovoltaic unit is increased.
4.3 influence of carbon trade reference price on optimal configuration of micro-grid
The microgrid planning results at different carbon trade reference prices are shown in table 3.
TABLE 3 Table 3
Reference price/(Yuan/kg) 0.268 0.536 0.804
Photovoltaic/stand 242 263 278
Blower/table 38 42 48
Diesel engine/bench 2 2 2
Fuel cell/each 37 53 64
Hydrogen storage tanks/tanks 2 2 2
Electrolytic cell/cell 26 29 28
Annual investment cost/yuan 1477649 1675262 1858659
Annual carbon emissions/kg 1189165 845840 802524
As can be seen from the table, the reference price of carbon transaction is increased, and the construction capacity of the wind-solar generator set is reduced. When the reference price of carbon transaction is increased, the carbon emission cost for maintaining the running of the micro-grid is increased, and the optimal configuration scheme is used for improving the economy of the system, so that the capacity configuration of the wind-solar unit is increased, the new energy generating capacity is increased, and the total carbon emission amount of the system is reduced.
While the invention has been described with reference to certain preferred embodiments, it will be understood by those skilled in the art that various changes and substitutions of equivalents may be made and equivalents will be apparent to those skilled in the art without departing from the scope of the invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.

Claims (10)

1. A micro-grid two-stage robust optimal configuration method based on ladder carbon transaction is characterized by comprising the following steps:
step S1, constructing a micro-grid model comprising a fan, a photovoltaic cell, a diesel generator and a hydrogen energy storage device based on an output model of a distributed power supply;
s2, constructing a two-stage robust optimal configuration model, wherein the first stage takes the minimum annual value comprehensive cost of the micro-grid as an optimal target, and the second stage introduces a step carbon transaction mechanism and takes the minimum sum of annual operation cost and annual carbon transaction cost of the micro-grid as the optimal target; the fluctuation range of the load power is described by adopting a base uncertainty set U;
and S3, solving the two-stage robust optimal configuration model by adopting a column and constraint method C & CG to obtain a microgrid optimal configuration result.
2. The two-stage robust optimization configuration method of the micro-grid based on the ladder carbon transaction according to claim 1, wherein the hydrogen energy storage device in the step S1 is used for coping with randomness and uncertainty of the output of a fan and a photovoltaic cell in the micro-grid by adopting a hydrogen production-hydrogen storage-power generation system; the energy conversion in the hydrogen production process and the mathematical expression corresponding to the power generation process are as follows:
W ec ＝P ec η ec λ
in which W is ec The amount of hydrogen produced for the electrolyzer; p (P) ec The electrolysis power of the electrolytic tank; η (eta) ec Is the electrolysis efficiency of the electrolytic cell; lambda is the amount of hydrogen generated by electroelectrolysis per degree;
Figure FDA0003969394620000011
wherein P is fc Output electric power of the fuel cell; w (W) fc The amount of hydrogen consumed for the fuel cell; η (eta) fc Is the efficiency of the fuel cell; mu is the amount of hydrogen needed to produce electricity per degree; subscript t is time;
Figure FDA0003969394620000012
wherein E is hst,t And E is hst,t+1 The hydrogen storage amount is respectively the hydrogen storage amount at the time t and the time t+1 of the hydrogen storage tank;
Figure FDA0003969394620000013
and->
Figure FDA0003969394620000014
The efficiency of hydrogen storage and hydrogen release of the hydrogen storage tank respectively; />
Figure FDA0003969394620000015
And->
Figure FDA0003969394620000016
The hydrogen storage amount and the hydrogen release amount are respectively.
3. The two-stage robust optimization configuration method of the micro-grid based on the ladder carbon transaction according to claim 2, wherein the output model of the fan in the step S1 is as follows:
Figure FDA0003969394620000017
wherein P is WT The output power of the fan, v is the wind speed, v F The wind speed is cut out for the fan; v C Cutting in wind speed for the fan; v R The rated wind speed of the fan is set; p (P) WT,N Rated power of the wind turbine generator;
the output mathematical model of the photovoltaic cell is as follows:
P PV ＝P STC G[1+k PV (T c -25)]/1000
wherein P is PV Is photovoltaic output; k (k) PV A power temperature coefficient; g is irradiance; p (P) STC Maximum test power under standard test conditions; t (T) c The working temperature of the photovoltaic cell;
the output model of the diesel generator is as follows:
f DE ＝αP DE +βP DE,rated
wherein f DE The fuel consumption of the diesel generator in unit time; p (P) DE 、P DE,rated The output power and the nominal power of the diesel engine are respectively; alpha and beta are slope and intercept coefficients in the fuel consumption-power curve.
4. The two-stage robust optimization configuration method for the micro-grid based on the ladder carbon transaction according to claim 3, wherein the two-stage robust optimization configuration model in the step S2 is specifically: the first-stage optimization is capacity configuration optimization, and aims at minimizing annual comprehensive cost of a micro-grid and the like; the second stage of optimization is system operation optimization, and aims at minimizing the sum of annual operation cost and annual carbon transaction cost of the micro-grid; transmitting the capacity configuration result obtained by optimizing the first stage to the second stage, transmitting the solved optimal operation result to the first stage according to the known equipment capacity by the second stage, and iterating to obtain the optimal configuration result;
the equivalent model mathematical expression of the two-stage robust optimal configuration model is as follows:
Figure FDA0003969394620000021
wherein C is int Is an objective function of the first stage; n is a decision variable of the first stage, including the capacity of each device of the micro-grid; u is an uncertain variable comprising wind-light output and load power of the micro-grid, wherein the fluctuation range of the load power is described by adopting a base uncertainty set U; c (C) ope The method is characterized in that the method is an objective function of the second stage, x and y are decision variables of the second stage, and x is a 0/1 state variable of each output device; y is the time sequence output of each output device.
5. The two-stage robust optimization configuration method for the micro-grid based on the ladder carbon transaction according to claim 4, wherein a first stage optimization model of the two-stage robust optimization configuration model is specifically:
objective function:
Figure FDA0003969394620000022
wherein r is WT 、r PV 、r G 、r H The years of discount of a blower, a photovoltaic cell, a diesel engine and a hydrogen energy storage device respectively; c WT.int 、c PV.int 、c G.int 、c i.int The unit investment cost of the blower, the photovoltaic cell, the diesel engine and the hydrogen energy storage device are respectively; ρ is the discount rate; p (P) PV.max 、P WT.max 、P G.max And E is i.max The unit capacities of the photovoltaic device, the fan, the diesel engine and the hydrogen energy storage device are respectively; n (N) WT 、N PV 、N DE 、N i The configuration quantity of the fans, the photovoltaic power generation device, the diesel engine and the hydrogen energy storage device is a decision variable in the first stage; n represents a hydrogen energy storage device, comprising an electrolytic cell, a fuel cell and a hydrogen storage tank;
the constraint condition is number constraint, and the expression is:
Figure FDA0003969394620000031
wherein N is s,WT 、N s,PV 、N s,DE 、N s,FC 、N s,EC 、N s,HST The number of fans, photovoltaic panels, diesel generators, fuel cells, electrolytic tanks and hydrogen storage tanks installed for each scene respectively;
Figure FDA0003969394620000032
Figure FDA0003969394620000033
the maximum number of fans, photovoltaic panels, diesel generators, fuel cells, electrolyzer and hydrogen storage tanks allowed to be installed for each scenario, respectively.
6. The two-stage robust optimization configuration method for the micro-grid based on the ladder carbon transaction according to claim 4, wherein the second-stage optimization model in the two-stage robust optimization configuration model is specifically:
objective function:
Figure FDA0003969394620000034
in the method, in the process of the invention,
Figure FDA0003969394620000035
respectively M total operating costs of the devices, < >>
Figure FDA0003969394620000036
The step carbon transaction cost is determined according to a step carbon transaction mechanism;
the constraint conditions include power balance constraint, electrolyzer power constraint, fuel cell power constraint, hydrogen storage tank constraint, distributed power supply output constraint and grid interaction constraint.
7. The two-stage robust optimization configuration method for the micro-grid based on the ladder carbon transaction according to claim 6, wherein the ladder carbon transaction mechanism is as follows: dividing the carbon emission into a plurality of sections, wherein the more the carbon emission is, the higher the carbon trade price is, the greater the carbon trade cost is, and the corresponding stepped carbon trade cost is
Figure FDA0003969394620000038
The expression is:
Figure FDA0003969394620000037
Figure FDA0003969394620000041
in the method, in the process of the invention,
Figure FDA0003969394620000042
for the step carbon trade cost, the positive value indicates that the system needs to purchase carbon emission rights, and the negative value indicates that the system sells the carbon emission rights to gain benefits; />
Figure FDA0003969394620000043
A trade price for carbon; e (E) G Carbon emissions for the system; l is the carbon emission interval length; lambda is the price increase amplitude of the carbon trade; beta de Carbon emission intensity beta of unit electric quantity of diesel generator grid,gf The carbon emission intensity of thermal power of outsourcing unit electric quantity; p (P) de Power per unit electric quantity of diesel generator, P grid,buy Maximum power for the micro grid to purchase electricity from the large grid.
8. The two-stage robust optimization configuration method of the micro-grid based on the ladder carbon transaction according to claim 6, wherein the constraint conditions comprise power balance constraint, electrolyzer power constraint, fuel cell power constraint, hydrogen storage tank constraint, distributed power supply output constraint and grid interaction constraint, and the specific expression is as follows:
1) Power balance constraint:
P WT (t)+P PV (t)+P H (t)+P DE (t)+P grid (t)＝P L (t)
wherein P is WT 、P PV 、P H 、P DE 、P grid 、P L The output power of the fan, the output power of the photovoltaic cell, the power supply power of the hydrogen energy storage device, the output power of the diesel engine, the power supply power of the micro-grid and the total output power are respectively;
2) Cell power constraint:
Figure FDA0003969394620000044
wherein P is EC 、
Figure FDA0003969394620000045
Respectively the electrolysis power and the maximum electrolysis power of the electrolytic tank;
3) Fuel cell power constraint:
Figure FDA0003969394620000046
wherein: p (P) FC 、
Figure FDA0003969394620000047
The output power and the maximum output power of the fuel cell respectively;
4) Hydrogen storage tank restraint:
S ht,min ≤S ht (t)≤S ht,max
S ht (t 0 )＝S ht (t N )
wherein S is ht (t) is the capacity state of the hydrogen storage tank at the time t, S ht,min ≤S ht (t)≤S ht,max The hydrogen storage tank capacity minimum value and the hydrogen storage tank capacity maximum value are respectively; s is S ht (t 0 ) And S is ht (t N ) Respectively the beginning t of the scheduling period 0 Last t N A capacity state of the hydrogen storage tank;
5) Distributed power supply output constraint:
the inequality constraint of the generated power of a fan, a photovoltaic generator and a diesel generator:
Figure FDA0003969394620000048
wherein P is pv 、P wt 、P de The power generation power of the fan, the photovoltaic and the diesel generator respectively, P pv,N 、P wt,N 、P de,N Respectively is a fan and lightMaximum power generated by the volt and diesel generator;
6) Grid interaction constraint:
-P grid,sell ≤P grid ≤P grid,buy
wherein P is grid,sell Maximum power sum P for selling electricity to large grid for micro grid grid,buy Maximum power for the micro grid to purchase electricity from the large grid.
9. The two-stage robust optimization configuration method of the micro-grid based on the ladder carbon transaction according to claim 1, wherein the fluctuation range of the load power in the step S2 is described by a base uncertainty set U, and the specific expression is:
Figure FDA0003969394620000051
wherein u is L l (t) and u L u (t) load fluctuation u respectively L Lower and upper limits of (t); deltau L max (t) is the fluctuation deviation of the maximum load power; Γ -shaped structure t Is an uncertainty parameter for the period t.
10. The two-stage robust optimization configuration method for the micro-grid based on the ladder carbon transaction according to claim 1, wherein the step S3 is specifically: and decomposing the two-stage robust optimization configuration model into a main problem and a sub problem by adopting a column and constraint method C & CG, and obtaining a micro-grid optimization configuration result through CPLEX Solver iteration solution.
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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116436099A (en) * 2023-06-12 2023-07-14 北京和瑞储能科技有限公司 Micro-grid robust optimal scheduling method and system
CN116780535A (en) * 2023-08-16 2023-09-19 国网浙江省电力有限公司金华供电公司 Light-storage collaborative optimization scheduling method based on ladder-type carbon transaction mechanism
CN117559563A (en) * 2023-11-23 2024-02-13 国网湖北省电力有限公司经济技术研究院 Optimization method and system for wind-solar energy storage-charging integrated micro-grid operation scheme

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116436099A (en) * 2023-06-12 2023-07-14 北京和瑞储能科技有限公司 Micro-grid robust optimal scheduling method and system
CN116436099B (en) * 2023-06-12 2023-11-10 北京和瑞储能科技有限公司 Micro-grid robust optimal scheduling method and system
CN116780535A (en) * 2023-08-16 2023-09-19 国网浙江省电力有限公司金华供电公司 Light-storage collaborative optimization scheduling method based on ladder-type carbon transaction mechanism
CN116780535B (en) * 2023-08-16 2024-01-02 国网浙江省电力有限公司金华供电公司 Light-storage collaborative optimization scheduling method based on ladder-type carbon transaction mechanism
CN117559563A (en) * 2023-11-23 2024-02-13 国网湖北省电力有限公司经济技术研究院 Optimization method and system for wind-solar energy storage-charging integrated micro-grid operation scheme
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