CN112927095A - Multi-time scale coordinated scheduling method for electric heating combined system - Google Patents

Multi-time scale coordinated scheduling method for electric heating combined system Download PDF

Info

Publication number
CN112927095A
CN112927095A CN202110027877.XA CN202110027877A CN112927095A CN 112927095 A CN112927095 A CN 112927095A CN 202110027877 A CN202110027877 A CN 202110027877A CN 112927095 A CN112927095 A CN 112927095A
Authority
CN
China
Prior art keywords
scheduling
day
time
formula
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.)
Granted
Application number
CN202110027877.XA
Other languages
Chinese (zh)
Other versions
CN112927095B (en
Inventor
杨冬锋
徐扬
刘晓军
姜超
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.)
Jilin Province Beitian Gong Software Development Co ltd
Northeast Electric Power University
Economic and Technological Research Institute of State Grid Jilin Electric Power Co Ltd
Original Assignee
Northeast Dianli University
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 Northeast Dianli University filed Critical Northeast Dianli University
Priority to CN202110027877.XA priority Critical patent/CN112927095B/en
Publication of CN112927095A publication Critical patent/CN112927095A/en
Application granted granted Critical
Publication of CN112927095B publication Critical patent/CN112927095B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

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
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06315—Needs-based resource requirements planning or analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/067—Enterprise or organisation modelling
    • 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
    • H02J3/003—Load forecast, e.g. methods or systems for forecasting future load 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
    • H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
    • H02J3/08—Synchronising of networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • 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
    • 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
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/70—Wind energy
    • Y02E10/76—Power conversion electric or electronic aspects
    • 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

Landscapes

  • Business, Economics & Management (AREA)
  • Engineering & Computer Science (AREA)
  • Human Resources & Organizations (AREA)
  • Strategic Management (AREA)
  • Economics (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Development Economics (AREA)
  • Power Engineering (AREA)
  • Theoretical Computer Science (AREA)
  • Marketing (AREA)
  • Physics & Mathematics (AREA)
  • General Business, Economics & Management (AREA)
  • General Physics & Mathematics (AREA)
  • Game Theory and Decision Science (AREA)
  • Finance (AREA)
  • Accounting & Taxation (AREA)
  • Tourism & Hospitality (AREA)
  • Educational Administration (AREA)
  • Health & Medical Sciences (AREA)
  • Quality & Reliability (AREA)
  • Operations Research (AREA)
  • Public Health (AREA)
  • Water Supply & Treatment (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Data Mining & Analysis (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

The invention discloses a multi-time scale coordinated scheduling method of an electric heating combined system, belonging to the field of electricity; including load resource classification; a multi-time scale coordinated scheduling framework; and modeling the multi-time scale coordinated scheduling. The method is scientific and reasonable, has strong applicability and good effect, promotes the wind power consumption of the system and improves the operation economy of the system.

Description

Multi-time scale coordinated scheduling method for electric heating combined system
Technical Field
The invention belongs to the field of electricity, and particularly relates to a multi-time scale coordinated scheduling method of an electric heating combined system considering multi-type demand response, which is used for solving the problem of system wind abandon caused by wind-heat contradiction in northern areas of China.
Background
With the increasing exhaustion of fossil energy and the outstanding environmental issues arising from the derivation, the development and utilization of renewable energy sources are receiving much attention. By 6 months end in 2019, the accumulated installed capacity of wind power in China reaches 1.93 hundred million kilowatts, wherein the installed capacity is newly increased to 909 million kilowatts in the last half of 2019. However, in the rapid development of wind power, the problem of wind power consumption is particularly prominent. According to the statistics of the national energy agency, the average wind power utilization hours in the whole country in the first half of 2019 is 1133 hours, and the wind abandoning amount reaches 10.5 hundred million kilowatt hours in the first six months.
Due to uncertainty of wind power output, the system faces a severe problem of wind power absorption and peak clipping and valley filling after large-scale wind power access. In addition, the northern area has higher heating demand, the cogeneration unit operates in a 'fixed power by heat' model, and the contradiction exists between the thermoelectric coupling relation and the new energy grid connection, so that the insufficient peak regulation capacity is more effective.
In order to solve the contradiction between wind power and a heat supply unit, the constraint of 'fixing electricity by heat' of a cogeneration unit can be decoupled by configuring an electric boiler and a heat storage device, although the existing research relates to the electric boiler and the corresponding heat storage device, and introduces the scheduling and operating technology of the thermoelectric device to solve the contradiction of wind heat, the scheduling of a thermoelectric system is not considered by considering the resource on the load side, and the flexibility is lacked.
Disclosure of Invention
The invention aims to improve the running economy of a system while promoting the wind power consumption of the system, and relates to a multi-time scale coordinated scheduling method of an electric heating combined system in consideration of multi-type demand response.
In order to achieve the purpose, the specific technical scheme of the multi-time scale coordinated scheduling method of the electric heating combined system is as follows:
a multi-time scale coordinated scheduling method of an electric heating combined system comprises the following steps which are sequentially carried out:
1) load resource classification
Load-side resources are largely divided into incentive-based demand responses and price-based demand responses, which can respond to grid demand on different time scales.
Price-based demand response (PDR) refers to the grid company guiding users to adjust electricity usage plans according to time of use electricity prices. Therefore, the time-of-use electricity price needs to be set in the day-ahead schedule, and the user can adjust the electricity utilization plan of one day in advance according to the electricity prices at different times.
Incentive based demand response (IDR) management is mainly interruptible load, direct load control, demand side bidding, emergency demand side response, etc. The load resource needs to meet the own power demand when participating in the operation of the power grid, so that a certain response delay time is provided when participating in the scheduling of the power grid, and the load resource needs to be informed in advance.
The load aggregator is an intermediate link for coordinating DR resources and a power grid dispatching center, internally coordinates various DR resources to respond to power grid dispatching information, externally only reflects external characteristics of a load group, and gives out an integral control instruction.
The power grid company and the load aggregator sign a contract, and can directly manage and call part of IDR resources in the scheduling process, wherein the scheduling framework is shown in figure 1, and table 1 is load resource classification.
The IDRs are classified according to the length of time the user is notified in advance:
a) IDR (class a IDR) of the user such as interruptible load with long partial response time is notified 24h in advance;
b) the IDR (IDR of type B) of the user is informed 15min-4h in advance, such as part of interruptible load with short response time and the like;
c) the IDR (IDR class C) of the user is informed 5min-15min in advance, such as direct load control.
2) Multi-time scale coordinated scheduling framework
The prediction precision of the new energy is gradually improved along with the continuous approach of the time scale, the shorter the time scale is, the smaller the prediction error is, and the smaller the uncertainty disturbance brought to the system is. Therefore, the load resources are scheduled in multiple time scales, and the new energy receiving capacity is improved by realizing scheduling on different time scales in a multi-time scale step-by-step coordination and refinement mode. The method comprises the following specific steps:
(a) the day-ahead scheduling time scale is 1h, a scheduling plan is made 24h in advance, and the day-ahead wind power and load prediction data are used for determining the start-stop plan of each unit;
(b) the scheduling time scale in the day is 15min, the scheduling plan is made 1h in advance, the wind power and load prediction data in the day are used, and the last level rolling scheduling only arranges the plan of the last 4h of the day;
(c) the real-time scheduling time scale is 5min, the scheduling plan is made in advance for 15min, and the output plan of each unit is determined by using real-time wind power and load prediction data.
FIG. 2 is a day ahead, day in and real-time scheduling framework. The multi-time scale scheduling steps over 24h a day as time goes by.
3) Multi-time scale coordinated scheduling modeling
(3.1) day-ahead scheduling model
In the day-ahead scheduling model, the minimum total system cost is taken as an optimization target.
Figure BDA0002890982750000031
Figure BDA0002890982750000032
In the formula, F1Scheduling an objective function for the day ahead; cn i,t、
Figure BDA0002890982750000033
Cw t、Ct IDRThe operation cost of the thermal power unit, the wind abandoning penalty cost and the IDR resource calling cost are obtained; n is a radical ofnThe number of the conventional thermal power generating units is; n is a radical ofhThe number of the cogeneration units is; pn i,tThe output power of the ith conventional thermal power generating unit at the moment t is obtained; a isi、bi、ciThe cost coefficient of the ith conventional thermal power generating unit is obtained; a isj、bj、cjGenerating cost coefficient of the jth cogeneration unit; ph i,tThe output power of the jth combined heat and power generation unit at the moment t; hj,tThe thermal power of the jth combined heat and power generation unit in the t period; alpha is alphajThe equivalent electric output coefficient is the thermal output of the thermoelectric unit; siThe starting and stopping cost of the ith thermal power generating unit is calculated; sjThe start-stop cost of the jth cogeneration unit is calculated; u. ofn i,tStarting and stopping the ith thermal power generating unit at the moment t;
Figure BDA0002890982750000041
starting and stopping a jth thermoelectric unit at the moment t; 0. 1 is a closed and running state; pw tThe output work of the wind power in the time period t is obtained; lambda [ alpha ]wPunishing a cost coefficient for wind abandonment; pt w,maxThe predicted power of the wind power day in the time period t is obtained; lambda [ alpha ]IDRA、λIDRBAnd λIDRCPaying costs for invocation of class A, class B and class C IDR loads; delta IDRAt、△IDRBtAnd Δ IDRCtThe call volume of IDR loads of A type, B type and C type in t period.
Constraints of combined electric and heat systems
Real-time balance constraint of electric power:
Figure BDA0002890982750000042
in the formula, Pahead load,tThe predicted value of the electric load at the moment t is the day ahead; delta PDRtThe load response after PDR at time t.
Electric power constraint of a thermal power generating unit and a cogeneration unit:
Figure BDA0002890982750000043
Figure BDA0002890982750000044
in the formula, Pn i,min、Pn i,maxThe lower limit/upper limit of the electric power of the ith thermal power generating unit;
Figure BDA0002890982750000045
the lower/upper limit of the electric power of the jth cogeneration unit.
And the thermal power generating unit and the cogeneration unit are restrained by climbing.
Figure BDA0002890982750000046
Figure BDA0002890982750000047
In the formula, Rn d、Rn uThe upward/downward climbing speed of the thermal power generating unit i is obtained; rh d、Rh uIs the up/down ramp rate of the cogeneration unit j.
The method comprises the following steps of (1) starting and stopping time constraint of a thermal power generating unit and a cogeneration unit:
Figure BDA0002890982750000048
Figure BDA0002890982750000051
in the formula, Tn s、Tn oThe minimum shutdown and startup time of the thermal power generating unit is obtained; t ish s、Th oThe minimum shutdown and startup time of the cogeneration unit.
And (4) standby constraint.
For the uncertainty issues involved in scheduling, the opportunity constraints are addressed here, so the standby constraints of the day-ahead scheduling on the system are as shown in equation (10):
Figure BDA0002890982750000052
in the formula, Cr { } is a confidence function for which the inequality holds; beta is a1A confidence level that is satisfied by the backup constraints in the day-ahead scheduling; and w and v are the spare capacity coefficients left by the load and the wind power respectively.
Demand response constraints.
The PDR mainly uses time-of-use electricity prices to guide the user to adjust the electricity usage plan, and therefore, the elastic matrix E is often used to represent the relationship between the rate of change of electricity prices and the load response rate as follows:
Figure BDA0002890982750000053
in the formula, λ△PDR,tLoad rate of change for a period t; lambda [ alpha ]△load,tRate of change of electricity price for time period t; e is an elastic matrix, the main diagonal line of the elastic matrix is a self-elastic parameter, and the auxiliary diagonal line of the elastic matrix is a mutual elastic parameter.
The load response after PDR is as shown in formula (12):
ΔPPDR,t＝λΔload,tPload,t (12)
electric boiler electric power constraints.
Figure BDA0002890982750000061
In the formula (I), the compound is shown in the specification,
Figure BDA0002890982750000062
is the rated power of the mth electric boiler.
Wind power output restraint:
0≤Pt w≤Pt w,max (14)
the IDR call volume is limited by the response speed and the response capacity, so the IDR resources are constrained as follows:
Figure BDA0002890982750000063
Figure BDA0002890982750000064
in the formula, IDRAmax、IDRBmax、IDRCmaxThe maximum call volume of IDR loads of A type, B type and C type at each moment is obtained; rIDRA、RIDRB、RIDRCThe response rate of IDR load of A class, B class and C class.
And (6) power flow constraint.
The method adopts a direct current power flow constraint model and describes the power flow of each branch by introducing a generator output power transfer distribution factor matrix G.
Figure BDA0002890982750000065
In the formula, Gl-iIs the effect of the input power at node i on line l; gl-jIs the effect of the input power at node j on line l; pl,max、Pl,minMaximum and minimum transmission capacities of line l; pi,tThe output of the unit at the node i in the time period t is obtained; pd,tThe load demand of node d during time t.
And (4) heat power balance constraint.
Figure BDA0002890982750000071
In the formula, Hj,tThe heating power of the mth electric boiler in the t period is obtained; htThe heating load for the period t.
And (4) heat power constraint of the cogeneration unit.
Figure BDA0002890982750000072
In the formula (I), the compound is shown in the specification,
Figure BDA0002890982750000073
the lower limit/upper limit of the thermal power of the jth cogeneration unit.
Electric boiler restraint.
Figure BDA0002890982750000074
In the formula etamThe heating efficiency of the mth electric boiler.
Thermoelectric coupling constraints of a cogeneration unit.
Figure BDA0002890982750000075
In the formula (I), the compound is shown in the specification,
Figure BDA0002890982750000076
-thermoelectric ratio, K, of cogeneration unit j under back pressure conditionsjIs a constant.
(3.2) scheduling model in day
In the day scheduling model, the lowest total system cost is taken as an optimization target.
Figure BDA0002890982750000077
In the formula, T2-number of time segments of the scheduling phase.
A constraint condition.
The standby constraint of the system in intra-day scheduling is as shown in equation (22):
Figure BDA0002890982750000078
in the formula, beta2Confidence that the backup constraints are satisfied in the intra-day schedule.
Considering that the day-to-day dispatching plan is made on the basis of the day-to-day dispatching plan, and setting output deviation constraint for better connection of the dispatching plan:
Figure BDA0002890982750000081
in the formula, Pi,t n,ahaed is the scheduled output of the ith thermal power generating unit before the day at the time period t; pi,t h,ahaedDispatching output for the jth cogeneration unit in the day of the t time period; pi,t n,rollScheduling output for the diurnal movement of the ith thermal power generating unit in a t period; pj,t h,rollDispatching output for the jth cogeneration unit in the day of the t time period; delta1、ζ1Is a constraint factor; pn i,maxThe output limit is the upper limit of the thermal power generating unit;
Figure BDA0002890982750000082
and the output upper limit of the cogeneration unit.
The remaining constraints in the intra-day scheduling model, such as grid constraints, heat supply network constraints, and coupling constraints, are consistent with the day-ahead scheduling.
(3.3) real-time scheduling model
An objective function:
the real-time scheduling model aims at the highest yield per time interval.
Figure BDA0002890982750000083
Figure BDA0002890982750000084
In the formula, T3The number of time segments of the real-time scheduling stage; ci,t n,roll、Ci,t n,realScheduling coal consumption cost for the ith thermal power generating unit in real time within the day of the t period; ci,t h,roll、Ci,t h,realScheduling coal consumption for jth cogeneration unit in real time within day of t time periodAnd (4) cost.
Constraint conditions are as follows:
the standby constraint of the system in real-time scheduling is as shown in equation (27):
Figure BDA0002890982750000091
as with the daily schedule, the real-time schedule formulation also takes into account the unit output deviation constraints:
Figure BDA0002890982750000092
in the formula, Pi,t n,realOutput is scheduled for the ith thermal power generating unit in real time at a time period t; pi,t h,realAnd (4) dispatching output of the jth cogeneration unit in real time in the t period.
The remaining constraints in the real-time scheduling model are consistent with the foregoing.
The multi-time scale coordinated scheduling method of the electric heating combined system has the following advantages: the method is scientific and reasonable, has strong applicability and good effect, and improves the running economy of the system while promoting the wind power consumption of the system.
Drawings
FIG. 1 is a wind power consumption scheduling framework of an IDR participating electric heating combined system.
FIG. 2 is a day ahead, day in and real-time scheduling framework.
Fig. 3 is a flowchart of a rolling schedule plan implementation.
Fig. 4 is a diagram of an improved IEEE-30 node system.
Fig. 5 is a load and wind power prediction curve.
FIG. 6 shows wind power consumption in each scene.
Fig. 7 shows the scheduling result of model 2.
FIG. 8 illustrates the DR resource invocation scenario for model 3.
Fig. 9 shows the scheduling result of model 3.
FIG. 10 illustrates the DR resource invocation scenario for model 3.
Detailed Description
In order to better understand the purpose, structure and function of the present invention, the following describes a multi-time scale coordinated scheduling method of an electric heating combination system in further detail with reference to the accompanying drawings.
A multi-time scale coordinated scheduling method of an electric heating combined system comprises the following steps which are sequentially carried out:
1) load resource classification
Load-side resources are largely divided into incentive-based demand responses and price-based demand responses, which can respond to grid demand on different time scales.
Price-based demand response (PDR) refers to the grid company guiding users to adjust electricity usage plans according to time of use electricity prices. Therefore, the time-of-use electricity price needs to be set in the day-ahead schedule, and the user can adjust the electricity utilization plan of one day in advance according to the electricity prices at different times.
Incentive based demand response (IDR) management is mainly interruptible load, direct load control, demand side bidding, emergency demand side response, etc. The load resource needs to meet the own power demand when participating in the operation of the power grid, so that a certain response delay time is provided when participating in the scheduling of the power grid, and the load resource needs to be informed in advance.
The load aggregator is an intermediate link for coordinating DR resources and a power grid dispatching center, internally coordinates various DR resources to respond to power grid dispatching information, externally only reflects external characteristics of a load group, and gives out an integral control instruction.
The power grid company and the load aggregator sign a contract, and can directly manage and call part of IDR resources in the scheduling process, wherein the scheduling framework is shown in figure 1, and table 1 is load resource classification.
The IDRs are classified according to the length of time the user is notified in advance:
a) IDR (class a IDR) of the user such as interruptible load with long partial response time is notified 24h in advance;
b) the IDR (IDR of type B) of the user is informed 15min-4h in advance, such as part of interruptible load with short response time and the like;
c) the IDR (IDR class C) of the user is informed 5min-15min in advance, such as direct load control.
TABLE 1 load resource Classification
Figure BDA0002890982750000101
Figure BDA0002890982750000111
2) Multi-time scale coordinated scheduling framework
The prediction precision of the new energy is gradually improved along with the continuous approach of the time scale, the shorter the time scale is, the smaller the prediction error is, and the smaller the uncertainty disturbance brought to the system is. Therefore, the load resources are scheduled in multiple time scales, and the new energy receiving capacity is improved by realizing scheduling on different time scales in a multi-time scale step-by-step coordination and refinement mode. The method comprises the following specific steps:
(a) the day-ahead scheduling time scale is 1h, a scheduling plan is made 24h in advance, and the day-ahead wind power and load prediction data are used for determining the start-stop plan of each unit;
(b) the scheduling time scale in the day is 15min, the scheduling plan is made 1h in advance, the wind power and load prediction data in the day are used, and the last level rolling scheduling only arranges the plan of the last 4h of the day;
(c) the real-time scheduling time scale is 5min, the scheduling plan is made in advance for 15min, and the output plan of each unit is determined by using real-time wind power and load prediction data.
FIG. 2 is a day ahead, day in and real-time scheduling framework. The multi-time scale scheduling steps over 24h a day as time goes by.
3) Multi-time scale coordinated scheduling modeling
(3.1) day-ahead scheduling model
In the day-ahead scheduling model, the minimum total system cost is taken as an optimization target.
Figure BDA0002890982750000112
Figure BDA0002890982750000121
In the formula, F1Scheduling an objective function for the day ahead; cn i,t、
Figure BDA0002890982750000122
Cw t、Ct IDRThe operation cost of the thermal power unit, the wind abandoning penalty cost and the IDR resource calling cost are obtained; n is a radical ofnThe number of the conventional thermal power generating units is; n is a radical ofhThe number of the cogeneration units is; pn i,tThe output power of the ith conventional thermal power generating unit at the moment t is obtained; a isi、bi、ciThe cost coefficient of the ith conventional thermal power generating unit is obtained; a isj、bj、cjGenerating cost coefficient of the jth cogeneration unit; ph i,tThe output power of the jth combined heat and power generation unit at the moment t; hj,tThe thermal power of the jth combined heat and power generation unit in the t period; alpha is alphajThe equivalent electric output coefficient is the thermal output of the thermoelectric unit; siThe starting and stopping cost of the ith thermal power generating unit is calculated; sjThe start-stop cost of the jth cogeneration unit is calculated; u. ofn i,tStarting and stopping the ith thermal power generating unit at the moment t;
Figure BDA0002890982750000123
starting and stopping a jth thermoelectric unit at the moment t; 0. 1 is a closed and running state; pw tThe output work of the wind power in the time period t is obtained; lambda [ alpha ]wPunishing a cost coefficient for wind abandonment; pt w,maxThe predicted power of the wind power day in the time period t is obtained; lambda [ alpha ]IDRA、λIDRBAnd λIDRCPaying costs for invocation of class A, class B and class C IDR loads; delta IDRAt、△IDRBtAnd Δ IDRCtInvocation of IDR loads of A, B and C classes in t periodAmount of the compound (A).
Constraints of combined electric and heat systems
Real-time balance constraint of electric power:
Figure BDA0002890982750000124
in the formula, Pahead load,tThe predicted value of the electric load at the moment t is the day ahead; delta PDRtThe load response after PDR at time t.
Electric power constraint of a thermal power generating unit and a cogeneration unit:
Figure BDA0002890982750000125
Figure BDA0002890982750000126
in the formula, Pn i,min、Pn i,maxThe lower limit/upper limit of the electric power of the ith thermal power generating unit;
Figure BDA0002890982750000131
the lower/upper limit of the electric power of the jth cogeneration unit.
And the thermal power generating unit and the cogeneration unit are restrained by climbing.
Figure BDA0002890982750000132
Figure BDA0002890982750000133
In the formula, Rn d、Rn uThe upward/downward climbing speed of the thermal power generating unit i is obtained; rh d、Rh uIs the up/down ramp rate of the cogeneration unit j.
The method comprises the following steps of (1) starting and stopping time constraint of a thermal power generating unit and a cogeneration unit:
Figure BDA0002890982750000134
Figure BDA0002890982750000135
in the formula, Tn s、Tn oThe minimum shutdown and startup time of the thermal power generating unit is obtained; t ish s、Th oThe minimum shutdown and startup time of the cogeneration unit.
And (4) standby constraint.
For the uncertainty issues involved in scheduling, the opportunity constraints are addressed here, so the standby constraints of the day-ahead scheduling on the system are as shown in equation (10):
Figure BDA0002890982750000136
in the formula, Cr { } is a confidence function for which the inequality holds; beta is a1A confidence level that is satisfied by the backup constraints in the day-ahead scheduling; and w and v are the spare capacity coefficients left by the load and the wind power respectively.
Demand response constraints.
The PDR mainly uses time-of-use electricity prices to guide the user to adjust the electricity usage plan, and therefore, the elastic matrix E is often used to represent the relationship between the rate of change of electricity prices and the load response rate as follows:
Figure BDA0002890982750000141
in the formula, λ△PDR,tLoad rate of change for a period t; lambda [ alpha ]△load,tRate of change of electricity price for time period t; e is an elastic matrix, the main diagonal line of the elastic matrix is a self-elastic parameter, and the auxiliary diagonal line of the elastic matrix is a mutual elastic parameter.
The load response after PDR is as shown in formula (12):
ΔPPDR,t＝λΔload,tPload,t (12)
electric boiler electric power constraints.
Figure BDA0002890982750000142
In the formula (I), the compound is shown in the specification,
Figure BDA0002890982750000143
is the rated power of the mth electric boiler.
Wind power output restraint:
0≤Pt w≤Pt w,max (14)
the IDR call volume is limited by the response speed and the response capacity, so the IDR resources are constrained as follows:
Figure BDA0002890982750000144
Figure BDA0002890982750000145
in the formula, IDRAmax、IDRBmax、IDRCmaxThe maximum call volume of IDR loads of A type, B type and C type at each moment is obtained; rIDRA、RIDRB、RIDRCThe response rate of IDR load of A class, B class and C class.
And (6) power flow constraint.
The method adopts a direct current power flow constraint model and describes the power flow of each branch by introducing a generator output power transfer distribution factor matrix G.
Figure BDA0002890982750000151
In the formula，Gl-iIs the effect of the input power at node i on line l; gl-jIs the effect of the input power at node j on line l; pl,max、Pl,minMaximum and minimum transmission capacities of line l; pi,tThe output of the unit at the node i in the time period t is obtained; pd,tThe load demand of node d during time t.
And (4) heat power balance constraint.
Figure BDA0002890982750000152
In the formula, Hj,tThe heating power of the mth electric boiler in the t period is obtained; htThe heating load for the period t.
And (4) heat power constraint of the cogeneration unit.
Figure BDA0002890982750000153
In the formula (I), the compound is shown in the specification,
Figure BDA0002890982750000154
the lower limit/upper limit of the thermal power of the jth cogeneration unit.
Electric boiler restraint.
Figure BDA0002890982750000155
In the formula etamThe heating efficiency of the mth electric boiler.
Thermoelectric coupling constraints of a cogeneration unit.
Figure BDA0002890982750000156
In the formula (I), the compound is shown in the specification,
Figure BDA0002890982750000161
-cogeneration unit j is at backpressureThermoelectric ratio under operating conditions, KjIs a constant.
(3.2) scheduling model in day
In the day scheduling model, the lowest total system cost is taken as an optimization target.
Figure BDA0002890982750000162
In the formula, T2-number of time segments of the scheduling phase.
A constraint condition.
The standby constraint of the system in intra-day scheduling is as shown in equation (22):
Figure BDA0002890982750000163
in the formula, beta2Confidence that the backup constraints are satisfied in the intra-day schedule.
Considering that the day-to-day dispatching plan is made on the basis of the day-to-day dispatching plan, and setting output deviation constraint for better connection of the dispatching plan:
Figure BDA0002890982750000164
in the formula, Pi,t n,ahaed is the scheduled output of the ith thermal power generating unit before the day at the time period t; pi,t h,ahaedDispatching output for the jth cogeneration unit in the day of the t time period; pi,t n,rollScheduling output for the diurnal movement of the ith thermal power generating unit in a t period; pj,t h,rollDispatching output for the jth cogeneration unit in the day of the t time period; delta1、ζ1Is a constraint factor; pn i,maxThe output limit is the upper limit of the thermal power generating unit;
Figure BDA0002890982750000165
and the output upper limit of the cogeneration unit.
The remaining constraints in the intra-day scheduling model, such as grid constraints, heat supply network constraints, and coupling constraints, are consistent with the day-ahead scheduling.
(3.3) real-time scheduling model
The real-time scheduling model aims at the highest yield in a single time interval, and the objective function is as follows:
Figure BDA0002890982750000171
Figure BDA0002890982750000172
in the formula, T3The number of time segments of the real-time scheduling stage; ci,t n,roll、Ci,t n,realScheduling coal consumption cost for the ith thermal power generating unit in real time within the day of the t period; ci,t h,roll、Ci,t h,realAnd (5) scheduling the coal consumption cost for the jth cogeneration unit in real time within the day of the t time period.
Constraint conditions are as follows:
the standby constraint of the system in real-time scheduling is as shown in equation (27):
Figure BDA0002890982750000173
as with the daily schedule, the real-time schedule formulation also takes into account the unit output deviation constraints:
Figure BDA0002890982750000174
in the formula, Pi,t n,realOutput is scheduled for the ith thermal power generating unit in real time at a time period t; pi,t h,realAnd (4) dispatching output of the jth cogeneration unit in real time in the t period.
The remaining constraints in the real-time scheduling model are consistent with the foregoing.
The multi-time scale coordinated scheduling of the electric-thermal combined system considering the multi-type demand response is realized through the process, the scheduling model is optimized through Yalmip calling optimization software Cplex in MATALB according to a mixed integer programming theory, and a model solving flow chart is shown in FIG. 3.
An example simulation was performed using the modified IEEE-30 system, and the structure of the modified IEEE-30 system is shown in fig. 4. The model comprises 3 thermal power generating units and 3 thermal power generating units, and 1 wind power station is connected to a node 8; the class a, class B and class C IDR load aggregators are located at nodes 7, 10, 16, respectively. Values of the self-elastic coefficient and the mutual-elastic coefficient of the PDR are assumed to be-0.2 and 0.033 respectively. The capacity of the invokable class a, class B and class C IDRs in the system is no more than 5%, 3% and 1% of the total load, respectively.
The electrical load and wind power prediction curves are shown in fig. 5, and in order to study the influence of wind power uncertainty on scheduling, the prediction curves are obtained by adding disturbance on the basis of real load and wind power curves. The prediction precision of the wind power and load curves in the day-ahead, day-in and real-time scheduling stages is gradually improved. The wind power and load prediction errors in the day-ahead, day-in and real-time stages are assumed to be 20%, 5%, 2%, 3%, 1% and 0.5%, respectively.
And analyzing the participation of the load resources on a day-ahead scale, the wind power consumption capacity of the electric heating combined system and the influence on the economy of the system.
The following 3 scenarios of comparison of results were performed.
Scene 1: not considering the participation of the demand response and the electric boiler;
scene 2: the participation of an electric boiler is considered, and the participation of demand response is not considered;
scene 3: the demand response and the electric boiler are both participated, namely the day-ahead scheduling.
TABLE 2
Figure BDA0002890982750000181
Fig. 6 shows the wind power consumption rate of each scene system, as shown in fig. 6, in the time periods of 02: 00-06: 00 and 22: 00-24: 00, the wind abandon phenomenon of scene 1 is serious due to the contradiction of wind heat, the wind power consumption rate does not exceed 90%, and in scene 2 and scene 3, the wind power consumption rate is improved by introducing an electric boiler for thermoelectric decoupling. Particularly, in a scene 3, the electric boiler and the demand response are considered to jointly participate in wind power to obtain full consumption, and the problem of system wind abandon caused by wind-heat contradiction is effectively solved.
As can be seen from the data in Table 2, scene 3 comprehensively considers the participation of the electric boiler and the demand response, the penalty cost of abandoned wind is lowest, and the wind power is completely consumed. Meanwhile, scene 3 reduces the coal consumption cost of the fire and thermoelectric unit while consuming the abandoned wind and abandoned light. In a comprehensive view, when the wind power curve is of a strong inverse peak regulation characteristic, compared with the scene 1, the comprehensive cost of the system of the scenes 2 and 3 is respectively reduced by 64.09 percent and 72.39 percent.
Through the above example analysis, the scheduling strategy provided by the method has certain effectiveness in improving the wind power consumption capability and reducing the comprehensive cost of the system in the day ahead.
The following 3 scheduling models were compared:
model 1: and (5) scheduling the model day ahead. And determining the output of thermal power units, thermoelectric power units and new energy and the scheduling conditions of various DR resources at the stage.
Model 2: scheduling model before and in day. And adjusting the adjustment amount of the B-type IDR and the output of each unit and new energy on the basis of a day-ahead scheduling model.
Model 3: the scheduling model is a scheduling model in the day-ahead, day-in and real-time modes, namely the scheduling model provided by the article.
The scheduling result of model 1 has been analyzed previously, and thus is not described in detail. Fig. 7 and 8 show the scheduling result and demand response resource scheduling situation of model 2, respectively, and fig. 9 and 10 show the scheduling result and demand response resource scheduling situation of model 3, respectively.
The model 2 shows that the class-B IDR is mainly used for peak clipping and valley filling and stabilizing wind power fluctuation, has large influence on users and has high scheduling cost.
Model 3 shows that the C-class IDR responds fastest in the scheduling period, needs to be frequently called to relieve the wind power fluctuation in a short time, and the fluctuation amplitude is small but changes very severely. With the improvement of the accuracy of the wind power and load prediction data, the output of the generator set needs to be adjusted to adapt to the wind power and load prediction data.
It is to be understood that the present invention has been described with reference to certain embodiments, and that various changes in the features and embodiments, or equivalent substitutions may be made therein by those skilled in the art without departing from the spirit and scope of the invention. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the invention without departing from the essential scope thereof. Therefore, it is intended that the invention not be limited to the particular embodiment disclosed, but that the invention will include all embodiments falling within the scope of the appended claims.

Claims (4)

1. A multi-time scale coordinated scheduling method of an electric heating combined system is characterized by comprising the following steps which are sequentially carried out:
step S1, classifying load resources, classifying IDRs according to the time length for informing users in advance, and informing IDRs of users 24h in advance, and marking the IDRs as A-type IDRs; the IDR of the user is informed 15min-4h in advance and is recorded as the B-type IDR; informing the IDR of the user 5-15 min in advance, and recording as the type C IDR;
step S2, establishing a multi-time scale coordinated scheduling framework, wherein the day-ahead scheduling time scale is 1h, a scheduling plan is made 24h in advance, and the day-ahead wind power and load prediction data are used for determining the start-stop plan of each unit; the scheduling time scale in the day is 15min, the scheduling plan is made 1h in advance, the wind power and load prediction data in the day are used, and the last level rolling scheduling only arranges the plan of the last 4h of the day; the real-time scheduling time scale is 5min, the scheduling plan is made in advance for 15min, and the output plan of each unit is determined by using real-time wind power and load prediction data;
step S3, a multi-time scheduling model comprises a day-ahead scheduling model, a day-in scheduling model and a real-time scheduling model; the day-ahead scheduling model takes the minimum total system cost as an optimization target, the in-day scheduling model takes the minimum total system cost as an optimization target, and the real-time scheduling model takes the highest yield in a single period as a target.
2. The multi-time scale coordinated scheduling method of the electric-thermal combined system according to claim 1, wherein an objective function of the day-ahead scheduling model is as follows:
Figure FDA0002890982740000011
Figure FDA0002890982740000012
in the formula, F1Scheduling an objective function for the day ahead; cn i,t、Ch j,t、Cw t、Ct IDRThe operation cost of the thermal power unit, the wind abandoning penalty cost and the IDR resource calling cost are obtained; n is a radical ofnThe number of the conventional thermal power generating units is; n is a radical ofhThe number of the cogeneration units is; pn i,tThe output power of the ith conventional thermal power generating unit at the moment t is obtained; a isi、bi、ciThe cost coefficient of the ith conventional thermal power generating unit is obtained; a isj、bj、cjGenerating cost coefficient of the jth cogeneration unit; ph i,tThe output power of the jth combined heat and power generation unit at the moment t; hj,tThe thermal power of the jth combined heat and power generation unit in the t period; alpha is alphajThe equivalent electric output coefficient is the thermal output of the thermoelectric unit; siThe starting and stopping cost of the ith thermal power generating unit is calculated; sjThe start-stop cost of the jth cogeneration unit is calculated; u. ofn i,tStarting and stopping the ith thermal power generating unit at the moment t; u. ofh j,tStarting and stopping a jth thermoelectric unit at the moment t; 0. 1 is a closed and running state; pw tThe output work of the wind power in the time period t is obtained; lambda [ alpha ]wPunishing a cost coefficient for wind abandonment; pt w,maxThe predicted power of the wind power day in the time period t is obtained; lambda [ alpha ]IDRA、λIDRBAnd λIDRCPaying costs for invocation of class A, class B and class C IDR loads; delta IDRAt、△IDRBtAnd Δ IDRCtThe call volume of IDR loads of A type, B type and C type in t period;
constraints of combined electric and heat systems
Real-time balance constraint of electric power:
Figure FDA0002890982740000021
in the formula, Pahead load,tThe predicted value of the electric load at the moment t is the day ahead; delta PDRtLoad response after PDR at time t;
electric power constraint of a thermal power generating unit and a cogeneration unit:
Figure FDA0002890982740000022
Figure FDA0002890982740000023
in the formula, Pn i,min、Pn i,maxThe lower limit/upper limit of the electric power of the ith thermal power generating unit; ph j,min、Ph j,maxThe lower limit/upper limit of the electric power of the jth cogeneration unit;
and (3) climbing restraint of the thermal power generating unit and the cogeneration unit:
Figure FDA0002890982740000024
Figure FDA0002890982740000025
in the formula, Rn d、Rn uThe upward/downward climbing speed of the thermal power generating unit i is obtained; rh d、Rh uThe upward/downward climbing rate of the cogeneration unit j;
the method comprises the following steps of (1) starting and stopping time constraint of a thermal power generating unit and a cogeneration unit:
Figure FDA0002890982740000031
Figure FDA0002890982740000032
in the formula, Tn s、Tn oThe minimum shutdown and startup time of the thermal power generating unit is obtained; t ish s、Th oThe minimum shutdown and startup time of the cogeneration unit;
standby constraint:
for the uncertainty issues involved in scheduling, the opportunity constraints are addressed here, so the standby constraints of the day-ahead scheduling on the system are as shown in equation (10):
Figure FDA0002890982740000033
in the formula, Cr { } is a confidence function for which the inequality holds; beta is a1A confidence level that is satisfied by the backup constraints in the day-ahead scheduling; w and v are respectively the spare capacity coefficients reserved by the load and the wind power;
and (3) constraint of demand response:
the PDR mainly uses time-of-use electricity prices to guide the user to adjust the electricity usage plan, and therefore, the elastic matrix E is often used to represent the relationship between the rate of change of electricity prices and the load response rate as follows:
Figure FDA0002890982740000041
in the formula, λ△PDR,tLoad rate of change for a period t; lambda [ alpha ]△load,tRate of change of electricity price for time period t; e is an elastic matrix, the main diagonal line of the elastic matrix is an auto-elasticity parameter, and the auxiliary diagonal line of the elastic matrix is a mutual elasticity parameter;
the load response after PDR is as shown in formula (12):
ΔPPDR,t＝λΔload,tPload,t (12)
electric boiler electric power constraint;
Figure FDA0002890982740000042
in the formula (I), the compound is shown in the specification,
Figure FDA0002890982740000043
rated power of the mth electric boiler;
wind power output restraint:
0≤Pt w≤Pt w,max (14)
the IDR call volume is limited by the response speed and the response capacity, so the IDR resources are constrained as follows:
Figure FDA0002890982740000044
Figure FDA0002890982740000045
in the formula, IDRAmax、IDRBmax、IDRCmaxThe maximum call volume of IDR loads of A type, B type and C type at each moment is obtained; rIDRA、RIDRB、RIDRCThe response rate of IDR load of A class, B class and C class;
and (3) power flow constraint:
the method adopts a direct current power flow constraint model, and describes the power flow of each branch by introducing a generator output power transfer distribution factor matrix G;
Figure FDA0002890982740000051
in the formula, Gl-iIs the effect of the input power at node i on line l; gl-jIs the effect of the input power at node j on line l; pl,max、Pl,minMaximum and minimum transmission capacities of line l; pi,tThe output of the unit at the node i in the time period t is obtained; pd,tLoad demand for node d during time t;
and thermal power balance constraint:
Figure FDA0002890982740000052
in the formula, Hj,tThe heating power of the mth electric boiler in the t period is obtained; htA heating load for a period t;
thermal power constraint of a cogeneration unit:
Figure FDA0002890982740000053
in the formula (I), the compound is shown in the specification,
Figure FDA0002890982740000054
the lower limit/the upper limit of the thermal power of the jth cogeneration unit;
electric boiler restraint:
Figure FDA0002890982740000055
in the formula etamThe heating efficiency of the mth electric boiler is obtained;
thermoelectric coupling of cogeneration units is contracted:
Figure FDA0002890982740000056
in the formula, Cm j-thermoelectric ratio, K, of cogeneration unit j under back pressure conditionsjIs a constant.
3. The electric-thermal combined system multi-time scale coordinated scheduling method of claim 1, wherein an objective function of the in-day scheduling model is as follows:
Figure FDA0002890982740000061
in the formula, T2-number of time segments of the scheduling phase;
constraint conditions are as follows:
the standby constraint of the system in intra-day scheduling is as shown in equation (22):
Figure FDA0002890982740000062
in the formula, beta2-confidence that the backup constraints in the intra-day schedule are met;
considering that the day-to-day dispatching plan is made on the basis of the day-to-day dispatching plan, and setting output deviation constraint for better connection of the dispatching plan:
Figure FDA0002890982740000063
in the formula, Pi,t n,ahaed is the scheduled output of the ith thermal power generating unit before the day at the time period t; pi,t h,ahaedDispatching output for the jth cogeneration unit in the day of the t time period; pi,t n,rollScheduling output for the diurnal movement of the ith thermal power generating unit in a t period; pj,t h,rollDispatching output for the jth cogeneration unit in the day of the t time period; delta1、ζ1Is a constraint factor; pn i,maxThe output limit is the upper limit of the thermal power generating unit; ph j,maxAnd the output upper limit of the cogeneration unit.
4. The multi-time scale coordinated scheduling method of the electric-thermal combined system according to claim 1, wherein an objective function of the real-time scheduling model is as follows:
Figure FDA0002890982740000064
Figure FDA0002890982740000071
in the formula, T3The number of time segments of the real-time scheduling stage; ci,t n,roll、Ci,t n,realScheduling coal consumption cost for the ith thermal power generating unit in real time within the day of the t period; ci,t h,roll、Ci,t h,realScheduling coal consumption cost for the jth cogeneration unit in real time within the day of the t period;
constraint conditions are as follows:
the standby constraint of the system in real-time scheduling is as shown in equation (27):
Figure FDA0002890982740000072
as with the daily schedule, the real-time schedule formulation also takes into account the unit output deviation constraints:
Figure FDA0002890982740000073
in the formula, Pi,t n,realFor i-th thermal power generating unit in t periodDispatching output in real time; pi,t h,realAnd (4) dispatching output of the jth cogeneration unit in real time in the t period.
CN202110027877.XA 2021-01-11 2021-01-11 Multi-time scale coordinated scheduling method for electric heating combined system Active CN112927095B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110027877.XA CN112927095B (en) 2021-01-11 2021-01-11 Multi-time scale coordinated scheduling method for electric heating combined system

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110027877.XA CN112927095B (en) 2021-01-11 2021-01-11 Multi-time scale coordinated scheduling method for electric heating combined system

Publications (2)

Publication Number Publication Date
CN112927095A true CN112927095A (en) 2021-06-08
CN112927095B CN112927095B (en) 2022-04-15

Family

ID=76162818

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110027877.XA Active CN112927095B (en) 2021-01-11 2021-01-11 Multi-time scale coordinated scheduling method for electric heating combined system

Country Status (1)

Country Link
CN (1) CN112927095B (en)

Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107194514A (en) * 2017-05-27 2017-09-22 重庆大学 A kind of demand response Multiple Time Scales dispatching method for wind power prediction error
CN107563676A (en) * 2017-10-11 2018-01-09 华中科技大学 Consider the source lotus coordinated operation dispatching method of Multiple Time Scales polymorphic type demand response
CN107994571A (en) * 2017-12-04 2018-05-04 国网山东省电力公司德州供电公司 A kind of Regional Energy net Multiple Time Scales management method of the parameter containing Full Fuzzy
CN110197312A (en) * 2019-06-28 2019-09-03 东南大学 A kind of user class integrated energy system Optimization Scheduling based on Multiple Time Scales
CN110209135A (en) * 2019-06-12 2019-09-06 东北大学 Home energy source Optimization Scheduling based on minisize thermoelectric coproduction Multiple Time Scales
CN110739696A (en) * 2019-10-21 2020-01-31 华北电力大学 Integrated scheduling method for demand side resources and renewable energy in intelligent distribution network environment
CN110807588A (en) * 2019-10-31 2020-02-18 国网河南省电力公司经济技术研究院 Optimized scheduling method of multi-energy coupling comprehensive energy system
CN111277005A (en) * 2020-02-19 2020-06-12 东北电力大学 Multi-source power system multi-time scale scheduling method considering source-load coordination optimization
CN111628503A (en) * 2020-06-20 2020-09-04 东北电力大学 Day-ahead-day two-stage rolling optimization scheduling method considering generalized energy storage and thermal power combined peak shaving
CN112186802A (en) * 2020-09-28 2021-01-05 国网江苏省电力有限公司苏州供电分公司 Multi-time scale rolling scheduling method and system for dynamic economic scheduling

Patent Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107194514A (en) * 2017-05-27 2017-09-22 重庆大学 A kind of demand response Multiple Time Scales dispatching method for wind power prediction error
CN107563676A (en) * 2017-10-11 2018-01-09 华中科技大学 Consider the source lotus coordinated operation dispatching method of Multiple Time Scales polymorphic type demand response
CN107994571A (en) * 2017-12-04 2018-05-04 国网山东省电力公司德州供电公司 A kind of Regional Energy net Multiple Time Scales management method of the parameter containing Full Fuzzy
CN110209135A (en) * 2019-06-12 2019-09-06 东北大学 Home energy source Optimization Scheduling based on minisize thermoelectric coproduction Multiple Time Scales
CN110197312A (en) * 2019-06-28 2019-09-03 东南大学 A kind of user class integrated energy system Optimization Scheduling based on Multiple Time Scales
CN110739696A (en) * 2019-10-21 2020-01-31 华北电力大学 Integrated scheduling method for demand side resources and renewable energy in intelligent distribution network environment
CN110807588A (en) * 2019-10-31 2020-02-18 国网河南省电力公司经济技术研究院 Optimized scheduling method of multi-energy coupling comprehensive energy system
CN111277005A (en) * 2020-02-19 2020-06-12 东北电力大学 Multi-source power system multi-time scale scheduling method considering source-load coordination optimization
CN111628503A (en) * 2020-06-20 2020-09-04 东北电力大学 Day-ahead-day two-stage rolling optimization scheduling method considering generalized energy storage and thermal power combined peak shaving
CN112186802A (en) * 2020-09-28 2021-01-05 国网江苏省电力有限公司苏州供电分公司 Multi-time scale rolling scheduling method and system for dynamic economic scheduling

Non-Patent Citations (6)

* Cited by examiner, † Cited by third party
Title
TAO LONG 等: "Optimization Strategy of CCHP Integrated Energy System Based on Source-Load Coordination", 《2018 INTERNATIONAL CONFERENCE ON POWER SYSTEM TECHNOLOGY (POWERCON)》 *
刘宝林 等: "计及多时间尺度需求响应资源的微电网能量优化调度策略", 《电力建设》 *
包宇庆 等: "考虑大规模风电接入并计及多时间尺度需求响应资源协调优化的滚动调度模型", 《中国电机工程学报》 *
唐楠: "柔性配置需求响应参与大规模风电消纳的调度模型研究", 《中国优秀博硕士学位论文全文数据库(硕士) 工程科技Ⅱ辑》 *
杨冬锋 等: "考虑电热耦合的交直流微电网多目标优化配置", 《电力***自动化》 *
金力 等: "考虑特性分布的储能电站接入的电网多时间尺度源储荷协调调度策略", 《电网技术》 *

Also Published As

Publication number Publication date
CN112927095B (en) 2022-04-15

Similar Documents

Publication Publication Date Title
CN111277005B (en) Multi-source power system multi-time scale scheduling method considering source-load coordination optimization
CN114498639B (en) Day-ahead scheduling method of multi-microgrid combined mutual aid considering demand response
CN111697578B (en) Multi-target energy storage-containing regional power grid operation control method
CN114336702B (en) Wind-solar storage station group power distribution collaborative optimization method based on double-layer random programming
CN111293682A (en) Multi-microgrid energy management method based on cooperative model predictive control
CN107749645A (en) A kind of method for controlling high-voltage large-capacity thermal storage heating device
CN111641233A (en) Electric power system day-based flexible peak regulation method considering new energy and load uncertainty
CN115498668A (en) Optimization method of comprehensive energy system
CN116191556A (en) 5G base station demand response method and system considering new energy consumption and unit combination
Awasthi et al. Operation of datacenter as virtual power plant
CN115170343A (en) Distributed resource and energy storage collaborative planning method for regional comprehensive energy system
CN113746105A (en) Optimal control method, device, equipment and storage medium for power demand response
CN112927095B (en) Multi-time scale coordinated scheduling method for electric heating combined system
CN116544953A (en) Flexible resource robust scheduling method for large-scale data center micro-grid group
CN115936336A (en) Virtual power plant capacity configuration and regulation operation optimization method
CN116187648A (en) Virtual power plant thermoelectric combination optimization scheduling method based on thermal decoupling
CN114723278A (en) Community microgrid scheduling method and system considering photovoltaic energy storage
CN113240279B (en) Comprehensive energy system operation control method and system based on comprehensive demand side response
US20230119377A1 (en) Method for the Control of an Energy System, and Associated Device
CN115081707A (en) Micro-grid multi-time scale optimization scheduling method based on source and load flexibility
CN114389262A (en) Regional power grid scheduling method based on robust optimization in elastic environment
Zhang et al. New urban power grid flexible load dispatching architecture and key technologies
CN111523792A (en) Method for calculating scheduling parameters of comprehensive energy system, method for controlling equipment and related devices
Trairat et al. Optimal Dispatch of Cogeneration with Thermal and Battery Energy Storage for BEMS Subject to Load Demand Uncertainty
CN116826867B (en) Optimized scheduling and cost compensation allocation method for improving source load multielement peak regulation initiative

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
GR01 Patent grant
GR01 Patent grant
TR01 Transfer of patent right

Effective date of registration: 20221118

Address after: 130021 no.4799, Renmin Street, Chaoyang District, Changchun City, Jilin Province

Patentee after: ECONOMIC TECHNOLOGY RESEARCH INSTITUTE OF STATE GRID JILIN ELECTRIC POWER CO.,LTD.

Patentee after: Jilin Province Beitian Gong Software Development Co.,Ltd.

Address before: 132012, Changchun Road, Jilin, Jilin, 169

Patentee before: NORTHEAST DIANLI University

TR01 Transfer of patent right
TR01 Transfer of patent right

Effective date of registration: 20221215

Address after: 132012, Changchun Road, Jilin, Jilin, 169

Patentee after: NORTHEAST DIANLI University

Patentee after: ECONOMIC TECHNOLOGY RESEARCH INSTITUTE OF STATE GRID JILIN ELECTRIC POWER CO.,LTD.

Patentee after: Jilin Province Beitian Gong Software Development Co.,Ltd.

Address before: 130021 no.4799, Renmin Street, Chaoyang District, Changchun City, Jilin Province

Patentee before: ECONOMIC TECHNOLOGY RESEARCH INSTITUTE OF STATE GRID JILIN ELECTRIC POWER CO.,LTD.

Patentee before: Jilin Province Beitian Gong Software Development Co.,Ltd.

TR01 Transfer of patent right