CN114744684A - Novel low-carbon economic regulation and control method for power system - Google Patents

Novel low-carbon economic regulation and control method for power system Download PDF

Info

Publication number
CN114744684A
CN114744684A CN202210433592.0A CN202210433592A CN114744684A CN 114744684 A CN114744684 A CN 114744684A CN 202210433592 A CN202210433592 A CN 202210433592A CN 114744684 A CN114744684 A CN 114744684A
Authority
CN
China
Prior art keywords
power
constraint
energy storage
carbon
generating unit
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202210433592.0A
Other languages
Chinese (zh)
Inventor
朱轶伦
吴华华
谢宏福
娄冰
张俊
吴侃侃
谷炜
郑翔
张静
张思
沈绍斐
蒙志全
张辰
蒋正邦
楼贤嗣
蒋轶澄
余雅琴
盛华挺
齐肇江
陈冰恽
鹿奇
陈龙
徐锦根
赵晓英
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Nanjing Dongbo Intelligent Energy Research Institute Co ltd
State Grid Zhejiang Electric Power Co Ltd
Zhejiang Huayun Information Technology Co Ltd
Original Assignee
Nanjing Dongbo Intelligent Energy Research Institute Co ltd
State Grid Zhejiang Electric Power Co Ltd
Zhejiang Huayun Information Technology Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Nanjing Dongbo Intelligent Energy Research Institute Co ltd, State Grid Zhejiang Electric Power Co Ltd, Zhejiang Huayun Information Technology Co Ltd filed Critical Nanjing Dongbo Intelligent Energy Research Institute Co ltd
Priority to CN202210433592.0A priority Critical patent/CN114744684A/en
Publication of CN114744684A publication Critical patent/CN114744684A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • 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
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/007—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources
    • H02J3/0075—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources for providing alternative feeding paths between load and source according to economic or energy efficiency considerations, e.g. economic dispatch
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • 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
    • H02J3/48—Controlling the sharing of the in-phase component
    • 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
    • H02J3/50—Controlling the sharing of the out-of-phase component
    • 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
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • 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]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
    • Y04S20/20—End-user application control systems
    • Y04S20/222—Demand response systems, e.g. load shedding, peak shaving

Landscapes

  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

The invention discloses a novel low-carbon economic regulation and control method for a power system, which aims to realize low-carbon economic operation of the novel power system in an environment. The method comprises the steps of (1) establishing a power grid operation cost objective function value F1Low carbon economic objective function value F of carbon emission2(ii) a Determining constraint conditions of a low-carbon economic regulation and control model of a power grid; and (3) solving the multi-target regulation and control model by using a multi-target solving algorithm IMQGA. On the premise of ensuring safe and stable operation of the power grid, the invention maximizes the power generation capacity of new energy such as wind and light and minimizes the operation cost and equivalent carbon of the power gridAnd (4) discharging, and providing theoretical support for realizing a low-carbon power grid and a clean power grid.

Description

Novel low-carbon economic regulation and control method for power system
Technical Field
The invention relates to the field of power system regulation, in particular to a novel low-carbon economic regulation and control method for a power system.
Background
The power grid is changed from a novel power system which takes thermal power as a main body to a new energy source as a main body. In a novel power system environment, on a power generation side, new energy represented by wind power and photovoltaic becomes a main power supply, and the high volatility and uncertainty of the wind power and the photovoltaic impact the operation of a power grid; on the load side, the access proportion of the controllable air conditioner and the electric automobile is increased gradually, and the high-proportion random load also impacts the power grid to operate. The traditional thermoelectricity and hydropower operation mode as the main power of power supply changes, and together with distributed energy storage, more undertakes the electric power balance task of the power grid, and the operation mode changes greatly, and the start and stop are more frequent, so that the traditional regulation and control thought needs to be changed urgently, and the safe and stable operation of a novel power system is guaranteed.
In the method for regulating and controlling the power grid, most of the existing regulating and controlling methods related to new energy are used for regulating and controlling the power grid in the traditional angle taking thermal power as a main body, and relevant researches are carried out on the one hand that the power grid is regulated and controlled in the angle taking thermal power as the main body and the requirement for regulating and controlling the power grid taking new energy as the main body is difficult to adapt; on the other hand, the dispatching among the power supply main bodies is relatively independent, the main purpose is to take pairwise combination or combination dispatching among three power supplies, various power supplies of a power grid are not sufficiently combined for dispatching, the dispatching potential of each power supply main body is not fully exerted, and the factors considered in the low-carbon economic dispatching method are not comprehensive enough. Therefore, the principle of economy and low carbon is considered, and the practical significance and the engineering practical value are important for realizing intelligent regulation and control under the environment of the novel power system.
Disclosure of Invention
In order to solve the problems, the novel low-carbon economic regulation and control method for the power system is provided, the novel power system is used as a main body, comprehensive regulation and control are carried out by combining wind, light, water and fire storage, the regulation and control potentials of respective power sources are fully exerted, the start-stop cost generated by the fact that thermal power, water and electricity and energy storage participate in electric quantity balance is fully considered in the regulation and control process, and the operation cost of the novel power system is comprehensively represented. And constructing an LSTM-based thermal power generating unit carbon emission model to realize accurate measurement of carbon emission of different thermal power generating units. The method comprises the steps of establishing a power grid multi-target regulation and control model under the novel power system environment, solving the regulation and control model based on IMQGA, maximizing the power generation capacity of new energy such as wind and light, minimizing the operation cost of the power grid and equivalent carbon emission on the premise of guaranteeing safe and stable operation of the power grid, and achieving low-carbon economic operation under the novel power system environment.
A novel low-carbon economic regulation and control method for a power system comprises the following steps:
step (1) of establishing a power grid operation cost objective function value F1Low carbon economic objective function value F of sum carbon emission2；
Determining constraint conditions of a low-carbon economic regulation and control model of a power grid;
step (3) utilizing a multi-objective solving algorithm IMQGA to carry out objective function value F on the operation cost of the power grid1Low carbon economic objective function value of carbon emissionF2And solving the multi-target regulation and control model.
Before the step (1), a novel low-carbon economic regulation and control framework of the power system is constructed, and the specific method comprises the following steps:
under the principle of maximizing the wind and light new energy power generation output, the influence of a multi-energy scheduling scheme on the economy and low carbon of a power grid is analyzed, and a novel low carbon economy regulation and control framework of a power system is constructed.
In the step (1), a power grid operation cost objective function value F is constructed by taking the minimum power grid operation cost as a target1Constructing a low-carbon economic objective function value F of carbon emission by taking the lowest carbon emission of a power grid as a target2，
1) Establishing a grid operating cost objective function value F1：
Figure RE-GDA0003691848960000021
Wherein, FGjFor the running cost F of the thermal power generating unithjFor the running cost of hydro-power generating units, FCjFor the operating cost of the energy storage unit, FlossjFor line loss, NG、Nh、NC、NlossThe number of thermal power generator sets, the number of hydroelectric generator sets, the number of energy storage generator sets and the total number of power grid lines are respectively,
operating cost F of thermal power generating unitGjThe calculation model is shown in formula (2):
Figure RE-GDA0003691848960000022
in the formula, aGj、bGj、cGjIs the consumption characteristic parameter of the thermal power generating unit Gj,
Figure RE-GDA0003691848960000023
the variable is 0-1, the operation state of the thermal power generating unit Gj at the moment t is represented, the thermal power generating unit is in a power generation state when the value is 1, the thermal power generating unit is in a stop operation state when the value is 0,
Figure RE-GDA0003691848960000024
the output rho of the thermal power generating unit j at the moment tGjThe cost for starting and stopping the thermal power generating unit Gj once;
running cost F of hydro-power generating unithjThe calculation model is shown in formula (3):
Figure RE-GDA0003691848960000025
wherein,
Figure RE-GDA0003691848960000026
the variable is 0-1, the variable represents the running state of the hydroelectric generating set hj at the time t, the variable represents that the hydroelectric generating set is in a power generation state when the value is 1, the variable represents that the hydroelectric generating set is in a stop running state when the value is 0, and rhohjThe cost for starting and stopping the hydroelectric generating set hj once;
energy storage unit operating cost FCjThe calculation model is shown in formula (4):
Figure RE-GDA0003691848960000027
wherein,
Figure RE-GDA0003691848960000028
for the cost coefficient of the charging and discharging power of the energy storage unit Cj,
Figure RE-GDA0003691848960000029
charging power for the jth energy storage unit at the moment t,
Figure RE-GDA00036918489600000210
discharging power for the jth energy storage unit at the moment t;
line loss FlossjThe calculation model is shown in formula (5):
Figure RE-GDA0003691848960000031
in the formula
Figure RE-GDA0003691848960000032
Is the current value of line j at time t, Rj、XjResistance and reactance of the line respectively;
2) construction of a Low carbon economic Objective function F of carbon emissions2：
In a dispatching period, the carbon emission of the power grid is generated by a thermal power generating unit, and a low-carbon economic objective function F of the carbon emission is constructed2As shown in equation (6):
Figure RE-GDA0003691848960000033
carbon emission per unit power of thermal power generating unit j at time t
Figure RE-GDA0003691848960000034
The calculation model of (2) is shown in equation (7):
Figure RE-GDA0003691848960000035
wherein,
Figure RE-GDA0003691848960000036
the fuel carbon emission factor of the thermal power generating unit j at the time t is obtained;
Figure RE-GDA0003691848960000037
active power output of the thermal power generating unit j at the moment t;
Figure RE-GDA0003691848960000038
and the thermal power generating unit j outputs reactive power at the moment t.
In the step (2), the constraint conditions of the low-carbon economic regulation and control model of the power grid comprise active balance constraint, wind power generation constraint, photovoltaic power generation constraint, hydroelectric power generation constraint, thermal power unit constraint, energy storage constraint, line constraint and load loss constraint.
Active balance constraint, as shown in equation (8):
Figure RE-GDA0003691848960000039
wherein,
Figure RE-GDA00036918489600000310
charging power for the jth energy storage unit at the moment t,
Figure RE-GDA00036918489600000311
for the discharge power of the jth energy storage unit at the moment t,
Figure RE-GDA00036918489600000312
for the power at the time t of the jth load,
Figure RE-GDA00036918489600000313
the power is output for the jth wind power plant at the moment t,
Figure RE-GDA00036918489600000314
the power is output for the jth photovoltaic station at the moment t,
Figure RE-GDA00036918489600000315
the output is output for the jth hydropower station at the moment t,
Figure RE-GDA00036918489600000316
the output of the jth thermal power generating unit at the moment t is NCNumber of energy storage units in charged state, NDNumber of energy storage units N in discharge stateLIs the number of loads, NPWIs the number of wind power plants, NPVNumber of photovoltaic power stations, NhNumber of hydroelectric stations, NGThe number of thermal power generating units;
the wind power generation constraint comprises wind power climbing constraint and wind power wind abandoning constraint, and is shown as a formula (9):
Figure RE-GDA00036918489600000317
wherein,
Figure RE-GDA00036918489600000318
the wind power climbing rate is the wind power climbing rate,
Figure RE-GDA00036918489600000319
respectively the minimum and maximum climbing rates of the wind power, the delta t is the time interval between two wind power at the moment t,
Figure RE-GDA00036918489600000320
the wind power output at the time t is obtained,
Figure RE-GDA00036918489600000321
for the wind power abandoning power of the wind power at the time t,
Figure RE-GDA00036918489600000322
wind power at time t; according to wind power permeability P'PWCalculating wind power output so as to obtain the wind power climbing rate;
wind power permeability P'PWActual output of wind power absorption power in scheduling period is taken
Figure RE-GDA00036918489600000323
And the total wind power load P in the dispatching cycleLj，tAs shown in equation (10):
Figure RE-GDA0003691848960000041
the photovoltaic power generation constraints include photovoltaic operation constraints, photovoltaic climbing constraints, and light abandonment constraints, as shown in equation (11):
Figure RE-GDA0003691848960000042
wherein,
Figure RE-GDA0003691848960000043
the lower limit of the photovoltaic power generation power is,
Figure RE-GDA0003691848960000044
upper limit of photovoltaic Power Generation, P'PV，tIs photovoltaic power generation power at time t, P'PV，t-1The photovoltaic power generation power at the time t-1,
Figure RE-GDA0003691848960000045
respectively photovoltaic down and up ramp rate limit value, P'PVD，tAbandoning the optical power for the time t;
the hydroelectric power generation constraints comprise a hydraulic turbine unit output constraint and an available water quantity constraint, as shown in a formula (12):
Figure RE-GDA0003691848960000046
wherein,
Figure RE-GDA0003691848960000047
respectively the minimum and maximum output of the jth hydraulic generator,
Figure RE-GDA0003691848960000048
respectively the maximum and minimum water consumption of the hydropower station,
Figure RE-GDA0003691848960000049
the output force at the moment t of the jth hydraulic generator,
Figure RE-GDA00036918489600000410
the water consumption of the hydropower station at the moment t;
the power unit constraint comprises thermal power unit operation constraint and thermal power unit climbing constraint, and is shown in a formula (13):
Figure RE-GDA00036918489600000411
wherein,
Figure RE-GDA00036918489600000412
the lower limit of the output of the ith thermal power generating unit,
Figure RE-GDA00036918489600000413
is the output upper limit of the ith thermal power generating unit,
Figure RE-GDA00036918489600000414
the output of the ith thermal power generating unit,
Figure RE-GDA00036918489600000415
is the lower climbing rate limit value of the ith thermal power generating unit,
Figure RE-GDA00036918489600000416
the value is the uphill gradient rate limit value of the ith thermal power generating unit;
Figure RE-GDA00036918489600000417
the slope climbing rate of the ith thermal power generating unit at the t moment
The energy storage constraint comprises energy storage power constraint, energy storage chargeability constraint and energy storage capacity constraint; wherein the energy storage power constraint is as shown in equation (14):
Figure RE-GDA00036918489600000418
in the formula
Figure RE-GDA00036918489600000419
In order to store the maximum charging power,
Figure RE-GDA00036918489600000420
for storing maximum discharge power, PC，tFor storing charging power at time t, PD，tFor the stored energy discharge power at time t,
Figure RE-GDA00036918489600000421
for storing minimum charging power, negative values indicate discharge,
Figure RE-GDA00036918489600000422
the energy storage minimum discharge power is obtained, and a negative value represents charging;
the energy storage charge constraint is shown in equation (15):
Figure RE-GDA00036918489600000423
in the formula, SOCmax、SOCminTo store maximum and minimum chargeability, SOCtFor the storage of the charge rate at time t, SOC0To the initial chargeability of the energy storage system, EtIs the energy storage capacity;
energy storage capacity constraint, as shown in equation (16):
Figure RE-GDA0003691848960000051
wherein E isMax、EMinUpper and lower limits of energy storage capacity, eta1，η2Efficiency of energy storage discharge and charging respectively;
the line constraints include line power equality constraints, line transmit power constraints, and nodal phase angle constraints, as shown in equation (17):
Figure RE-GDA0003691848960000052
wherein, Pij，tTransmission power of the line with i, j as end points at time t, BijIs the susceptance, θ, of the linei，t、θj，tThe phase angle of the i and j nodes at the t time,
Figure RE-GDA0003691848960000053
is the line thermal stability limit;
the loss-of-load constraint means that the loss-of-load power of a node in the power system cannot be greater than the load power of the node, as shown in equation (18):
0≤PLDj，t≤PLj，t (18)
wherein, PLDj，tIs the power of the node lost load, PLj，tLoad power for the node.
In the step (3): solving a multi-target regulation and control model of the power grid in the novel power system environment by adopting a multi-target solving algorithm IMQGA (inertial measurement System), coordinating the power of a thermal power unit, a hydroelectric power unit and an energy storage unit, and enabling the operation cost of the power grid to be an objective function F1Low carbon economic objective function F for carbon emissions2An optimum state is reached.
The method for solving the multi-target regulation and control model of the power grid in the novel power system environment by adopting the multi-target solution algorithm IMQGA comprises the following steps:
1) multi-target regulation and control initial solution population for establishing initial power grid
Figure RE-GDA0003691848960000054
According to formulas (8) - (18), checking the initial population, and removing individuals which do not meet the constraint; calculating a power grid operation cost objective function F according to formulas (1) - (7)1Low carbon economic objective function of carbon emissions F2(ii) a If the individuals which do not meet the constraint condition are removed, the population quantity is supplemented according to the initialization rule again;
2) sorting the non-dominant solutions of the population according to a non-dominant sorting algorithm, and determining the grade i of the non-dominant solutionsrankCarrying out crowding degree calculation, and screening an initial population according to the lowest grade and the highest crowding degree;
3) performing rotation quantum gate, crossing and variation on the screened initial population to generate a progeny population;
4) according to the formulas (8) - (18), the generated population is checked, unqualified individuals are removed, the parent population and the offspring population are combined, and the power grid operation cost objective function F is calculated according to the formulas (1) and (6)1Low carbon economic objective function of carbon emission F2；
5) Calculating the non-dominance level and the crowding degree according to the step 2), selecting optimal individuals to form a new parent population, returning to the step 3) for iteration if the evolution algebra Gen does not reach the maximum evolution algebra max Gen, and outputting an optimal solution if the maximum evolution algebra is reached.
The power grid in the novel power system environment of the invention takes new energy as a main body, the randomness, the intermittence and the fluctuation of the output of the new energy bring impact to the safe and stable operation of the power grid, and each link of the power grid scheduling is greatly changed.
Drawings
FIG. 1 is a flow chart of a novel low-carbon economic regulation and control method for an electric power system;
FIG. 2 is a flow chart of an IMQGA multi-objective solution method.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
Referring to fig. 1, the novel low-carbon economic regulation and control method for the power system comprises the following steps (1) to (4):
step (1): the method comprises the steps of constructing a novel low-carbon economic regulation and control framework of the power system, specifically, analyzing the influence of a multi-energy scheduling scheme on the economy and low carbon of a power grid under the principle of maximizing the power generation output of new wind and light energy, and constructing the novel low-carbon economic regulation and control framework of the power system.
Step (2): construction of grid operation cost objective function value F1Low carbon economic objective function value F of sum carbon emission2；
Establishing a power grid operation cost objective function value F with the minimum power grid operation cost1Constructing a low-carbon economic objective function value F of carbon emission with the lowest carbon emission of a power grid2，
1) Establishing a power grid operation cost objective function value F for establishing the minimum power grid operation cost1：
Figure RE-GDA0003691848960000061
Wherein, FGjFor the running cost F of the thermal power generating unithjFor the running cost of hydro-power generating units, FCjFor the operating cost of the energy storage unit, FlossjFor line loss, NG、Nh、NC、NlossThe number of the thermal power generator sets, the number of the hydraulic power generator sets, the number of the energy storage generator sets and the total number of the power grid lines are respectively.
Operating cost F of thermal power generating unitGjThe calculation model is shown in formula (2):
Figure RE-GDA0003691848960000071
in the formula aGj、bGj、cGjIs the consumption characteristic parameter of the thermal power generating unit Gj,
Figure RE-GDA0003691848960000072
the variable is 0-1, the operation state of the thermal power generating unit Gj at the moment t is represented, the thermal power generating unit is in a power generation state when the value is 1, the thermal power generating unit is in a stop operation state when the value is 0,
Figure RE-GDA0003691848960000073
the output rho of the thermal power generating unit j at the moment tGjThe cost of starting and stopping the thermal power generating unit Gj once is saved.
Running cost F of hydroelectric generating sethjThe calculation model is shown in formula (3):
Figure RE-GDA0003691848960000074
wherein,
Figure RE-GDA0003691848960000075
the variable is 0-1, the variable represents the running state of the hydroelectric generating set hj at the time t, the variable represents that the hydroelectric generating set is in a power generation state when the value is 1, the variable represents that the hydroelectric generating set is in a stop running state when the value is 0, and rhohjThe cost for starting and stopping the hydro-power generating unit hj once is saved.
Energy storage unit operating cost FCjThe calculation model is shown in formula (4):
Figure RE-GDA0003691848960000076
wherein,
Figure RE-GDA0003691848960000077
for the cost coefficient of the charging and discharging power of the energy storage unit Cj,
Figure RE-GDA0003691848960000078
charging power for the jth energy storage unit at the moment t,
Figure RE-GDA0003691848960000079
and discharging power of the jth energy storage unit at the moment t.
Line loss FlossjThe calculation model is shown in formula (5):
Figure RE-GDA00036918489600000710
in the formula
Figure RE-GDA00036918489600000711
Is the current value of line j at time t, Rj、XjRespectively the resistance and reactance of the line.
2) Establishing a low-carbon economic objective function F of carbon emission for constructing the lowest carbon emission of a power grid2：
In a dispatching period, the carbon emission of the power grid is generated by a thermal power generating unit, and a low-carbon economic objective function F of the carbon emission is constructed2As shown in equation (6):
Figure RE-GDA00036918489600000712
carbon emission per unit power of thermal power generating unit j at time t
Figure RE-GDA00036918489600000713
The calculation model of (2) is shown in equation (7):
Figure RE-GDA00036918489600000714
wherein,
Figure RE-GDA00036918489600000715
and (4) the fuel carbon emission factor of the thermal power generating unit j at the time t.
And (3): and determining constraint conditions of the power grid low-carbon economic regulation and control model, specifically, the constraint conditions of the power grid low-carbon economic regulation and control model comprise active balance constraint, wind power generation constraint, photovoltaic power generation constraint, hydroelectric power generation constraint, thermal power unit constraint, energy storage constraint, line constraint and load loss constraint.
Active balance constraint, as shown in equation (8):
Figure RE-GDA0003691848960000081
wherein,
Figure RE-GDA0003691848960000082
charging power for the jth energy storage unit at the moment t,
Figure RE-GDA0003691848960000083
for the discharge power of the jth energy storage unit at the moment t,
Figure RE-GDA0003691848960000084
for the power at the time t of the jth load,
Figure RE-GDA0003691848960000085
the power is output for the jth wind power plant at the moment t,
Figure RE-GDA0003691848960000086
the power is output for the jth photovoltaic station at the moment t,
Figure RE-GDA0003691848960000087
the output is output for the jth hydropower station at the moment t,
Figure RE-GDA0003691848960000088
the output of the jth thermal power generating unit at the moment t is NCNumber of energy storage units in charged state, NDNumber of energy storage units N in discharge stateLIs the number of loads, NPWIs the number of wind power plants, NPVIs the number N of photovoltaic power stationshNumber of hydroelectric stations, NGThe number of thermal power units.
The wind power generation constraint comprises wind power climbing constraint and wind power wind abandoning constraint, and is shown in a formula (9):
Figure RE-GDA0003691848960000089
wherein,
Figure RE-GDA00036918489600000810
the wind power climbing rate is the wind power climbing rate,
Figure RE-GDA00036918489600000811
respectively the minimum and maximum climbing rates of the wind power, delta t is the time interval between two wind power at the moment t,
Figure RE-GDA00036918489600000812
the wind power output at the time t is obtained,
Figure RE-GDA00036918489600000813
for the wind power abandoning power of the wind power at the time t,
Figure RE-GDA00036918489600000814
the wind power at the moment t.
Permeability P of wind powerPWActual output of wind power absorption power in scheduling period is taken
Figure RE-GDA00036918489600000815
And the total wind power load P in the dispatching cycleLj，tAs shown in equation (10):
Figure RE-GDA00036918489600000816
the photovoltaic power generation constraints include photovoltaic operation constraints, photovoltaic climbing constraints, and light abandonment constraints, as shown in equation (11):
Figure RE-GDA00036918489600000817
wherein,
Figure RE-GDA00036918489600000818
the lower limit of the photovoltaic power generation power is,
Figure RE-GDA00036918489600000819
upper limit of photovoltaic Power Generation, P'PV，tIs photovoltaic power generation power at time t ', P'PV，t-1The photovoltaic power generation power at the time t-1,
Figure RE-GDA00036918489600000820
respectively photovoltaic down and up ramp rate limit value, P'PVD，tThe optical power is discarded for time t.
The hydro-power generation constraints include hydro-turbine output constraints and available water volume constraints, as shown in equation (12):
Figure RE-GDA00036918489600000821
wherein,
Figure RE-GDA00036918489600000822
respectively the minimum and maximum output of the jth hydraulic generator,
Figure RE-GDA00036918489600000823
respectively the maximum and minimum water consumption of the hydropower station,
Figure RE-GDA00036918489600000824
the output force at the moment t of the jth hydraulic generator,
Figure RE-GDA00036918489600000825
the water consumption of the hydropower station at the moment t.
The power unit constraint comprises thermal power unit operation constraint and thermal power unit climbing constraint, and is shown in a formula (13):
Figure RE-GDA0003691848960000091
wherein,
Figure RE-GDA0003691848960000092
is the output lower limit of the ith thermal power generating unit,
Figure RE-GDA0003691848960000093
is the output upper limit of the ith thermal power generating unit,
Figure RE-GDA0003691848960000094
the output of the ith thermal power generating unit,
Figure RE-GDA0003691848960000095
is the lower climbing rate limit value of the ith thermal power generating unit,
Figure RE-GDA0003691848960000096
the value is the uphill gradient rate limit value of the ith thermal power generating unit.
The energy storage constraint comprises energy storage power constraint, energy storage chargeability constraint and energy storage capacity constraint; wherein the energy storage power constraint is as shown in equation (14):
Figure RE-GDA0003691848960000097
in the formula
Figure RE-GDA0003691848960000098
In order to store the maximum charging power,
Figure RE-GDA0003691848960000099
to store maximum discharge power, PC，tFor storing charging power at time t, PD，tThe energy storage discharge power is stored for the time t,
Figure RE-GDA00036918489600000910
in order to realize the purpose,
Figure RE-GDA00036918489600000911
is as follows.
Setting energy storage charge constraint for avoiding energy storage overcharge and overdischarge, wherein the energy storage charge constraint is shown as a formula (15):
Figure RE-GDA00036918489600000912
in the formula, SOCmax、SOCminFor maximum and minimum charge rate of stored energy, SOCtFor storing energy charge rate at time t, SOC0To the initial chargeability of the energy storage system, EtIs the energy storage capacity.
Energy storage capacity constraint, as shown in equation (16):
Figure RE-GDA00036918489600000913
wherein E isMax、EMinAre respectively the upper and lower limits of the energy storage capacity, eta1，η2Are respectively provided withThe efficiency of energy storage discharging and charging is improved.
The line constraints include line power equality constraints, line transmit power constraints, and nodal phase angle constraints, as shown in equation (17):
Figure RE-GDA00036918489600000914
wherein, Pij，tTransmission power of the line with i, j as end points at time t, BijIs the susceptance, θ, of the linei，t、θj，tThe phase angles of the i and j nodes at the time t,
Figure RE-GDA00036918489600000915
is the thermal stability limit of the line.
The loss-of-load constraint means that the loss-of-load power of a node in the power system cannot be greater than the load power of the node, as shown in equation (18):
0≤PLDj，t≤PLj，t (18)
wherein, PLDj，tIs the power of the node lost load, PLj，tLoad power for the node.
And (4): solving the multi-target regulation and control model by using a multi-target solving algorithm IMQGA, solving the multi-target regulation and control model of the power grid under the novel power system environment by using the multi-target solving algorithm IMQGA, and coordinating the power of a thermal power unit, a hydroelectric power unit and an energy storage unit to enable the power grid operation cost to be an objective function F1Low carbon economic objective function F for carbon emissions2An optimum state is reached.
Referring to fig. 2, the solution is as follows:
1) establishing initial power grid multi-target regulation initial solution population
Figure RE-GDA0003691848960000101
According to formulas (8) - (18), checking the initial population, and removing individuals which do not meet the constraint; calculating a power grid operation cost objective function F according to the formulas (1) - (7)1Low carbon economic objective function of carbon emissions F2(ii) a If the individuals which do not meet the constraint condition are removed, the population quantity is supplemented according to the initialization rule again;
2) sorting the non-dominant solutions of the population according to a non-dominant sorting algorithm, and determining the grade i of the non-dominant solutionsrankCarrying out crowding degree calculation, and screening an initial population according to the lowest level and the highest crowding degree;
3) performing rotation quantum gate, crossover and mutation on the screened initial population to generate a progeny population;
4) according to the formulas (8) - (18), the generated population is checked, unqualified individuals are removed, the parent population and the offspring population are combined, and the power grid operation cost objective function F is calculated according to the formulas (1) and (6)1Low carbon economic objective function of carbon emissions F2；
5) Calculating the non-dominated level and the crowding degree according to the step 2), selecting an optimal individual to form a new parent population, returning to the step 3) for iteration if the evolution algebra Gen does not reach the maximum evolution algebra max Gen, and outputting an optimal solution if the maximum evolution algebra is reached.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (7)

1. A novel low-carbon economic regulation and control method for a power system is characterized by comprising the following steps:
step (1) of establishing a power grid operation cost objective function value F1Low carbon economic objective function value F of sum carbon emission2；
Determining constraint conditions of a low-carbon economic regulation and control model of a power grid;
step (3) solving algorithm IMQG by utilizing multiple targetsA is to the power grid running cost objective function value F1Low carbon economic objective function value F of carbon emission2And solving the multi-target regulation and control model.
2. The novel low-carbon economic regulation and control method for the power system as claimed in claim 1, wherein before the step (1), a novel low-carbon economic regulation and control framework for the power system is constructed, and the specific method is as follows:
under the principle of maximizing the wind and light new energy power generation output, the influence of a multi-energy scheduling scheme on the economy and low carbon of a power grid is analyzed, and a novel low carbon economy regulation and control framework of a power system is constructed.
3. The novel low-carbon economic regulation and control method for the power system as claimed in claim 1 or 2, wherein in the step (1), a power grid operation cost objective function value F is established with the aim of minimizing the power grid operation cost1Constructing a low-carbon economic objective function value F of carbon emission by taking the lowest carbon emission of a power grid as a target2，
1) Construction of grid operation cost objective function value F1：
Figure FDA0003611967600000011
Wherein, FGjFor the running cost F of the thermal power generating unithjFor the running cost of hydro-power generating units, FCjFor the operating cost of the energy storage unit, FlossjFor line loss, NG、Nh、NC、NlossThe number of thermal power generator sets, the number of hydroelectric generator sets, the number of energy storage generator sets and the total number of power grid lines are respectively,
operating cost F of thermal power generating unitGjThe calculation model is shown in formula (2):
Figure FDA0003611967600000012
in the formula，aGj、bGj、cGjIs the consumption characteristic parameter of the thermal power generating unit Gj,
Figure FDA0003611967600000013
the variable is 0-1, the operation state of the thermal power generating unit Gj at the moment t is represented, the thermal power generating unit is in a power generation state when the value is 1, the thermal power generating unit is in a stop operation state when the value is 0,
Figure FDA0003611967600000014
the output rho of the thermal power generating unit j at the moment tGjThe cost is the cost of starting and stopping the thermal power generating unit Gj once;
running cost F of hydroelectric generating sethjThe calculation model is shown in formula (3):
Figure FDA0003611967600000015
wherein,
Figure FDA0003611967600000016
the variable is 0-1, the variable represents the running state of the hydroelectric generating set hj at the time t, the variable represents that the hydroelectric generating set is in a power generation state when the value is 1, the variable represents that the hydroelectric generating set is in a stop running state when the value is 0, and rhohjThe cost for starting and stopping the hydroelectric generating set hj once;
energy storage unit operating cost FCjThe calculation model is shown in formula (4):
Figure FDA0003611967600000021
wherein,
Figure FDA0003611967600000022
for the cost coefficient of the charging and discharging power of the energy storage unit Cj,
Figure FDA0003611967600000023
charging power for the jth energy storage unit at the moment t,
Figure FDA0003611967600000024
discharging power for the jth energy storage unit at the moment t;
line loss FlossjThe calculation model is shown in formula (5):
Figure FDA0003611967600000025
in the formula
Figure FDA0003611967600000026
Is the current value of line j at time t, Rj、XjResistance and reactance of the line respectively;
2) construction of a Low carbon economic Objective function F of carbon emissions2：
In a dispatching period, the carbon emission of the power grid is generated by a thermal power generating unit, and a low-carbon economic objective function F of the carbon emission is constructed2As shown in equation (6):
Figure FDA0003611967600000027
carbon emission per unit power of thermal power generating unit j at time t
Figure FDA0003611967600000028
The calculation model of (2) is shown in equation (7):
Figure FDA0003611967600000029
wherein,
Figure FDA00036119676000000210
the fuel carbon emission factor of the thermal power generating unit j at the time t is obtained;
Figure FDA00036119676000000211
active power output of the thermal power generating unit j at the moment t;
Figure FDA00036119676000000212
and the thermal power generating unit j outputs reactive power at the moment t.
4. The novel low-carbon economic regulation and control method for the power system as claimed in claim 3, wherein in the step (2), the constraint conditions of the power grid low-carbon economic regulation and control model comprise active balance constraint, wind power generation constraint, photovoltaic power generation constraint, hydroelectric power generation constraint, thermal power unit constraint, energy storage constraint, line constraint and load loss constraint.
5. The novel low-carbon economic regulation and control method for the power system as claimed in claim 4, characterized by active balance constraint as shown in formula (8):
Figure FDA00036119676000000213
wherein,
Figure FDA00036119676000000214
charging power for the jth energy storage unit at the moment t,
Figure FDA00036119676000000215
for the discharge power of the jth energy storage unit at the moment t,
Figure FDA00036119676000000216
for the power at the time t of the jth load,
Figure FDA00036119676000000217
the power is output for the jth wind power plant at the moment t,
Figure FDA00036119676000000218
the power is applied to the jth photovoltaic field station at the moment t,
Figure FDA00036119676000000219
the output is output for the jth hydropower station at the moment t,
Figure FDA00036119676000000220
the output of the jth thermal power generating unit at the moment t is NCNumber of energy storage units N in charged stateDNumber of energy storage units N in discharge stateLNumber of loads, NPWIs the number of wind power plants, NPVNumber of photovoltaic power stations, NhNumber of hydroelectric stations, NGThe number of thermal power generating units;
the wind power generation constraint comprises wind power climbing constraint and wind power wind abandoning constraint, and is shown in a formula (9):
Figure FDA0003611967600000031
wherein,
Figure FDA0003611967600000032
the wind power climbing rate is the wind power climbing rate,
Figure FDA0003611967600000033
respectively the minimum and maximum climbing rates of the wind power, delta t is the time interval between two wind power at the moment t,
Figure FDA0003611967600000034
the wind power output at the time t is obtained,
Figure FDA0003611967600000035
for the wind power abandoning power of the wind power at the time t,
Figure FDA0003611967600000036
wind power at time t; according toWind power permeability P'PWCalculating wind power output so as to obtain the wind power climbing rate;
wind power permeability P'PWActual output of wind power absorption power in scheduling period is taken
Figure FDA0003611967600000037
And the total wind power load P in the dispatching cycleLj，tAs shown in equation (10):
Figure FDA0003611967600000038
the photovoltaic power generation constraints include photovoltaic operation constraints, photovoltaic climbing constraints and light abandonment constraints, as shown in equation (11):
Figure FDA0003611967600000039
wherein,
Figure FDA00036119676000000310
the lower limit of the photovoltaic power generation power is,
Figure FDA00036119676000000311
upper limit of photovoltaic Power Generation, P'PV,tIs photovoltaic power generation power at time t ', P'PV，t-1The photovoltaic power generation power at the time t-1,
Figure FDA00036119676000000312
respectively photovoltaic down and up ramp rate limit value, P'PVD,tAbandoning the optical power for the time t;
the hydro-power generation constraints include hydro-turbine output constraints and available water volume constraints, as shown in equation (12):
Figure FDA00036119676000000313
wherein,
Figure FDA00036119676000000314
respectively the minimum and maximum output of the jth hydraulic generator,
Figure FDA00036119676000000315
respectively the maximum and minimum water consumption of the hydropower station,
Figure FDA00036119676000000316
the output force at the moment t of the jth hydraulic generator,
Figure FDA00036119676000000317
the water consumption of the hydropower station at the moment t;
the power unit constraint comprises thermal power unit operation constraint and thermal power unit climbing constraint, and is shown in a formula (13):
Figure FDA00036119676000000318
wherein,
Figure FDA00036119676000000319
is the output lower limit of the ith thermal power generating unit,
Figure FDA00036119676000000320
is the output upper limit of the ith thermal power generating unit,
Figure FDA00036119676000000321
the output of the ith thermal power generating unit,
Figure FDA00036119676000000322
is the lower climbing rate limit value of the ith thermal power generating unit,
Figure FDA00036119676000000323
the value is the uphill gradient rate limit value of the ith thermal power generating unit;
Figure FDA00036119676000000324
the slope climbing rate of the ith thermal power generating unit at the t moment
The energy storage constraint comprises energy storage power constraint, energy storage chargeability constraint and energy storage capacity constraint; wherein the stored energy power is constrained as shown in equation (14):
Figure FDA0003611967600000041
in the formula
Figure FDA0003611967600000042
In order to store the maximum charging power,
Figure FDA0003611967600000043
for storing maximum discharge power, PC，tFor storing charging power at time t, PD，tThe energy storage discharge power is stored for the time t,
Figure FDA0003611967600000044
for storing minimum charging power, negative values indicate discharge,
Figure FDA0003611967600000045
is the energy storage minimum discharge power, and negative values represent charging;
the energy storage charge constraint is shown in equation (15):
Figure FDA0003611967600000046
in the formula, SOCmax、SOCminFor maximum and minimum charge rate of stored energy, SOCtFor storing energy charge rate at time t, SOC0For initial chargeability of the energy storage system, EtIs the energy storage capacity;
energy storage capacity constraint, as shown in equation (16):
Figure FDA0003611967600000047
wherein E isMax、EMinUpper and lower limits of energy storage capacity, eta1，η2Efficiency of energy storage discharge and charging respectively;
the line constraints include line power equality constraints, line transmit power constraints, and nodal phase angle constraints, as shown in equation (17):
Figure FDA0003611967600000048
wherein, Pij,tTransmission power of the line with i, j as end points at time t, BijIs the susceptance, θ, of the linei,t、θj,tThe phase angles of the i and j nodes at the time t,
Figure FDA0003611967600000049
is the line thermal stability limit;
the loss-of-load constraint means that the loss-of-load power of a node in the power system cannot be greater than the load power of the node, as shown in equation (18):
Figure FDA00036119676000000410
wherein, PLDj，tIs the power of the node lost load, PLj，tLoad power for the node.
6. The novel low-carbon economic regulation and control method for the power system as claimed in claim 5, wherein in the step (3): solving the multi-target regulation and control model of the power grid in the novel power system environment by adopting a multi-target solving algorithm IMQGA (inertial measurement System), and coordinating the power of a thermal power unit, a hydroelectric power unit and an energy storage unitRate, cost of grid operation objective function F1Low carbon economic objective function F of sum carbon emission2An optimum state is reached.
7. The novel low-carbon economic regulation and control method of the power system as claimed in claim 6, characterized in that the solving method of the multi-target regulation and control model of the power grid in the novel power system environment by adopting the multi-target solving algorithm IMQGA is as follows:
1) establishing initial power grid multi-target regulation initial solution population
Figure FDA0003611967600000051
According to formulas (8) - (18), checking the initial population, and removing individuals which do not meet the constraint; calculating a power grid operation cost objective function F according to the formulas (1) - (7)1Low carbon economic objective function of carbon emissions F2(ii) a If the individuals which do not meet the constraint condition are removed, the population quantity is supplemented again according to the initialization rule;
2) sorting the non-dominant solutions of the population according to a non-dominant sorting algorithm, and determining the grade i of the non-dominant solutionsrankCarrying out crowding degree calculation, and screening an initial population according to the lowest level and the highest crowding degree;
3) performing rotation quantum gate, crossing and variation on the screened initial population to generate a progeny population;
4) according to the formulas (8) - (18), the generated population is checked, unqualified individuals are removed, the parent population and the offspring population are combined, and the power grid operation cost objective function F is calculated according to the formulas (1) and (6)1Low carbon economic objective function of carbon emission F2；
5) Calculating the non-dominated level and the crowding degree according to the step 2), selecting an optimal individual to form a new parent population, returning to the step 3) for iteration if the evolution algebra Gen does not reach the maximum evolution algebra maxGen, and outputting an optimal solution if the maximum evolution algebra is reached.
CN202210433592.0A 2022-04-24 2022-04-24 Novel low-carbon economic regulation and control method for power system Pending CN114744684A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210433592.0A CN114744684A (en) 2022-04-24 2022-04-24 Novel low-carbon economic regulation and control method for power system

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210433592.0A CN114744684A (en) 2022-04-24 2022-04-24 Novel low-carbon economic regulation and control method for power system

Publications (1)

Publication Number Publication Date
CN114744684A true CN114744684A (en) 2022-07-12

Family

ID=82284060

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210433592.0A Pending CN114744684A (en) 2022-04-24 2022-04-24 Novel low-carbon economic regulation and control method for power system

Country Status (1)

Country Link
CN (1) CN114744684A (en)

Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115130900A (en) * 2022-07-19 2022-09-30 东南大学溧阳研究院 LSTM-based online carbon emission calculation method for gas turbine set under variable working conditions
CN115147007A (en) * 2022-08-01 2022-10-04 东南大学溧阳研究院 Park low-carbon economic energy utilization method based on electricity-carbon information and NSGA-II
CN115481802A (en) * 2022-09-21 2022-12-16 东南大学溧阳研究院 Urban power grid orderly power utilization method considering carbon emission and power utilization grade constraints
CN115563815A (en) * 2022-11-11 2023-01-03 国网江苏省电力有限公司电力科学研究院 Method and device for simulating and calculating space-time evolution of carbon emission flow of double-high power system
CN115860287A (en) * 2023-03-02 2023-03-28 东方电气集团科学技术研究院有限公司 Low-carbon economical scheduling method for energy storage and generator set
CN116882641A (en) * 2023-05-22 2023-10-13 国网湖北省电力有限公司宜昌供电公司 Synergistic carbon reduction method suitable for ground and municipal power grid company
WO2024108641A1 (en) * 2022-11-24 2024-05-30 安徽蓝海之光科技有限公司 Regional carbon emission smart measurement system based on low-carbon energy consumption optimization collaboration
CN118504940A (en) * 2024-07-17 2024-08-16 国网浙江省电力有限公司金华供电公司 Multi-element optimization method and system suitable for carbon emission and transmission and distribution integration

Cited By (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115130900A (en) * 2022-07-19 2022-09-30 东南大学溧阳研究院 LSTM-based online carbon emission calculation method for gas turbine set under variable working conditions
CN115130900B (en) * 2022-07-19 2023-04-18 东南大学溧阳研究院 LSTM-based online carbon emission calculation method for gas turbine set under variable working conditions
CN115147007A (en) * 2022-08-01 2022-10-04 东南大学溧阳研究院 Park low-carbon economic energy utilization method based on electricity-carbon information and NSGA-II
CN115481802A (en) * 2022-09-21 2022-12-16 东南大学溧阳研究院 Urban power grid orderly power utilization method considering carbon emission and power utilization grade constraints
CN115481802B (en) * 2022-09-21 2024-03-01 东南大学溧阳研究院 Urban power grid ordered electricity utilization method considering carbon emission and electricity utilization level constraint
CN115563815A (en) * 2022-11-11 2023-01-03 国网江苏省电力有限公司电力科学研究院 Method and device for simulating and calculating space-time evolution of carbon emission flow of double-high power system
CN115563815B (en) * 2022-11-11 2023-11-28 国网江苏省电力有限公司电力科学研究院 Method and device for simulating and calculating space-time evolution of carbon emission flow of double-high power system
WO2024108641A1 (en) * 2022-11-24 2024-05-30 安徽蓝海之光科技有限公司 Regional carbon emission smart measurement system based on low-carbon energy consumption optimization collaboration
CN115860287A (en) * 2023-03-02 2023-03-28 东方电气集团科学技术研究院有限公司 Low-carbon economical scheduling method for energy storage and generator set
CN116882641A (en) * 2023-05-22 2023-10-13 国网湖北省电力有限公司宜昌供电公司 Synergistic carbon reduction method suitable for ground and municipal power grid company
CN116882641B (en) * 2023-05-22 2024-05-28 国网湖北省电力有限公司宜昌供电公司 Synergistic carbon reduction method suitable for ground and municipal power grid company
CN118504940A (en) * 2024-07-17 2024-08-16 国网浙江省电力有限公司金华供电公司 Multi-element optimization method and system suitable for carbon emission and transmission and distribution integration

Similar Documents

Publication Publication Date Title
CN114744684A (en) Novel low-carbon economic regulation and control method for power system
CN109325608B (en) Distributed power supply optimal configuration method considering energy storage and considering photovoltaic randomness
WO2023274425A1 (en) Multi-energy capacity optimization configuration method for wind-solar-water-fire storage system
Wang et al. PSO-based multi-criteria optimum design of a grid-connected hybrid power system with multiple renewable sources of energy
CN105811409B (en) A kind of microgrid multiple target traffic control method containing hybrid energy storage system of electric automobile
CN110970912A (en) Operation simulation method for new energy power system containing energy storage
CN107609693A (en) Multi-objective optimization method for micro-grid based on Pareto archive particle swarm algorithm
CN107546781A (en) Micro-capacitance sensor multiple target running optimizatin method based on PSO innovatory algorithms
CN112821470B (en) Micro-grid group optimization scheduling strategy based on niche chaotic particle swarm algorithm
CN111327053A (en) Multi-source microgrid capacity optimal configuration method suitable for polar climate
CN116667325B (en) Micro-grid-connected operation optimization scheduling method based on improved cuckoo algorithm
CN203933038U (en) From the grid-connected mixing photovoltaic power generation control system of net
CN107565880B (en) Optimization-type wind light mutual complementing hybrid power system
CN116805803A (en) Energy scheduling method of wind-solar energy storage off-grid hydrogen production system based on self-adaptive MPC
CN115271195A (en) Power system multi-objective energy storage optimization method based on improved genetic algorithm
Jemaa et al. Optimum sizing of hybrid PV/wind/battery system using Fuzzy-Adaptive Genetic Algorithm
CN117833320A (en) Energy storage optimization scheduling method and system in distributed photovoltaic power distribution network
CN110190630B (en) Distribution network prevention-emergency control method containing multiple micro energy networks
CN117252425A (en) Power supply planning and thermal power transformation decision modeling method considering carbon emission and power balance risk
CN112836423A (en) Microgrid capacity optimization configuration method based on improved differential evolution algorithm
Jemaa et al. Optimum sizing of hybrid PV/Wind/battery installation using a fuzzy PSO
CN115375032A (en) Multi-time scale optimization scheduling method for regional power grid with distributed energy storage
CN114398777A (en) Power system flexibility resource allocation method based on Bashi game theory
Kai et al. Optimization for PV-ESS in Distribution Network Based on CSBO
Zhao et al. Comprehensive Dispatching Optimization for Distribution Network Containing Microgrid

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination