CN114399162A - Rolling optimization scheduling method based on energy scheduling time adaptive change - Google Patents

Rolling optimization scheduling method based on energy scheduling time adaptive change Download PDF

Info

Publication number
CN114399162A
CN114399162A CN202111520536.2A CN202111520536A CN114399162A CN 114399162 A CN114399162 A CN 114399162A CN 202111520536 A CN202111520536 A CN 202111520536A CN 114399162 A CN114399162 A CN 114399162A
Authority
CN
China
Prior art keywords
scheduling
power
model
energy
optimization
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
CN202111520536.2A
Other languages
Chinese (zh)
Inventor
吴振杰
黄天恩
唐剑
李祥
王源涛
莫雅俊
周依希
徐双蝶
许�鹏
周志全
张洁
李城达
应燕
陈煜�
张超
王艳
廖培
夏衍
董航
孙思聪
陈嘉宁
苏熀兴
杨兴超
李跃华
祝文澜
向新宇
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.)
Hangzhou Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
Original Assignee
Hangzhou Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Hangzhou Power Supply Co of State Grid Zhejiang Electric Power Co Ltd filed Critical Hangzhou Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
Priority to CN202111520536.2A priority Critical patent/CN114399162A/en
Publication of CN114399162A publication Critical patent/CN114399162A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • G06Q30/0202—Market predictions or forecasting for commercial activities
    • 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
    • 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
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
    • 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
    • Y04S50/00—Market activities related to the operation of systems integrating technologies related to power network operation or related to communication or information technologies
    • Y04S50/14—Marketing, i.e. market research and analysis, surveying, promotions, advertising, buyer profiling, customer management or rewards

Abstract

The invention discloses a rolling optimization scheduling method based on self-adaptive change of energy scheduling time, which comprises the following steps of: s1, establishing a power load model and an energy output model; s2, determining a multi-objective function of the low-carbon power generation scheduling model by taking the lowest power generation energy consumption and the lowest carbon emission of the power system as optimization targets; s3, converting the low-carbon power generation scheduling optimization model with the double optimization targets into a single target function with the aim of minimizing the total cost of electric energy production represented by the total cost of power generation energy consumption and carbon emission of the power system; s4, introducing feedback control, and correcting the current scheduling strategy by using the real-time measurement value to reduce the scheduling deviation; and performing rolling optimization on the objective function. According to the scheme, on the premise of ensuring the safety and stability of the electric power system, the running cost of the novel electric power system can be reduced, and the economical efficiency and the safety of the system running are improved.

Description

Rolling optimization scheduling method based on energy scheduling time adaptive change
Technical Field
The invention relates to the field of optimal scheduling of power systems, in particular to a rolling optimal scheduling method based on adaptive change of energy scheduling time.
Background
Wind energy and photovoltaic are used as important components of renewable energy sources, and after a large number of new energy source units are connected into a power distribution network, the problems of volatility, intermittency, low controllability and the like not only bring great challenges to safe and reliable operation of the power distribution network, but also bring complexity and uncertainty to planning of the power distribution network. Meanwhile, the flexible load has uncertainty, such as uncertainty of influence of psychological behaviors, weather factors and emergencies on user demand response behaviors; the electric automobile has time-varying and random access to the internet. On the basis of load fluctuation, uncertainty and difficulty in power prediction are increased in renewable energy power generation, so that power generation planning is poor, and adverse effects are brought to power grid mode arrangement and real-time monitoring.
Wind power and photovoltaic power are used as a new energy power generation mode with the largest application scale at present, and have positive significance on energy conservation and emission reduction; however, the output of the wind power generation system has randomness and volatility, and large-scale wind power and photovoltaic grid connection provides higher requirements for power market operation and unit scheduling decisions. Considering that wind power and photovoltaic have uncertainty, when the proportion of a fire power unit in the system is higher and the proportion of a new energy unit is lower, the carbon emission of the system is more and the safety is higher; when the proportion of the fire-electric machine set in the system is lower, the proportion of the new energy machine set is lower and higher, the carbon emission of the system is less, and the safety is lower. Therefore, establishing a scientific scheduling decision method is very important for reasonably consuming wind power and considering the safety of system operation and the carbon emission target.
The following challenges exist in scheduling the operation of new power systems:
(1) the carbon capture technology and the new energy power generation can bring huge economic benefits and are environment-friendly, but the randomness and the intermittence of renewable energy also bring challenges to the safe and reliable operation of a power grid, the fluctuation of the renewable energy can cause the change of the quality of electric energy, and certain influence can be brought to the stability and the reliability of the whole power grid. And some renewable energy sources, including the potential energy of wind, solar and water, have uncertainty related to time. In order to promote the development of low-carbon economy, the difficulty is brought to research on the uncertainty modeling of the output of the carbon-trapping new energy;
(2) at present, many optimized dispatching models of an electric power system are based on model predictive control, the model predictive control belongs to the field of dynamic optimization, the actual operation condition is considered in the optimization process, real-time data are input into an objective function continuously in a rolling mode, and therefore the optimal dispatching of the electric power system is achieved. In the MPC-based decentralized rolling optimization scheduling process, the scheduling time interval is a fixed value, but in the actual operation process, the power load of the system is not very large, and if the fixed value is still adopted as the scheduling time interval, unnecessary scheduling cost is caused. Therefore, it is difficult to study the rolling optimization scheduling of adaptive change of scheduling time interval.
Disclosure of Invention
The invention aims to design a rolling optimization scheduling method based on energy scheduling time adaptive change, fully considers the power system disturbance caused by the gradual increase of the permeability of renewable energy, and establishes a power load model and an energy output model considering source load uncertainty; responding to a call of a double-carbon policy and energy transformation, considering the safety, low carbon and economy of system operation, establishing a multi-objective optimization model, introducing feedback correction, and correcting the deviation of the day-ahead scheduling; on the premise of ensuring safety and stability, the running cost of a novel power system is reduced, and the economical efficiency and safety of system running are improved.
In order to achieve the technical purpose, the invention provides a technical scheme that the rolling optimization scheduling method based on the self-adaptive change of the energy scheduling time comprises the following steps:
s1, establishing a power load model and an energy output model;
s2, determining a multi-objective function of the low-carbon power generation scheduling model by taking the lowest power generation energy consumption and the lowest carbon emission of the power system as optimization targets;
s3, converting the low-carbon power generation scheduling optimization model with the double optimization targets into a single target function with the aim of minimizing the total cost of electric energy production represented by the total cost of power generation energy consumption and carbon emission of the power system;
s4, introducing feedback control, and correcting the current scheduling strategy by using the real-time measurement value to reduce the scheduling deviation; and performing rolling optimization on the objective function.
Preferably, the power load model comprises a static load model and a temperature control load model; the static load model comprises a constant impedance characteristic load Z, a constant current characteristic load I and a constant power characteristic load P, and 6 parameters of% Z are usedi、％Ii、％Pi、
Figure BDA0003407157070000021
And characterizing the active and reactive consumption at the load i, wherein the formula is as follows:
Figure BDA0003407157070000022
in the formula: vNiAnd ViAre respectively provided withIs the nominal and actual voltage at point i; siIs rated power; % Zi、％Ii、％PiThe proportion of three loads, namely a constant impedance characteristic load Z, a constant current characteristic load I and a constant power characteristic load P, is respectively large and small;
Figure BDA0003407157070000023
Figure BDA0003407157070000024
the phase angles of three loads, namely a constant impedance characteristic load Z, a constant current characteristic load I and a constant power characteristic load P are respectively.
Preferably, the formula of the temperature-controlled load model is as follows:
Figure BDA0003407157070000025
in the formula: t isaIs the indoor air temperature; t ismIs the mass temperature; u shapeaIs the electrical conductance of the building; t isoIs the outdoor temperature; hmElectrical conductance for room air and solids; caIs the thermal mass of air; cmThermal mass for building materials and finishing; qaHeat for the air entering the room; qmIs the heat of the solids entering the chamber.
Preferably, the energy output model comprises a wind power output model, a photovoltaic output model and a hydroelectric output model;
wherein the content of the first and second substances,
the wind power output model has the following formula:
Figure BDA0003407157070000031
in the formula, VrIs the rated wind speed, P, of the fanrIs rated output power of the fan, V is current wind speed, VciTo cut into the wind speed; vcoFor cutting out wind speed Vco。
Preferably, the photovoltaic output model has the following formula:
Figure BDA0003407157070000032
in the formula: pstcIs the output power of the photovoltaic cell panel under the standard condition; i isstcIs standard degree of solar radiation, ItIs the maximum solar irradiance, eta, of the location of the photovoltaic arrayt1Is the photoelectric conversion efficiency of the solar panel;
preferably, the hydroelectric power output model has the following formula:
Pwat＝9.81QHηt2
Pwatis the generating power of the unit, Q is the average flow rate in a certain period, H is the fall of the river reach, etat2Is the efficiency of the water turbine for generating electricity.
Preferably, in S2, the multi-objective function formula of the low-carbon power generation scheduling model is as follows:
Figure BDA0003407157070000033
in the formula, F1The total power consumption of the power system; f2Is the total amount of carbon emission generated by the power system; t is the number of time segments; n is the total number of the internal combustion engine sets in the power system; u shapei,tIs the variable of the running state of the unit i in the time period t, when Ui,tRunning on 1 time group, Ui,tStopping the machine when the speed is 0; siStarting energy consumption of the unit i; SC (Single chip computer)iIs CO when the unit i starts2Discharge capacity; f. ofi(Pi,t) Is a characteristic function of the consumption of the unit i, fi(Pi,t)＝a0,i+a1,iPi,t+a2,iPi,t 2Wherein a is0,i、a1,i、a2,iIs given parameters; ci(Pi,t) Is the electrical carbon characteristic parameter of unit i, Ci(Pi,t)＝b0,i+b1,iPi,t+b2,iPi,t 2Wherein b is0,i、b1,i、b2,iGiven the parameters.
Preferably, in S3, the transformed single objective function formula is as follows:
Figure BDA0003407157070000041
wherein F is the total cost of electric energy production; pi,fuelThe unit is yuan/kg for the price of the fuel used by the unit;
Figure BDA0003407157070000042
is the external cost per carbon emission in units of yuan/kg, Ui,tAnd Ui,t-1Representing the states of the unit i at the time T and the time T-1, taking 1 when starting, taking 0 when stopping, T representing the total time period, N representing the number of the generator units, Pi,tRepresenting the power of the unit i at time t, fi(Pi,t) And SiRepresenting the fuel consumption of the unit i during operation and start-stop, ci(Pi,t) And SCiIndicating the CO2 emissions when unit i is running and starting and stopping.
Preferably, S4 includes the steps of:
s41, forecasting the load value and the energy output in the day through the established power load model and the energy output model, and setting a scheduling time interval t1And t2And scheduling time interval decision index standard value xi0；
S42, calculating the actual output P of the current generator0(k) As an initial value; at a scheduling time interval t1Establishing a generator output prediction model: the formula is as follows:
Figure BDA0003407157070000043
where P (k + i) represents the variable output value at the time k when k + i is predicted, and Δ u (k + i | k) represents the increment of the power system variable in the (k + (i-1), k + i) period predicted at the time k;
s43, obtaining a control sequence according to the optimization of the single objective function, wherein the formula is as follows:
Δu＝[Δu(k+1|k),Δu(k+2|k),Δu(k+3|k)...,Δu(k+N|k)]T
wherein Δ u (k + i | k) represents the increment of the power system variable within the predicted (k + (i-1), k + i ] time period at time k, and N represents the predicted total step size;
s44, issuing the obtained control increment to a power system, calculating the cost c1 in the rolling period by using the output of the generator at the k +1-k + N moment, and calculating a scheduling time interval decision index xi;
s45, calculating a scheduling time interval decision index xi and a standard value xi0Compared with the case that xi is less than xi0Changing the scheduling time interval to t2 for rolling optimization, repeating the steps S42-S43 to obtain control increment, and solving
Figure BDA0003407157070000044
The output of the generator and the gas production rate of a natural gas source are carried out at any moment; judging whether the optimization period is finished or not, if not, measuring
Figure BDA0003407157070000051
Repeating the step of S42 to continue the rolling optimization at the moment of the output of the generator and the gas production rate of the natural gas source, and otherwise, ending the optimization scheduling;
s46, if xi > xi0And then solving the generator output at the moment k +1 according to the control sequence obtained in the S43, judging whether the optimization cycle is finished, if not, measuring the generator output at the moment k +1 and the gas production rate of the natural gas source, repeating the S42 to continue the rolling optimization, otherwise, finishing the optimization scheduling.
Preferably, in S44, the scheduling time interval decision index ξ is formulated as follows:
Figure BDA0003407157070000052
in the formula, c1Indicating the determination of the scheduling cost of the system during a roll period at time k, c0A reference cost of a day-ahead schedule for one rolling cycle at time k is indicated.
The invention has the beneficial effects that: the invention designs a rolling optimization scheduling method based on energy scheduling time adaptive change, which fully considers the power system disturbance caused by the gradual increase of the permeability of renewable energy, and establishes a power load model and an energy output model considering source load uncertainty; responding to a call of a double-carbon policy and energy transformation, considering the safety, low carbon and economy of system operation, establishing a multi-objective optimization model, introducing feedback correction, and correcting the deviation of the day-ahead scheduling; on the premise of ensuring safety and stability, the running cost of a novel power system is reduced, and the economical efficiency and safety of system running are improved.
Drawings
Fig. 1 is a flowchart of a rolling optimization scheduling method based on adaptive change of energy scheduling time according to the present invention.
Detailed Description
For the purpose of better understanding the objects, technical solutions and advantages of the present invention, the following detailed description of the present invention with reference to the accompanying drawings and examples should be understood that the specific embodiment described herein is only a preferred embodiment of the present invention, and is only used for explaining the present invention, and not for limiting the scope of the present invention, and all other embodiments obtained by a person of ordinary skill in the art without making creative efforts shall fall within the scope of the present invention.
Example (b): as shown in fig. 1, a rolling optimization scheduling method based on adaptive change of energy scheduling time includes the following steps:
s1, establishing a power load model and an energy output model;
the power load model comprises a static load model and a temperature control load model; the static load model comprises a constant impedance characteristic load Z, a constant current characteristic load I and a constant power characteristic load P, and 6 parameters of% Z are usedi、％Ii、％Pi、
Figure BDA0003407157070000053
Figure BDA0003407157070000054
And characterizing the active and reactive consumption at the load i, wherein the formula is as follows:
Figure BDA0003407157070000061
in the formula: vNiAnd ViRated voltage and actual voltage at point i, respectively; siIs rated power; % Zi、％Ii、％PiThe proportion of three loads, namely a constant impedance characteristic load Z, a constant current characteristic load I and a constant power characteristic load P, is respectively large and small;
Figure BDA0003407157070000062
Figure BDA0003407157070000063
the phase angles of three loads, namely a constant impedance characteristic load Z, a constant current characteristic load I and a constant power characteristic load P are respectively.
The temperature control load model has the following formula:
Figure BDA0003407157070000064
in the formula: t isaIs the indoor air temperature; t ismIs the mass temperature; u shapeaIs the electrical conductance of the building; t isoIs the outdoor temperature; hmElectrical conductance for room air and solids; caIs the thermal mass of air; cmThermal mass for building materials and finishing; qaHeat for the air entering the room; qmHeat of solids entering the chamber;
the system also comprises an electric automobile load model, wherein the electric automobile is usually used for adjusting the supply and demand balance of the electric power market, the electric automobile is considered as a discrete point to be optimized, and a charge and discharge mode is implemented through a control system with detection and control capabilities. In the charging mode, the electric vehicle physical model describes the rule that the SOC value of the power battery changes along with the period, and has the following relationship:
Figure BDA0003407157070000065
in the formula: SOC0The battery power state of the initial electric vehicle is set; when the SOCi is i, the battery electric quantity state of the electric automobile is obtained; edr is the capacity consumed by the electric automobile in running; cbatt is the rated capacity of the battery; PEV is the electric vehicle power; Δ t is the interval time;
the method also comprises the following steps:
the flexible loads can be classified into three categories, classified by response characteristics: transferable load, translatable load and reducible load. The load can be transferred, namely the total power consumption is unchanged in a scheduling cycle, but the power consumption characteristics are flexible, and the power consumption in each period can be flexibly adjusted; taking the response price of electricity as an example, the transferable load can be summarized as follows:
ΔPshift(t)＝f1(P0(t),Δpshift(t),kshift(t),vshift(t))
Figure BDA0003407157070000066
in the formula: delta Pshift(t) is the response of the transferable load for a period of t; p0(t) is the base charge for a period t; Δ pshift(t) is a vector of price difference between the t period and other periods; k is a radical ofshift(t) is the mutual elastic vector of the t period relative to other periods; v. ofshift(t) is the transfer rate; t is a scheduling period.
The load can be translated, the load is restricted by the production flow, and the power utilization curve can only be translated in different periods, such as large industrial users; can be expressed as:
ΔPshape(t)＝f2(t+Δt(Δp))-f2(t)
in the formula: delta Pshape(t) is the response of the translatable load over a period of t; f. of2(t) is the initial power usage curve; Δ t (Δ p) is a load shift period due to the change in electricity price Δ p.
Can reduce load and power consumption according to requirementSuch as air conditioning, lighting, etc. Can be expressed as: delta Pre(t)＝f3(P0(t),Δpre(t),kre(t),vre(t))
In the formula: delta Pre(t) the response amount of the load can be reduced in the period t; Δ pre(t) the variation of the electricity price in the period t; k is a radical ofre(t) is the coefficient of self-elasticity for a period of t; v. ofshift(t) is the clipping rate.
The energy output model comprises a wind power output model, a photovoltaic output model and a hydroelectric output model;
wherein the content of the first and second substances,
the wind power output model has the following formula:
Figure BDA0003407157070000071
in the formula, VrIs the rated wind speed, P, of the fanrIs rated output power of the fan, V is current wind speed, VciTo cut into the wind speed; vcoFor cutting out wind speed Vco。
The photovoltaic output model has the following formula:
Figure BDA0003407157070000072
in the formula: pstcIs the output power of the photovoltaic cell panel under the standard condition; i isstcIs standard degree of solar radiation, ItIs the maximum solar irradiance, eta, of the location of the photovoltaic arrayt1Is the photoelectric conversion efficiency of the solar panel;
the water and electricity output model has the following formula:
Pwat＝9.81QHηt2
Pwatis the generating power of the unit, Q is the average flow rate in a certain period, H is the fall of the river reach, etat2Is the efficiency of the water turbine for generating electricity.
S2, determining a multi-objective function of the low-carbon power generation scheduling model by taking the lowest power generation energy consumption and the lowest carbon emission of the power system as optimization targets;
in order to actively respond to the call of the double-carbon policy and the energy transformation, the low carbon property of the system operation is more emphasized, the influence on the environment is further reduced on the premise of ensuring the economy and stability of the system, and a multi-objective optimization model containing the low carbon property, the economy and the safety is established; according to the definition of the low-carbon power generation dispatching, the objective function of the low-carbon power generation dispatching model is determined by taking the lowest power generation energy consumption and the lowest carbon emission of the system as optimization targets, and the multi-objective function formula of the low-carbon power generation dispatching model is as follows:
Figure BDA0003407157070000081
in the formula, F1The total power consumption of the power system; f2Is the total amount of carbon emission generated by the power system; t is the number of time segments; n is the total number of the internal combustion engine sets in the power system; u shapei,tIs the variable of the running state of the unit i in the time period t, when Ui,tRunning on 1 time group, Ui,tStopping the machine when the speed is 0; siStarting energy consumption of the unit i; SC (Single chip computer)iIs CO when the unit i starts2Discharge capacity; f. ofi(Pi,t) Is a characteristic function of the consumption of the unit i, fi(Pi,t)＝a0,i+a1,iPi,t+a2,iPi,t 2Wherein a is0,i、a1,i、a2,iIs given parameters; ci(Pi,t) Is the electrical carbon characteristic parameter of unit i, Ci(Pi,t)＝b0,i+b1,iPi,t+b2,iPi,t 2Wherein b is0,i、b1,i、b2,iGiven the parameters.
Formulating constraint conditions of the multi-objective function:
technical constraints of thermal power generating units: the method mainly comprises the steps of unit capacity constraint, climbing rate constraint, minimum start-stop time constraint and the like; and (3) unit capacity constraint:
Ui,tPimin≤Pi,t≤Ui,tPimax
in the formula Pimax、PiminRespectively representing the upper limit and the lower limit of the output of the thermal power generating unit i;
and (3) slope climbing rate constraint:
Figure BDA0003407157070000082
in the formula
Figure BDA0003407157070000083
Respectively limiting the up-down climbing of the thermal power generating unit i;
minimum start-stop time constraint:
Figure BDA0003407157070000091
in the formula Ti,on、Ti,offRespectively, the minimum continuous operation time and the minimum continuous shutdown time of the thermal power generating unit i.
Technical restraint of the hydroelectric generating set: the method mainly comprises the steps of hydropower generation amount constraint and hydroelectric generating set capacity constraint. For a hydropower plant with regulation capacity, in the day-ahead scheduling, the next day water consumption upper limit is generally given, and the water consumption upper limit can be regarded as the daily power generation upper limit constraint; the formula is as follows:
Figure BDA0003407157070000092
0≤Pj,t≤Pj,max
in the formula Qj,maxIs the daily generated energy upper limit of the hydroelectric generating set j; pj,iIs the j output upper limit of the hydroelectric generating set.
Technical constraints of the wind turbine generator: the method mainly comprises the following steps of (1) restraining the upper limit and the lower limit of the output of the wind turbine generator; the formula is as follows:
Pk,tmin≤Pk,t≤Pk,tmax
in the formula Pk,tmaxThe method is characterized in that the upper limit of the generated power of the wind turbine k in a time period t is obtained by calculating comprehensive wind speed prediction information and a wind speed-power curve of the wind turbine; pk,tminIs the generation of the wind turbine generator k in the time period tThe lower limit of the electric power can be generally 0 in consideration of the good climbing performance of the wind turbine generator.
A system power balance constraint; the formula is as follows:
Figure BDA0003407157070000093
m, L in the formula is the total number of the hydroelectric generator sets and the wind turbine sets in the system respectively; pL,tThe total system load demand after network loss is considered for time period t.
System spare capacity constraints; the formula is as follows:
Figure BDA0003407157070000094
in the formula RU,t、RD,tThe upper and lower rotation reserve capacities required by the system in the period t are respectively.
Line transmission capacity constraints; the formula is as follows:
Figure BDA0003407157070000095
in the formula
Figure BDA0003407157070000096
Is the maximum transmission capacity of the line; sl,zThe sensitivity of the node injection power to the line power flow; pl,netIs the injected power of the node.
A system carbon emission total constraint; the formula is as follows:
Figure BDA0003407157070000101
in the formula (I), the compound is shown in the specification,
Figure BDA0003407157070000102
CO in the dispatching cycle for the power industry2And controlling the total emission.
S3, converting the low-carbon power generation scheduling optimization model with the double optimization targets into a single target function with the aim of minimizing the total cost of electric energy production represented by the total cost of power generation energy consumption and carbon emission of the power system;
the converted single objective function formula is as follows:
Figure BDA0003407157070000103
wherein F is the total cost of electric energy production; pi,fuelThe unit is yuan/kg for the price of the fuel used by the unit;
Figure BDA0003407157070000104
is the external cost per carbon emission in units of yuan/kg, Ui,tAnd Ui,t-1Representing the states of the unit i at the time T and the time T-1, taking 1 when starting, taking 0 when stopping, T representing the total time period, N representing the number of the generator units, Pi,tRepresenting the power of the unit i at time t, fi(Pi,t) And SiRepresenting the fuel consumption of the unit i during operation and start-stop, ci(Pi,t) And SCiIndicating the CO2 emissions when unit i is running and starting and stopping.
S4, introducing feedback control, and correcting the current scheduling strategy by using the real-time measurement value to reduce the scheduling deviation; performing rolling optimization on the target function; the method comprises the following substeps:
s41, forecasting the load value and the energy output in the day through the established power load model and the energy output model, and setting a scheduling time interval t1And t2And scheduling time interval decision index standard value xi0；
S42, calculating the actual output P of the current generator0(k) As an initial value; at a scheduling time interval t1Establishing a generator output prediction model: the formula is as follows:
Figure BDA0003407157070000105
where P (k + i) represents the variable output value at the time k when k + i is predicted, and Δ u (k + i | k) represents the increment of the power system variable in the (k + (i-1), k + i) period predicted at the time k;
s43, obtaining a control sequence according to the optimization of the single objective function, wherein the formula is as follows:
Δu＝[Δu(k+1|k),Δu(k+2|k),Δu(k+3|k)...,Δu(k+N|k)]T
wherein Δ u (k + i | k) represents the increment of the power system variable within the predicted (k + (i-1), k + i ] time period at time k, and N represents the predicted total step size;
s44, issuing the obtained control increment to a power system, calculating the cost c1 in the rolling period by using the output of the generator at the k +1-k + N moment, and calculating a scheduling time interval decision index xi;
s45, calculating a scheduling time interval decision index xi and a standard value xi0Compared with the case that xi is less than xi0Changing the scheduling time interval to t2 for rolling optimization, repeating the steps S42-S43 to obtain control increment, and solving
Figure BDA0003407157070000111
The output of the generator and the gas production rate of a natural gas source are carried out at any moment; the formula is as follows:
Figure BDA0003407157070000112
judging whether the optimization period is finished or not, if not, measuring
Figure BDA0003407157070000113
The power output of the generator and the gas production rate of the natural gas source at the moment
Figure BDA0003407157070000114
Repeating the step of S42 to continue the rolling optimization, otherwise, ending the optimization scheduling;
s46, if xi > xi0Then, the generator output at the time k +1, P (k +1) ═ P, is solved according to the control sequence obtained in S430(k)+Δup(k +1| k), judging whether the optimization period is finished, if not, measuring the time of k +1Power output of generator and gas production of natural gas source0(k+1)＝PrealAnd (k +1), wherein k is k +1, repeating the step S42 to continue the rolling optimization, and otherwise, ending the optimization scheduling.
In S44, the scheduling time interval decision index ξ formula is as follows:
Figure BDA0003407157070000115
in the formula, c1Indicating the determination of the scheduling cost of the system during a roll period at time k, c0A reference cost of a day-ahead schedule for one rolling cycle at time k is indicated.
The above-mentioned embodiments are preferred embodiments of the rolling optimization scheduling method based on adaptive variation of energy scheduling time according to the present invention, and the scope of the present invention is not limited thereto, and the scope of the present invention includes and is not limited to the embodiments, and all equivalent variations made according to the shape and structure of the present invention are within the protection scope of the present invention.

Claims (8)

1. A rolling optimization scheduling method based on energy scheduling time adaptive change is characterized in that: the method comprises the following steps:
s1, establishing a power load model and an energy output model;
s2, determining a multi-objective function of the low-carbon power generation scheduling model by taking the lowest power generation energy consumption and the lowest carbon emission of the power system as optimization targets;
s3, converting the low-carbon power generation scheduling optimization model with the double optimization targets into a single target function with the aim of minimizing the total cost of electric energy production represented by the total cost of power generation energy consumption and carbon emission of the power system;
s4, introducing feedback control, and correcting the current scheduling strategy by using the real-time measurement value to reduce the scheduling deviation; and performing rolling optimization on the objective function.
2. The rolling optimization scheduling method based on the adaptive change of the energy scheduling time according to claim 1, wherein:
the power load model comprises a static load model and a temperature control load model; the static load model comprises a constant impedance characteristic load Z, a constant current characteristic load I and a constant power characteristic load P, and 6 parameters of% Z are usedi、％Ii、％Pi、
Figure FDA0003407157060000013
And characterizing the active and reactive consumption at the load i, wherein the formula is as follows:
Figure FDA0003407157060000011
in the formula: vNiAnd ViRated voltage and actual voltage at point i, respectively; siIs rated power; % Zi、％Ii、％PiThe proportion of three loads, namely a constant impedance characteristic load Z, a constant current characteristic load I and a constant power characteristic load P, is respectively large and small;
Figure FDA0003407157060000014
Figure FDA0003407157060000015
the phase angles of three loads, namely a constant impedance characteristic load Z, a constant current characteristic load I and a constant power characteristic load P are respectively.
3. The rolling optimization scheduling method based on the adaptive change of the energy scheduling time according to claim 2, wherein: the temperature control load model has the following formula:
Figure FDA0003407157060000012
in the formula: t isaIs the indoor air temperature; t ismIs the mass temperature; u shapeaFor buildingsThe conductance of (c); t isoIs the outdoor temperature; hmElectrical conductance for room air and solids; caIs the thermal mass of air; cmThermal mass for building materials and finishing; qaHeat for the air entering the room; qmIs the heat of the solids entering the chamber.
4. The rolling optimization scheduling method based on the adaptive change of the energy scheduling time according to claim 1 or 2, wherein: the energy output model comprises a wind power output model, a photovoltaic output model and a hydroelectric output model;
wherein the content of the first and second substances,
the wind power output model has the following formula:
Figure FDA0003407157060000021
in the formula, VrIs the rated wind speed, P, of the fanrIs rated output power of the fan, V is current wind speed, VciTo cut into the wind speed; vcoFor cutting out wind speed Vco；
The photovoltaic output model has the following formula:
Figure FDA0003407157060000022
in the formula: pstcIs the output power of the photovoltaic cell panel under the standard condition; i isstcIs standard degree of solar radiation, ItIs the maximum solar irradiance, eta, of the location of the photovoltaic arrayt1Is the photoelectric conversion efficiency of the solar panel;
the water and electricity output model has the following formula:
Pwat＝9.81QHηt2
Pwatis the generating power of the unit, Q is the average flow rate in a certain period, H is the fall of the river reach, etat2Is the efficiency of the water turbine for generating electricity.
5. The rolling optimization scheduling method based on the adaptive change of the energy scheduling time according to claim 1, wherein: in S2, the multi-objective function formula of the low-carbon power generation dispatching model is as follows:
Figure FDA0003407157060000023
in the formula, F1The total power consumption of the power system; f2Is the total amount of carbon emission generated by the power system; t is the number of time segments; n is the total number of the internal combustion engine sets in the power system; u shapei,tIs the variable of the running state of the unit i in the time period t, when Ui,tRunning on 1 time group, Ui,tStopping the machine when the speed is 0; siStarting energy consumption of the unit i; SC (Single chip computer)iIs CO when the unit i starts2Discharge capacity; f. ofi(Pi,t) Is a characteristic function of the consumption of the unit i, fi(Pi,t)＝a0,i+a1,iPi,t+a2,iPi,t 2Wherein a is0,i、a1,i、a2,iIs given parameters; ci(Pi,t) Is the electrical carbon characteristic parameter of unit i, Ci(Pi,t)＝b0,i+b1,iPi,t+b2,iPi,t 2Wherein b is0,i、b1,i、b2,iGiven the parameters.
6. The rolling optimization scheduling method based on the adaptive change of the energy scheduling time according to claim 1 or 5, wherein: in S3, the formula of the transformed single objective function is as follows:
Figure FDA0003407157060000031
wherein F is the total cost of electric energy production; pi,fuelThe unit is yuan/kg for the price of the fuel used by the unit;
Figure FDA0003407157060000033
is the external cost per carbon emission in units of yuan/kg, Ui,tAnd Ui,t-1Representing the states of the unit i at the time T and the time T-1, taking 1 when starting, taking 0 when stopping, T representing the total time period, N representing the number of the generator units, Pi,tRepresenting the power of the unit i at time t, fi(Pi,t) And SiRepresenting the fuel consumption of the unit i during operation and start-stop, ci(Pi,t) And SCiIndicating the CO2 emissions when unit i is running and starting and stopping.
7. The rolling optimization scheduling method based on the adaptive change of the energy scheduling time according to claim 1, wherein: s4 includes the steps of:
s41, forecasting the load value and the energy output in the day through the established power load model and the energy output model, and setting a scheduling time interval t1And t2And scheduling time interval decision index standard value xi0；
S42, calculating the actual output P of the current generator0(k) As an initial value; at a scheduling time interval t1Establishing a generator output prediction model: the formula is as follows:
Figure FDA0003407157060000032
where P (k + i) represents the variable output value at the time k when k + i is predicted, and Δ u (k + i | k) represents the increment of the power system variable in the (k + (i-1), k + i) period predicted at the time k;
s43, obtaining a control sequence according to the optimization of the single objective function, wherein the formula is as follows:
Δu＝[Δu(k+1|k),Δu(k+2|k),Δu(k+3|k)...,Δu(k+N|k)]T
wherein Δ u (k + i | k) represents the increment of the power system variable within the predicted (k + (i-1), k + i ] time period at time k, and N represents the predicted total step size;
s44, issuing the obtained control increment to a power system, calculating the cost c1 in the rolling period by using the output of the generator at the k +1-k + N moment, and calculating a scheduling time interval decision index xi;
s45, calculating a scheduling time interval decision index xi and a standard value xi0Compared with the case that xi is less than xi0Changing the scheduling time interval to t2 for rolling optimization, repeating the steps S42-S43 to obtain control increment, and solving
Figure FDA0003407157060000041
The output of the generator and the gas production rate of a natural gas source are carried out at any moment; judging whether the optimization period is finished or not, if not, measuring
Figure FDA0003407157060000042
Repeating the step of S42 to continue the rolling optimization at the moment of the output of the generator and the gas production rate of the natural gas source, and otherwise, ending the optimization scheduling;
s46, if xi > xi0And then solving the generator output at the moment k +1 according to the control sequence obtained in the S43, judging whether the optimization cycle is finished, if not, measuring the generator output at the moment k +1 and the gas production rate of the natural gas source, repeating the S42 to continue the rolling optimization, otherwise, finishing the optimization scheduling.
8. The rolling optimization scheduling method based on the adaptive change of the energy scheduling time according to claim 7, wherein: in S44, the scheduling time interval decision index ξ formula is as follows:
Figure FDA0003407157060000043
in the formula, c1Indicating the determination of the scheduling cost of the system during a roll period at time k, c0A reference cost of a day-ahead schedule for one rolling cycle at time k is indicated.
CN202111520536.2A 2021-12-13 2021-12-13 Rolling optimization scheduling method based on energy scheduling time adaptive change Pending CN114399162A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202111520536.2A CN114399162A (en) 2021-12-13 2021-12-13 Rolling optimization scheduling method based on energy scheduling time adaptive change

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202111520536.2A CN114399162A (en) 2021-12-13 2021-12-13 Rolling optimization scheduling method based on energy scheduling time adaptive change

Publications (1)

Publication Number Publication Date
CN114399162A true CN114399162A (en) 2022-04-26

Family

ID=81227517

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202111520536.2A Pending CN114399162A (en) 2021-12-13 2021-12-13 Rolling optimization scheduling method based on energy scheduling time adaptive change

Country Status (1)

Country Link
CN (1) CN114399162A (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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

Similar Documents

Publication Publication Date Title
CN109193815A (en) A kind of combined heat and power dispatching method improving wind electricity digestion
CN109919399B (en) Day-ahead economic dispatching method and system for comprehensive energy system
CN109256810B (en) Multi-objective optimization method considering uncertain cost of fan output
CN111738502A (en) Multi-energy complementary system demand response operation optimization method for promoting surplus wind power consumption
CN113241757B (en) Multi-time scale optimization scheduling method considering flexible load and ESS-SOP
CN111934360B (en) Virtual power plant-energy storage system energy collaborative optimization regulation and control method based on model predictive control
CN111737884B (en) Multi-target random planning method for micro-energy network containing multiple clean energy sources
CN112821432B (en) Double-layer multi-position configuration method of energy storage system under wind-solar access
CN110543966A (en) day-ahead scheduling optimization method for micro-energy grid with combined supply of electricity, heat and gas
CN111697578A (en) Multi-target energy-storage-containing regional power grid operation control method
CN113255198B (en) Multi-objective optimization method for combined cooling heating and power supply micro-grid with virtual energy storage
CN110829408A (en) Multi-domain scheduling method considering energy storage power system based on power generation cost constraint
CN112270433B (en) Micro-grid optimization method considering renewable energy uncertainty and user satisfaction
CN114330827A (en) Distributed robust self-scheduling optimization method for multi-energy flow virtual power plant and application thereof
CN115498668A (en) Optimization method of comprehensive energy system
CN117436773B (en) Independent micro-grid resource planning method and system containing interruptible load
Zhang et al. Optimized scheduling model for isolated microgrid of wind-photovoltaic-thermal-energy storage system with demand response
CN114399162A (en) Rolling optimization scheduling method based on energy scheduling time adaptive change
CN115659666A (en) Virtual power plant wind-solar combined optimization scheduling method considering comprehensive demand response
CN114188942A (en) Power grid dispatching method comprising large-scale new energy base
Guoqiang et al. A Multi-Source Dispatching Model with Considering the Nuclear Power Plants Dispatching and Wind Power Accommodation
CN113313329A (en) Optimal scheduling method for power distribution network containing comprehensive energy system
CN111555270A (en) Method and system for comprehensive energy optimization and dynamic analysis
Lu et al. Optimal modeling of integrated energy system demand response operation considering wind power absorption
CN117474301B (en) Comprehensive energy system multi-target optimization scheduling method and device based on occupational advanced

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