CN116207748A - Regulation and control system for large-scale flexible load resources - Google Patents
Regulation and control system for large-scale flexible load resources Download PDFInfo
- Publication number
- CN116207748A CN116207748A CN202310142525.8A CN202310142525A CN116207748A CN 116207748 A CN116207748 A CN 116207748A CN 202310142525 A CN202310142525 A CN 202310142525A CN 116207748 A CN116207748 A CN 116207748A
- Authority
- CN
- China
- Prior art keywords
- load
- power
- unit
- regulation
- time
- 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.)
- Withdrawn
Links
- 238000005457 optimization Methods 0.000 claims abstract description 47
- 238000004220 aggregation Methods 0.000 claims abstract description 15
- 230000002776 aggregation Effects 0.000 claims abstract description 15
- 230000008901 benefit Effects 0.000 claims abstract description 8
- 238000004146 energy storage Methods 0.000 claims description 91
- 238000000034 method Methods 0.000 claims description 35
- 238000005096 rolling process Methods 0.000 claims description 29
- 238000007599 discharging Methods 0.000 claims description 23
- 238000010248 power generation Methods 0.000 claims description 20
- 239000011159 matrix material Substances 0.000 claims description 14
- 238000004422 calculation algorithm Methods 0.000 claims description 13
- 230000008569 process Effects 0.000 claims description 13
- 238000004378 air conditioning Methods 0.000 claims description 12
- 238000004364 calculation method Methods 0.000 claims description 9
- 230000009194 climbing Effects 0.000 claims description 9
- 238000004458 analytical method Methods 0.000 claims description 6
- 230000008859 change Effects 0.000 claims description 6
- 230000007547 defect Effects 0.000 claims description 5
- 230000005611 electricity Effects 0.000 claims description 5
- 230000001105 regulatory effect Effects 0.000 claims description 5
- 230000003068 static effect Effects 0.000 claims description 5
- 239000003245 coal Substances 0.000 claims description 4
- 238000006467 substitution reaction Methods 0.000 claims description 4
- 238000003723 Smelting Methods 0.000 claims description 3
- 238000006243 chemical reaction Methods 0.000 claims description 3
- 238000011217 control strategy Methods 0.000 claims description 3
- 238000005520 cutting process Methods 0.000 claims description 3
- 238000000354 decomposition reaction Methods 0.000 claims description 3
- 230000029087 digestion Effects 0.000 claims description 3
- 238000009826 distribution Methods 0.000 claims description 3
- 238000005516 engineering process Methods 0.000 claims description 3
- 238000011065 in-situ storage Methods 0.000 claims description 3
- 238000003754 machining Methods 0.000 claims description 3
- 239000002184 metal Substances 0.000 claims description 3
- 239000002245 particle Substances 0.000 claims description 3
- 230000000737 periodic effect Effects 0.000 claims description 3
- 230000000630 rising effect Effects 0.000 claims description 3
- 238000005070 sampling Methods 0.000 claims description 3
- 238000004088 simulation Methods 0.000 claims description 3
- 230000001360 synchronised effect Effects 0.000 claims description 3
- 238000010977 unit operation Methods 0.000 claims description 3
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 3
- NAWXUBYGYWOOIX-SFHVURJKSA-N (2s)-2-[[4-[2-(2,4-diaminoquinazolin-6-yl)ethyl]benzoyl]amino]-4-methylidenepentanedioic acid Chemical compound C1=CC2=NC(N)=NC(N)=C2C=C1CCC1=CC=C(C(=O)N[C@@H](CC(=C)C(O)=O)C(O)=O)C=C1 NAWXUBYGYWOOIX-SFHVURJKSA-N 0.000 claims description 2
- 239000003344 environmental pollutant Substances 0.000 claims description 2
- 101150036841 minJ gene Proteins 0.000 claims description 2
- 231100000719 pollutant Toxicity 0.000 claims description 2
- 238000010586 diagram Methods 0.000 description 7
- 230000009467 reduction Effects 0.000 description 6
- 238000010521 absorption reaction Methods 0.000 description 5
- 230000005284 excitation Effects 0.000 description 2
- 238000012163 sequencing technique Methods 0.000 description 2
- 240000002853 Nelumbo nucifera Species 0.000 description 1
- 235000006508 Nelumbo nucifera Nutrition 0.000 description 1
- 235000006510 Nelumbo pentapetala Nutrition 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 238000010438 heat treatment Methods 0.000 description 1
- 230000035772 mutation Effects 0.000 description 1
- 239000013589 supplement Substances 0.000 description 1
Images
Classifications
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
- H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
- H02J3/144—Demand-response operation of the power transmission or distribution network
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
-
- 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
-
- 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
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/007—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources
- H02J3/0075—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources for providing alternative feeding paths between load and source according to economic or energy efficiency considerations, e.g. economic dispatch
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
- H02J3/06—Controlling transfer of power between connected networks; Controlling sharing of load between connected 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
- H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
- H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Business, Economics & Management (AREA)
- Power Engineering (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- Mathematical Physics (AREA)
- Data Mining & Analysis (AREA)
- Health & Medical Sciences (AREA)
- Strategic Management (AREA)
- Marketing (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- General Business, Economics & Management (AREA)
- Software Systems (AREA)
- Pure & Applied Mathematics (AREA)
- Entrepreneurship & Innovation (AREA)
- Tourism & Hospitality (AREA)
- Mathematical Optimization (AREA)
- General Health & Medical Sciences (AREA)
- Mathematical Analysis (AREA)
- Computational Mathematics (AREA)
- Educational Administration (AREA)
- Quality & Reliability (AREA)
- Water Supply & Treatment (AREA)
- Public Health (AREA)
- Game Theory and Decision Science (AREA)
- Algebra (AREA)
- Development Economics (AREA)
- Databases & Information Systems (AREA)
- Operations Research (AREA)
- Primary Health Care (AREA)
- Life Sciences & Earth Sciences (AREA)
- Artificial Intelligence (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- Evolutionary Computation (AREA)
Abstract
The invention relates to a regulation and control system for large-scale flexible load resources, which comprises a resource layer, a distributed control layer, a scheduling system layer, a primary control layer and a management layer, wherein the resource layer comprises at least one edge controller unit, and the edge controller unit comprises at least one sensor unit, at least one acquisition equipment unit and at least one control equipment unit; the distributed control layer comprises at least one secondary control center unit; the scheduling system layer comprises a source load coordination scheduling system, a random optimization scheduling system, an ultra-short term regulation strategy system and a flexible load regulation system; the first-level control layer comprises at least one management control network element unit; the management layer comprises a management server, a backup management server, a database, a backup database and a cloud server; the invention has the advantages of improving the wind power consumption level, effectively coordinating the running cost and risk of the power system, reducing the wind and light abandoning and realizing the flexible load aggregation regulation.
Description
Technical Field
The invention belongs to the technical field of regulation and control systems, and particularly relates to a regulation and control system for large-scale flexible load resources.
Background
The flexible load comprises an interrupt load and an excitation load, the interrupt load and the excitation load are increasingly focused due to good peak regulation performance, renewable energy in China is developed rapidly in recent years, however, the problems of wind abandon and light abandon are still serious, the uncertainty of wind turbine and photovoltaic output influences the power generation arrangement and operation plan, the conventional large-scale wind power dispatching is to carry out 'bundling' operation on wind power in thermal power, but the probability characteristics of wind power output are not considered in the dispatching method due to the intermittence and fluctuation of wind power output, the risk level of actual operation of a system is difficult to reflect, the severity and the possibility of wind power output fluctuation cannot be coordinated comprehensively, and the requirements of large-scale wind power grid connection cannot be met by the traditional power grid operation dispatching mode and the regulation capability of a conventional unit; in addition, as the supplement of power generation scheduling, the flexible load scheduling can cut peaks and fill valleys, balance intermittent energy fluctuation and provide auxiliary services, is a regulating means for enriching power grid scheduling operation, has larger regulating potential in power grid frequency modulation and peak regulation and new energy consumption, but at present, the flexible load scheduling still has the defect of optimizing and needs to be further supplemented and perfected; therefore, it is very necessary to provide a regulation and control system for large-scale flexible load resources, which improves the wind power consumption level, effectively coordinates the running cost and risk of the power system, reduces the wind and light abandoning, and realizes the flexible load aggregation regulation and control.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a regulation and control system for large-scale flexible load resources, which is used for improving the wind power consumption level, effectively coordinating the running cost and risk of a power system, reducing the wind and light abandoning and realizing the flexible load aggregation regulation and control.
The purpose of the invention is realized in the following way: a regulation and control system for large-scale flexible load resources comprises a resource layer, a distributed control layer, a scheduling system layer, a primary control layer and a management layer, wherein the resource layer comprises at least one edge controller unit, and the edge controller unit comprises at least one sensor unit, at least one acquisition equipment unit and at least one control equipment unit; the distributed control layer comprises at least one secondary control center unit; the dispatching system layer comprises a source load coordination dispatching system, a random optimization dispatching system, an ultra-short term regulation strategy system and a flexible load regulation system; the first-level control layer comprises at least one management control network element unit; the management layer comprises a management server, a backup management server, a database, a backup database and a cloud server.
The distributed control layer is connected with the resource layer through a secondary control center unit, and the dispatching system layer is connected with the distributed control layer; the first-level control layer is connected with the management layer upwards and the dispatching system layer downwards through the management control network element unit.
The management server of the management layer is respectively connected with the backup management server, the database and the backup database; the backup management server is respectively connected with the management server, the database and the backup database; the database is respectively connected with the management server, the backup management server and the cloud server; and the backup database is respectively connected with the management server, the backup management server and the cloud server.
The management server comprises a source load coordination scheduling management unit, a random optimization scheduling management unit, an ultra-short term regulation strategy management unit and a flexible load regulation management unit; the source load coordination scheduling management unit, the random optimization scheduling management unit, the ultra-short-term regulation strategy management unit and the flexible load regulation management unit are correspondingly connected with the source load coordination scheduling system, the random optimization scheduling system, the ultra-short-term regulation strategy system and the flexible load regulation system respectively.
The source load coordination scheduling system adopts a power system multi-time scale source load coordination scheduling model containing large-scale wind power, and specifically comprises the following steps:
step 1: the source load coordination scheduling model for wind power consumption is considered, and specifically comprises the following steps: step 1.1: and (3) analyzing the source load characteristics of wind power consumption: the high energy load refers to the enterprise user load with higher energy value in the output value, and comprises machining, petrochemical industry and metal smelting; the control strategy of tracking wind power fluctuation in real time by utilizing high-energy load is utilized to promote the in-situ digestion of blocked wind power, especially in the period of down-peak regulation, the defect of insufficient peak regulation capacity of the conventional thermal power generating unit can be overcome, and the occurrence of abandoned wind is reduced; the equivalent load of the period t of the wind power system is as follows: p (P) E,t ＝P L,t -P W,t (1) Wherein P is L,t System load for period t; p (P) W,t Dispatching a force output value for the wind farm before the day of the period t; p (P) E,t Is the equivalent load of period t; the equivalent load increment for the t+1 period is: ΔP E,t+1 ＝ΔP L,t+1 -ΔP W,t+1 ＝(P L,t+1 -P L,t )-(P W,t+1 -P W,t ) (2) when the load demand of the time period t+1 increases greatly and the wind power dispatching output is obviously reduced, the following situations may occur:
in (1) the->
The maximum increment of the output force of the grid-connected thermal power generating unit in the t+1 period is set; u (u) i,t+1 For the start-stop state of the thermal power generating unit i in the period t+1, u i,t+1 =1 denotes the on state, u i,t+1 =0 indicates a shutdown state; p (P) i,t Active output of the unit i in a period t; u (U) i The rising climbing speed of the active force of the unit i is set; n is the number of thermal power generating units; Δt is the duration of a certain period;
step 2: and (3) solving a wind power-containing unit combination: the uncertainty of wind power is described by adopting a scene generation and reduction method, and the start-stop strategy of a conventional unit in day-ahead scheduling is determined by simplifying a scene set, which is specifically as follows: step 2.1: scene generation and clipping: the actual output of a wind farm can be expressed as:
in (1) the->
The actual output of the wind power plant in the t period; epsilon t For the prediction error of the wind power in the t period, the compliance average value is 0 standard deviation sigma t Is a normal distribution of (2); the original scene set is generated by Latin hypercube sampling, namely LHS combined with Cholesky decomposition, and is cut down by synchronous back substitution cutting method based on probability distance, thereby obtaining a simplified scene set X S The method comprises the following steps:
wherein X is S One of (2)The rows represent one scene; />
The wind power is the wind power of the T period in the scene k; s is the number of scenes in the reduced scene set; step 2.2: solving a unit combination: the day-ahead start-stop decision of a conventional unit can be described as: / >
The initial value of the start-stop state of the unit is zero; for X S All scenes in (U) g The value of (1) is required to meet the system rotation standby constraint and the unit operation constraint;
step 3: solving a multi-time scale scheduling model: on the basis of acquiring a day-ahead unit combination through a multi-scene analysis method, a day-ahead dynamic scheduling model, a day-ahead rolling scheduling model considering high energy load and a day-ahead real-time static scheduling model are required to be solved respectively, and the method specifically comprises the following steps: step 3.1: solving a day-ahead scheduling model: the variables to be optimized of the day-ahead scheduling model are day-ahead planned output of a conventional unit and a wind farm, and an improved multi-target particle swarm algorithm is adopted to solve the variables; step 3.2: solving an intra-day rolling model: the rolling optimization is performed every 15min for 1h in the day, and the optimization period T intra For 4 time periods, variables to be optimized of the intra-day rolling model are the switching states of a conventional unit, the planned intra-day output of a wind power plant and the high-load energy load, and the following 0/1 state matrix is established aiming at the switching state variables of the high-load energy load:
the IMOPSO is adopted to solve the intra-day scheduling model, and the individual adopts a hybrid coding mode, and the structure is as follows: x is X m ＝[P NW B H ](30)，/>
Wherein P is NW Is a real number matrix; b (B) H A 0/1 discrete variable matrix; the solving step of the intra-day rolling model is the same as the pre-day schedule, except for individual X m B in (B) H The matrix is updated by adopting a speed and position updating formula of a BPSO algorithm; step 3.3, solving a real-time scheduling model: the real-time scheduling is static scheduling for 15min in the future, the daily rolling output of the conventional unit is corrected according to the latest ultra-short-term wind power predicted value so that the coal consumption increment and the wind abandoning cost of the unit are minimum, the variable to be optimized is the real-time planned output of the conventional unit and a wind farm, and the PSO algorithm is adopted for solving.
The multi-time scale coordinated scheduling model of the high-energy load in the step 1.2 specifically comprises the following steps:
step 1.21: day-ahead scheduling model: (1) objective function: day time operation cost F of wind-powered electricity generation-containing power system ahead The wind curtailment cost including the conventional unit power generation cost and the wind farm can be expressed as:
wherein T is the total time period number of day-ahead scheduling; s is S i The starting-up cost of the unit i is set; f (f) i The running cost function of the unit i; a, a i 、b i 、c i The running cost parameter of the unit i; c W Punishment cost is given for system unit abandoned wind;
a predicted value of wind power before the day of a period t is used for the wind power plant; the system pollution emissions can be expressed as: / >
Wherein: alpha i 、β i 、γ i 、δ i The characteristic parameters of the pollutant gas emission of the thermal power unit i are as follows; (2) constraint conditions: the system power balance constraint is: />
The system rotation reserve constraint is: />
Wherein P is i,max 、P i,min The upper limit and the lower limit of the output power of the unit i are respectively set; ρ l A rotation reserve coefficient corresponding to the load demand; u (u) w The rotation reserve coefficient corresponding to wind power fluctuation is used; the wind farm output constraint is: />
The operation constraint of the thermal power generating unit is as follows: />
In (1) the->
Respectively the continuous start-up and stop time of the thermal power generating unit i in the period t; />
Minimum on-off time of the unit i; d (D) i The falling climbing speed of the active force of the unit i is set;
step 1.22: rolling schedule model in day: (1) objective function: intra-day scheduling cost F considering high energy load intra The method comprises the steps of generating cost, wind abandoning cost and high-energy load operation cost of a conventional unit in a scheduling period, and can be expressed as follows:
wherein T is intra Scheduling the number of time periods for intra-day scrolling; />
Scheduling values for the thermal power generating unit i in the day of the period t; />
Respectively a predicted value and a scheduling value of wind power in the day of a period t; f (F) high The switching cost is high in energy load; n (N) H The number of the high-energy-carrying load groups which can be put into the system; c h Is the firsth groups of unit adjustment cost of high-energy load; b (B) h,t B is the switching state of the h group high-energy-carrying load in the period t h,t =1 represents that the h group high energy load is put into operation in the period t, B h,t =0 then indicates that the h group high energy load interrupts operation during period t; p (P) H Switching capacity for units with high energy load; the pollution emission target of the daily rolling schedule is consistent with the daily scheduling, and the daily unit scheduling output is replaced by the daily scheduling value; (2) constraint conditions: the system power balance constraint is: />
The wind farm output constraint is: />
The operation constraint of the thermal power generating unit is the same as that of the day-ahead scheduling; the operational constraints for high energy loads are: />
Wherein P is H,max 、P H,min The upper limit and the lower limit of the input capacity of the high energy load are respectively; m is M H,max The maximum number of times of switching is allowed in a scheduling period T for high-energy load; />
Continuous input and interruption time of the high-energy load h in the period t are respectively;
the minimum input time and the minimum interruption time of the high energy load h are respectively;
step 1.23: real-time scheduling model: (1) objective function: the real-time dispatching is to adjust the daily rolling dispatching output value of the machine set in the t+1 time period in the t time period, and the real-time dispatching cost F real The coal consumption and the wind disposal cost which comprise the unit output adjustment increase can be expressed as:
in (1) the- >
A real-time scheduling value of the thermal power generating unit i in a t+1 period; />
Respectively a predicted value and a scheduling value of the real-time wind power in a t+1 period; (2) constraint conditions: the system power balance constraint is: />
The wind farm output constraint is: />
The real-time scheduling value of the thermal power generating unit can meet the output limit and the climbing limit in the formula (10).
The ultra-short term regulation strategy system adopts an MPC-based flexible load and energy storage system ultra-short term regulation strategy model, and specifically comprises the following steps:
step a1: the ultra-short term regulation model based on MPC (model predictive control) comprises an overall target and constraint conditions; wherein the overall objective is: the ultra-short term optimization regulation and control based on MPC aims at utilizing load to maximize the consumption of renewable energy sources, reducing the power of an energy storage system, the power supply quantity of a large power grid and the wind and light discarding quantity, and the overall aim of minimizing a control system is as follows:
wherein F is a system optimization overall target; t represents time; p (P) 1 For power supplied by the grid to the load; p (P) 2 Power supplied to the load by the renewable energy source; p (P) 3 Charging the energy storage battery with power for a renewable energy source; p (P) RER Generating total power for renewable energy sources; the constraint conditions are as follows: adjustable flexible load constraint: the flexible load is mostly air conditioner load, in order to ensure user comfort and economy and flexible load working condition, the adjustable flexible load is limited in a specified range: p (P) LFmin ≤P LF (t)≤P LFmax (49) Wherein P is LF Is an adjustable flexible negativeA lotus; p (P) LFmin 、P LFmax Respectively the minimum value and the maximum value of the adjustable flexible load; power balance constraint: in order to ensure the normal operation of the electric equipment, the regulation and control of the flexible load and the energy storage system are required to meet the power balance of the power grid, namely: p (P) 1 (t)+P 2 (t)±P 4 (t)＝P LS (t)±P LF (t) (50) wherein P LS Is a non-adjustable load; energy storage system charge-discharge power and state of charge constraints: considering the problem of the service life of the energy storage system, in order to avoid the problems of overlarge charging and discharging power and over-charging and discharging, the charging and discharging power of the energy storage system should be limited in a specified range: />
SOC min ≤SOC(t)≤SOC max (52) Wherein->
A maximum value of charging power for the energy storage system; p (P) 4 Providing power to the energy storage system for the load; />
The maximum value of the absolute value of the discharge power of the energy storage system; SOC (State of Charge) min 、SOC max Respectively the minimum value and the maximum value of the charge state of the energy storage battery;
step a2: ultra-short time scale optimization regulation: the MPC takes predicted values of wind power generation and photovoltaic power generation as input variables, actual measured values of the wind power generation and the photovoltaic power generation as initial values, and flexible load adjustment quantity and energy storage power in a limited time domain in the future as control variables to perform rolling optimization solution;
step a3: state space conversion: -converting the objective function (48) into a single state equation and the input quantity into a single vector x (k), and the output quantity into a single output vector y (k), the inputs and outputs being expressed as: 
Where x (k) =soc (k) is the value of SOC at time k; in a state ofThe relationship between the variables presented in the form of space is: />
In the method, in the process of the invention,
Step a4: model solving: according to the characteristics of the state space equation of the objective function and the constraint condition, a dynamic matrix control algorithm is adopted for solving, wherein the predictive output vector Y (k) can be expressed as follows by the definition of gain: y (k) =fx (k) +Φu (k) (63), wherein,
the objective function is:
wherein,,
the flexible load control system adopts a flexible load aggregation control model based on cloud-edge cooperative technology, and specifically comprises the following steps:
step b1: temperature control flexible load model: the equivalent thermal parameter model can be used for simulating the thermal power process of flexible loads of an air conditioning unit, a heat pump, a water heater and a refrigerator, the air conditioning has periodic working characteristics when in operation, the indoor temperature fluctuates up and down near a certain temperature set value, when the air conditioning in a refrigerating state is started, the indoor temperature continuously drops, and when the temperature change reaches the lower limit value T of the boundary temperature set,min When the air conditioner is turned off; when the cavity is in the closed state, the temperature in the room continuously rises until reaching the upper limit value T of the boundary temperature set,max When the air conditioner is started again, the start-stop state variable s of the ith air conditioner at the time t+1 i,t+1 Indoor temperature T along with time T i,t The calculation formula of the change is as follows:
wherein s is i,t+1 =0 indicates that the air conditioner is turned off; s is(s) i,t+1 =1 indicates air conditioning start; indoor temperature T of ith air conditioner at time t+1 i,t+1 The calculation formula of (2) is as follows:
wherein: t (T) o,t+1 An outdoor temperature at time t+1; Δt is the simulation time interval;
step b2: electric automobile load model: the electric automobile is charged and discharged in the power grid through the power battery, wherein the most critical index is the battery state of charge, and the battery state of charge S of the electric automobile OC The calculation formula of (2) is as follows: (1) when the electric automobile is charged with constant power at the time t:
wherein, in the formula, < >>
The state of charge of the electric automobile i at the time t+delta t; />
The state of charge of the electric automobile i at the time t; p (P) c,i The charging power of the electric automobile i; η (eta) c,i The charging efficiency of the electric automobile i is; c (C) i The battery capacity of the electric automobile i; (2) when the electric automobile discharges with constant power at t time interval: />
Wherein P is d,i The discharge power of the electric automobile i; η (eta) d,i The discharge efficiency of the electric automobile i; (3) relationship between state of charge and mileage of electric vehicle: if the battery power of the electric vehicle is reduced at a constant rate in the running process, the relationship between the current charge state of the electric vehicle and the running mileage after the last charging is as follows: / >
In (1) the->
The electric automobile i is charged in the j-th network time period and leaves the charge state at the moment; />
The initial charge state of the electric automobile i in the j+1th on-line time period is set; d the driving mileage of the electric automobile i between the jth and the (j+1) th network time periods; d is the endurance mileage of the electric automobile i;
step b3: and (3) energy storage model: the charge and discharge of the energy storage are limited by the capacity of the grid-connected device, the charge state of the energy storage is also an important constraint of the operation of the energy storage, and the charge and discharge power P of the energy storage ES Defined as positive charge and negative discharge, assuming the charge efficiency η of the energy storage system c And discharge efficiency eta d The energy storage charging and discharging efficiency alpha is as follows when the energy storage charging and discharging efficiency alpha is unchanged in the operation process:
wherein S is OC (t) is the state of charge of the stored energy at time t; s is S OC (0) The energy storage is in a power-on state at the initial moment; p (P) ES (t) is the charge and discharge power of the stored energy at the moment t, written as S in discrete form OC (i)＝S OC (i-1)+α(i)P ES (i) Δt (71), the constraints that the energy storage discharge needs to satisfy are: />
Wherein P is ESmin 、P ESmax The upper limit and the lower limit of the energy storage charging and discharging power are respectively set; s is S OCmin 、S OCmax The upper limit and the lower limit of the energy storage charge state are respectively;
step b4: flexible load aggregation regulation strategy analysis: firstly, an optimization target curve of a regulating quantity is determined according to the adjustable potential of the elastic load in the range governed by a dispatching center or a load gatherer, a global optimization target of regulation is formulated according to an actual economical efficiency and a fluctuation minimization target, then, a load group participating in regulation is further determined according to the load characteristic and the electricity consumption behavior prediction result in the full time domain, regulation is sequentially carried out according to the state quantity of the elastic load, and finally, the economic benefit of load regulation is evaluated after the regulation of the elastic load is completed.
The invention has the beneficial effects that: the invention relates to a regulation and control system for large-scale flexible load resources, in use, the invention combines the daily scheduling, daily rolling scheduling and real-time scheduling, adopts intelligent optimization algorithm to solve step by step, effectively improves the wind power absorption level, effectively coordinates the operation cost of a power system at risk, improves the operation benefit of the system, enhances the wind power absorption capacity of the system, rapidly responds to wind power and photovoltaic uncertainty output by ultra-short-term regulation and control of flexible adjustable load and an energy storage system, reduces wind abandon and light abandon when wind power and photovoltaic greatly suddenly changes, and realizes flexible load aggregation regulation and control; the invention has the advantages of improving the wind power consumption level, effectively coordinating the running cost and risk of the power system, reducing the wind and light abandoning and realizing the flexible load aggregation regulation.
Drawings
Fig. 1 is a schematic diagram of the present invention.
Fig. 2 is a schematic diagram of the thermal power generating unit output according to the present invention.
FIG. 3 is a flowchart of a multi-time scale source-load coordination scheduling model according to the present invention.
FIG. 4 is a block diagram of coordinated optimization control in ultra-short term according to the present invention.
Fig. 5 is a power flow diagram of the system of the present invention.
Fig. 6 is a schematic diagram of an equivalent thermal parameter model of an air conditioner according to the present invention.
FIG. 7 is a schematic diagram of a flexible load aggregation regulation strategy of the present invention.
Fig. 8 is a schematic diagram of the flexible load control function of the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
Example 1
1-8, a regulation and control system for large-scale flexible load resources comprises a resource layer, a distributed control layer, a scheduling system layer, a primary control layer and a management layer, wherein the resource layer comprises at least one edge controller unit, and the edge controller unit comprises at least one sensor unit, at least one acquisition equipment unit and at least one control equipment unit; the distributed control layer comprises at least one secondary control center unit; the dispatching system layer comprises a source load coordination dispatching system, a random optimization dispatching system, an ultra-short term regulation strategy system and a flexible load regulation system; the first-level control layer comprises at least one management control network element unit; the management layer comprises a management server, a backup management server, a database, a backup database and a cloud server.
The source load coordination scheduling system adopts a power system multi-time scale source load coordination scheduling model containing large-scale wind power, and specifically comprises the following steps:
Step 1: the source load coordination scheduling model for wind power consumption is considered, and specifically comprises the following steps: step 1.1: consider the source-load characteristics of wind power absorptionAnalysis: the high energy load refers to the enterprise user load with higher energy value in the output value, and comprises machining, petrochemical industry and metal smelting; the control strategy of tracking wind power fluctuation in real time by utilizing high-energy load is utilized to promote the in-situ digestion of blocked wind power, especially in the period of down-peak regulation, the defect of insufficient peak regulation capacity of the conventional thermal power generating unit can be overcome, and the occurrence of abandoned wind is reduced; the equivalent load of the period t of the wind power system is as follows: p (P) E,t ＝P L,t -P W,t (1) Wherein P is L,t System load for period t; p (P) W,t Dispatching a force output value for the wind farm before the day of the period t; p (P) E,t Is the equivalent load of period t; the equivalent load increment for the t+1 period is: ΔP E,t+1 ＝ΔP L,t+1 -ΔP W,t+1 ＝(P L,t+1 -P L,t )-(P W,t+1 -P W,t ) (2) when the load demand of the time period t+1 increases greatly and the wind power dispatching output is obviously reduced, the following situations may occur:
in (1) the->
The maximum increment of the output force of the grid-connected thermal power generating unit in the t+1 period is set; u (u) i,t+1 For the start-stop state of the thermal power generating unit i in the period t+1, u i,t+1 =1 denotes the on state, u i,t+1 =0 indicates a shutdown state; p (P) i,t Active output of the unit i in a period t; u (U) i The rising climbing speed of the active force of the unit i is set; n is the number of thermal power generating units; Δt is the duration of a certain period.
In the present embodiment, as shown in FIG. 2, when
Equivalent load delta deltap less than t+1 time period E,t+1 When the output of the conventional unit in the t+1 period cannot meet the load demand, power shortage occurs, and in order to avoid load shedding, the wind power dispatching value P in the t period can be reduced when a dispatching plan is prepared W,t Thereby making DeltaP E,t+1 Reduced to discard windThe power supply reliability of the system is guaranteed at the cost, and a certain amount of high-load can be input in the period t to absorb the excessive wind power in the period t and cut off in the period t+1 under the operation mode of the high-load energy participating in the coordinated scheduling, so that the delta P is reduced E,t The purpose of (2); similarly, when the maximum reduction of the unit output force in the period t+1 is smaller than the reduction of the equivalent load, the wind abandoning can meet the system power balance in the period t+1, and in the coordinated scheduling mode of participation of the high-load energy load, the high-load energy load can be input in the period t+1 to consume the blocked wind power; therefore, the high-energy load is considered to participate in the coordinated scheduling of the wind power system, so that the method has practical application value for improving the power supply reliability and the wind power absorption capacity of the system; step 1.2: consider a multi-time scale coordinated scheduling model of high energy load: because the wind power prediction precision is gradually improved along with the reduction of the prediction scale and the high-load energy load is rapidly switched, a coordinated scheduling model which is gradually refined in time scale and takes the high-load energy load into consideration is established on the basis of day-ahead scheduling, and the method specifically comprises the following steps: a high-load energy load switching strategy within 1h in the future is formulated in advance of 1h in the daily rolling scheduling, and the output of a machine unit scheduled in the day is corrected; and the real-time scheduling establishes a real-time unit output value according to the latest 15-min-level wind power predicted value, and further corrects a unit output plan of the daily rolling scheduling.
Step 2: and (3) solving a wind power-containing unit combination: the uncertainty of wind power is described by adopting a scene generation and reduction method, and the start-stop strategy of a conventional unit in day-ahead scheduling is determined by simplifying a scene set, which is specifically as follows: step 2.1: scene generation and clipping: the actual output of a wind farm can be expressed as:
in (1) the->
The actual output of the wind power plant in the t period; epsilon t For the prediction error of the wind power in the t period, the compliance average value is assumed to beStandard deviation of 0 sigma t Is a normal distribution of (2); the original scene set is generated by Latin hypercube sampling, namely LHS combined with Cholesky decomposition, and is cut down by synchronous back substitution cutting method based on probability distance, thereby obtaining a simplified scene set X S The method comprises the following steps:
wherein X is S Represents a scene; />
The wind power is the wind power of the T period in the scene k; s is the number of scenes in the reduced scene set; step 2.2: solving a unit combination: the day-ahead start-stop decision of a conventional unit can be described as: />
The initial value of the start-stop state of the unit is zero; for X S All scenes in (U) g The value of (1) is required to meet the system rotation standby constraint and the unit operation constraint;
in the present embodiment, U g The solving process of (1) is as follows: (1) system rotation standby constraint processing: sequencing all the units according to the priority, wherein the priority coefficient of the unit i is as follows: 
Wherein: w (w) 1 、w 2 Representing the weight coefficient; if the rotation standby constraint of the formula (8) cannot be met in the period t under the scene k, selecting a unit grid connection with the highest priority from shutdown units; (2) minimum on-off time constraint processing of a unit: on the basis of meeting the requirement of system rotation standby, only u needs to be checked i,t Whether the set of =0 violates the on-off time constraint in equation (10); when u is i,t-1 =1 and u i,t When=0, u is required to satisfy one of the following 3 cases i,t Setting 1: />
(3) And (5) separating redundant units: minimum on-off time constraint processing may result in a crewThe start-stop decision generates excessive rotational spares to increase the running cost, so that the redundant grid-connected units need to be separated, and for the unit combination of the period t under the scene k, the rotational spares of the system can be calculated according to the following formula: />
Sequencing grid-connected units in a period t from low to high according to priority, and when the upper limit of the output power of the units with low priority is smaller than the system rotation standby in all scenes +.>
When the unit is separated and the minimum on-off time constraint cannot be violated, the unit can be separated in a period t; (4) the unit substitution principle: when the load demand is reduced, the unit cannot be closed due to the constraint of starting time limit, so that excessive systems are reserved, in this case, the unit with lower priority coefficient and shorter minimum starting time can be selected to replace the original unit to be started in the peak-to-peak load period, so that the minimum output limit of the conventional unit in the peak-to-peak period is reduced;
Step 3: solving a multi-time scale scheduling model: on the basis of acquiring a day-ahead unit combination through a multi-scene analysis method, a day-ahead dynamic scheduling model, a day-ahead rolling scheduling model considering high energy load and a day-ahead real-time static scheduling model are required to be solved respectively, and the method specifically comprises the following steps: step 3.1: solving a day-ahead scheduling model: the variables to be optimized of the day-ahead scheduling model are day-ahead planned output of a conventional unit and a wind farm, and an improved multi-target particle swarm algorithm is adopted to solve the variables;
in this embodiment, the IMOPSO algorithm mainly includes the following steps: (1) initializing a population: processing population individuals according to constraint conditions (7) - (10) of a day-ahead scheduling model, solving an objective function value of each individual, comprehensively considering the output power limit and the climbing speed limit of the thermal power unit, and obtaining the planned output upper limit and the planned output lower limit of a unit i period t as follows:
and according to the set start-stop state limit and the peak shaving depth limit, the following correction is made for the planned output of the conventional set: for the upper bound of the unit planned output, equation (26) does not consider the effect of the period after t on the t period planned output; such as: when unit u i,t =1 and u i,t+1 When=0, P may occur i,t Greater than D i In the case of deltat, that is, the planned output reduction rate of the unit from the T period to the t+1 period violates the limit of the descending climbing rate, the planned output of the conventional unit needs to be corrected according to the start-stop strategy of the unit before the day; when u is i,t When=1, for the start-stop strategy of the unit i after the period t, the latest stop state of the unit i is set at t stop The upper planned output limit of the unit i can be expressed as: />
Lower limit for the planned output of the machine set>
The method can be obtained according to the formula (26), however, when wind power is sufficient and the load demand is low, the unit is easy to operate in a deep peak regulation state, the running risk of the unit is increased, the service life of the unit is influenced, and the method is modified as follows:
wherein, the max {.cndot. } term limits the unit output to more than 50% of the capacity; the min {.cndot. } term indicates that the unit start-up phase and the unit stop phase can operate in a lower output state; (2) establishing an external elite set: establishing an initial external elite set according to the dominant relationship among population individuals, and guiding the evolution direction of the population; (3) population optimization: entering a main loop, carrying out updating and mutation operation on an individual according to the external elite set in each iteration, and updating the external elite set in real time until the maximum iteration times are reached; (4) and (3) optimization is finished: the external elite set finally obtained is the Pareto optimal solution set of the multi-objective optimal scheduling.
The ultra-short term regulation strategy system adopts an MPC-based flexible load and energy storage system ultra-short term regulation strategy model, and specifically comprises the following steps:
step a1: the ultra-short term regulation model based on MPC (model predictive control) comprises an overall target and constraint conditions; wherein the overall objective is: the ultra-short term optimization regulation and control based on MPC aims at utilizing load to maximize the consumption of renewable energy sources, reducing the power of an energy storage system, the power supply quantity of a large power grid and the wind and light discarding quantity, and the overall aim of minimizing a control system is as follows:
wherein F is a system optimization overall target; t represents time; p (P) 1 For power supplied by the grid to the load; p (P) 2 Power supplied to the load by the renewable energy source; p (P) 3 Charging the energy storage battery with power for a renewable energy source; p (P) RER Generating total power for renewable energy sources; the constraint conditions are as follows: adjustable flexible load constraint: the flexible load is mostly air conditioner load, in order to ensure user comfort and economy and flexible load working condition, the adjustable flexible load is limited in a specified range: p (P) LFmin ≤P LF (t)≤P LFmax (49) Wherein P is LF Is an adjustable flexible load; p (P) LFmin 、P LFmax Respectively the minimum value and the maximum value of the adjustable flexible load; power balance constraint: in order to ensure the normal operation of the electric equipment, the regulation and control of the flexible load and the energy storage system are required to meet the power balance of the power grid, namely: p (P) 1 (t)+P 2 (t)±P 4 (t)＝P LS (t)±P LF (t) (50) wherein P LS Is a non-adjustable load; energy storage system charge-discharge power and state of charge constraints: considering the problem of the service life of the energy storage system, in order to avoid the problems of overlarge charging and discharging power and over-charging and discharging, the charging and discharging power of the energy storage system should be limited in a specified range: />
SOC min ≤SOC(t)≤SOC max (52) Wherein->
Charging power for energy storage systemMaximum value of the rate; p (P) 4 Providing power to the energy storage system for the load; />
The maximum value of the absolute value of the discharge power of the energy storage system; SOC (State of Charge) min 、SOC max Respectively the minimum value and the maximum value of the charge state of the energy storage battery;
step a2: ultra-short time scale optimization regulation: the MPC takes predicted values of wind power generation and photovoltaic power generation as input variables, actual measured values of the wind power generation and the photovoltaic power generation as initial values, and flexible load adjustment quantity and energy storage power in a limited time domain in the future as control variables to perform rolling optimization solution;
in this embodiment, the step a2 specifically includes the following steps:
step a2.1: prediction model: the flexible load adjustment quantity and the energy storage power are solved through rolling optimization, the power of a fan, a photovoltaic device and energy storage and the flexible load adjustment quantity in a limited future domain are predicted, and a prediction model is as follows: 
Wherein y (k+ik) is the adjustment quantity of a fan, photovoltaic and energy storage active power and a flexible load at the moment k+i in the future obtained by prediction at the moment k; y is 0 (k) Is the actual initial value; deltau (k+tk) is the predicted future at time k [ k+ (t-1), k+t ]]The active output variable quantity in the time period is an optimized control variable; n (N) p Representing a prediction step size;
step a2.2: optimizing a daily function: equation (48) is an overall objective of the regulation strategy, and the regulation strategy based on the MPC needs to be optimized in each regulation period, so that an objective function is defined in each regulation period, and on the basis of the active output of wind power generation and photovoltaic power generation and the adjustable flexible load reference value, the ultra-short time scale optimization objective is set up to have minimum correction deviation of the active output, so that the ultra-short time scale active optimization scheduling secondary optimization performance index based on the MPC is established: minJ (k) =min (Y (k) -R (k)) T (Y (k) -R (k)) (54) wherein,
wherein P is LE Power is required for the load; vector Y (k) is the output value of the k-moment prediction model; vector R (k) is a planned value of output quantity, and comprises flexible load adjustment quantity of the generated energy of the fan and the photovoltaic power and energy storage charging and discharging quantity;
step a2.3: feedback correction: the prediction error is fed back, and the prediction output of the model is corrected through feedback information, so that closed loop optimization is formed: y is 0 (k+1)＝y real (k+1) (56) wherein y real (k+1) is the actual active value measured at time k+1; y is 0 (k+1) is an initial value of active output force at the time of k+1;
step a3: state space conversion: -converting the objective function (48) into a single state equation and the input quantity into a single vector x (k), and the output quantity into a single output vector y (k), the inputs and outputs being expressed as:
where x (k) =soc (k) is the value of SOC at time k; the relationship between the variables presented in the form of a state space is: />
In the method, in the process of the invention,
Step a4: model solving: according to the characteristics of the state space equation of the objective function and the constraint condition, a dynamic matrix control algorithm is adopted for solving, wherein the predictive output vector Y (k) can be expressed as follows by the definition of gain: y (k) =fx (k) +Φu (k) (63), wherein,
the objective function is:
wherein,,
the flexible load control system adopts a flexible load aggregation control model based on cloud-edge cooperative technology, and specifically comprises the following steps:
Step b1: temperature control flexible load model: the equivalent thermal parameter model can be used for simulating the thermal power process of flexible loads of an air conditioning unit, a heat pump, a water heater and a refrigerator, and the equivalent thermal parameter model of the air conditioner is shown in fig. 6, wherein eta is the air conditioning energy efficiency ratio; p is the refrigerating/heating power of the air conditioning unit; t is the outdoor temperature; c is equivalent heat capacity; r is equivalent thermal resistance; the air conditioner has periodic operation characteristic, indoor temperature fluctuates around a certain set value, when the air conditioner in refrigerating state is started, the indoor temperature continuously drops, and when the temperature change reaches the lower limit value T of boundary temperature set,min When the air conditioner is turned off; when the cavity is in the closed state, the temperature in the room continuously rises until reaching the upper limit value T of the boundary temperature set,max When the air conditioner is started again, the ith air conditioner is started at time t+1Start-stop state variable s of (2) i,t+1 Indoor temperature T along with time T i,t The calculation formula of the change is as follows:
wherein s is i,t+1 =0 indicates that the air conditioner is turned off; s is(s) i,t+1 =1 indicates air conditioning start; indoor temperature T of ith air conditioner at time t+1 i,t+1 The calculation formula of (2) is as follows: />
Wherein: t (T) o,t+1 An outdoor temperature at time t+1; Δt is the simulation time interval;
step b2: electric automobile load model: the electric automobile is charged and discharged in the power grid through the power battery, wherein the most critical index is the battery state of charge, and the battery state of charge S of the electric automobile OC The calculation formula of (2) is as follows: (1) when the electric automobile is charged with constant power at the time t:
wherein, in the formula, < >>
The state of charge of the electric automobile i at the time t+delta t; />
The state of charge of the electric automobile i at the time t; p (P) c,i The charging power of the electric automobile i; η (eta) c,i The charging efficiency of the electric automobile i is; c (C) i The battery capacity of the electric automobile i; (2) when the electric automobile discharges with constant power at t time interval: />
Wherein P is d,i The discharge power of the electric automobile i; η (eta) d,i The discharge efficiency of the electric automobile i; (3) relationship between state of charge and mileage of electric vehicle: if the battery power of the electric automobile is reduced at a constant rate in the running process, the current charge of the electric automobileThe relation between the state and the driving mileage after the last charging is: />
In (1) the->
The electric automobile i is charged in the j-th network time period and leaves the charge state at the moment; />
The initial charge state of the electric automobile i in the j+1th on-line time period is set; d the driving mileage of the electric automobile i between the jth and the (j+1) th network time periods; d is the endurance mileage of the electric automobile i;
step b3: and (3) energy storage model: the charge and discharge of the energy storage are limited by the capacity of the grid-connected device, the charge state of the energy storage is also an important constraint of the operation of the energy storage, and the charge and discharge power P of the energy storage ES Defined as positive charge and negative discharge, assuming the charge efficiency η of the energy storage system c And discharge efficiency eta d The energy storage charging and discharging efficiency alpha is as follows when the energy storage charging and discharging efficiency alpha is unchanged in the operation process:
wherein S is OC (t) is the state of charge of the stored energy at time t; s is S OC (0) The energy storage is in a power-on state at the initial moment; p (P) ES (t) is the charge and discharge power of the stored energy at the moment t, written as S in discrete form OC (i)＝S OC (i-1)+α(i)P ES (i) Δt (71), the constraints that the energy storage discharge needs to satisfy are: />
Wherein P is ESmin 、P ESmax The upper limit and the lower limit of the energy storage charging and discharging power are respectively set; s is S OCmin 、S OCmax The upper limit and the lower limit of the energy storage charge state are respectively;
step b4: flexible load aggregation regulation strategy analysis: firstly, an optimization target curve of a regulating quantity is determined according to the adjustable potential of the elastic load in the range governed by a dispatching center or a load aggregator, a global optimization target of regulation is formulated according to an actual economical efficiency and a fluctuation minimization target, then, a load group participating in regulation is further determined according to load characteristics and electricity consumption behavior prediction results in the whole time domain range, regulation is sequentially carried out according to the state quantity of the elastic load, finally, after the elastic load regulation is completed, the economic benefit of the load regulation is evaluated, and a flexible load aggregation regulation strategy is shown in figure 7.
The invention relates to a regulation and control system for large-scale flexible load resources, in use, the regulation and control system aims at the restriction influence of the regulation capacity of a conventional unit on wind power absorption, a high-load energy load and conventional unit coordinated operation regulation mode is provided, a multi-time scale source load coordinated regulation model is established with the aim of minimum total operation cost and pollution emission of the system on the basis, the daily scheduling, daily rolling scheduling and real-time scheduling are comprehensively matched, an intelligent optimization algorithm is adopted for solving step by step, the wind power absorption level is effectively improved, the operation cost of a power system is effectively coordinated at risk, the operation benefit of the system is improved, the capacity of the system for absorbing wind power is enhanced, wind power and photovoltaic uncertainty output is rapidly responded through ultra-short-term regulation and control of a flexible adjustable load and an energy storage system, wind power and photovoltaic uncertainty output are reduced, wind and light are abandoned when wind power and photovoltaic are greatly suddenly changed, and flexible load aggregation regulation and control is realized; the invention has the advantages of improving the wind power consumption level, effectively coordinating the running cost and risk of the power system, reducing the wind and light abandoning and realizing the flexible load aggregation regulation.
Claims (9)
1. A regulation and control system for large-scale flexible load resources comprises a resource layer, a distributed control layer, a dispatching system layer, a primary control layer and a management layer, and is characterized in that: the resource layer comprises at least one edge controller unit, wherein the edge controller unit comprises at least one sensor unit, at least one acquisition equipment unit and at least one control equipment unit; the distributed control layer comprises at least one secondary control center unit; the dispatching system layer comprises a source load coordination dispatching system, a random optimization dispatching system, an ultra-short term regulation strategy system and a flexible load regulation system; the first-level control layer comprises at least one management control network element unit; the management layer comprises a management server, a backup management server, a database, a backup database and a cloud server.
2. A large scale flexible load resource oriented regulation and control system as claimed in claim 1, wherein: the distributed control layer is connected with the resource layer through a secondary control center unit, and the dispatching system layer is connected with the distributed control layer; the first-level control layer is connected with the management layer upwards and the dispatching system layer downwards through the management control network element unit.
3. A large scale flexible load resource oriented regulation and control system as claimed in claim 2, wherein: the management server of the management layer is respectively connected with the backup management server, the database and the backup database; the backup management server is respectively connected with the management server, the database and the backup database; the database is respectively connected with the management server, the backup management server and the cloud server; and the backup database is respectively connected with the management server, the backup management server and the cloud server.
4. A large scale flexible load resource oriented regulation and control system as claimed in claim 3 wherein: the management server comprises a source load coordination scheduling management unit, a random optimization scheduling management unit, an ultra-short term regulation strategy management unit and a flexible load regulation management unit; the source load coordination scheduling management unit, the random optimization scheduling management unit, the ultra-short-term regulation strategy management unit and the flexible load regulation management unit are correspondingly connected with the source load coordination scheduling system, the random optimization scheduling system, the ultra-short-term regulation strategy system and the flexible load regulation system respectively.
5. The large-scale flexible load resource oriented regulation and control system of claim 4, wherein: the source load coordination scheduling system adopts a power system multi-time scale source load coordination scheduling model containing large-scale wind power, and specifically comprises the following steps:
step 1: the source load coordination scheduling model for wind power consumption is considered, and specifically comprises the following steps: step 1.1: and (3) analyzing the source load characteristics of wind power consumption: the high energy load refers to the enterprise user load with higher energy value in the output value, and comprises machining, petrochemical industry and metal smelting; the control strategy of tracking wind power fluctuation in real time by utilizing high-energy load is utilized to promote the in-situ digestion of blocked wind power, especially in the period of down-peak regulation, the defect of insufficient peak regulation capacity of the conventional thermal power generating unit can be overcome, and the occurrence of abandoned wind is reduced; the equivalent load of the period t of the wind power system is as follows: p (P) E,t ＝P L,t -P W,t (1) Wherein P is L,t System load for period t; p (P) W,t Dispatching a force output value for the wind farm before the day of the period t; p (P) E,t Is the equivalent load of period t; the equivalent load increment for the t+1 period is: ΔP E,t+1 ＝ΔP L,t+1 -ΔP W,t+1 ＝(P L,t+1 -P L,t )-(P W,t+1 -P W,t ) (2) when the load demand of the time period t+1 increases greatly and the wind power dispatching output is obviously reduced, the following situations may occur: 
In (1) the->
The maximum increment of the output force of the grid-connected thermal power generating unit in the t+1 period is set; u (u) i,t+1 For the start-stop state of the thermal power generating unit i in the period t+1, u i,t+1 =1 denotes the on state, u i,t+1 =0 indicates a shutdown state; p (P) i,t Active output of the unit i in a period t; u (U) i The rising climbing speed of the active force of the unit i is set; n is the number of thermal power generating units; Δt is the duration of a certain period;
step 2: and (3) solving a wind power-containing unit combination: using scene generation and curtailment methods to describe wind powerAnd determining a start-stop strategy of a conventional unit in the day-ahead scheduling through the reduced scene set, wherein the start-stop strategy is specifically as follows: step 2.1: scene generation and clipping: the actual output of a wind farm can be expressed as:
in (1) the->
The actual output of the wind power plant in the t period; epsilon t For the prediction error of the wind power in the t period, the compliance average value is 0 standard deviation sigma t Is a normal distribution of (2); the original scene set is generated by Latin hypercube sampling, namely LHS combined with Cholesky decomposition, and is cut down by synchronous back substitution cutting method based on probability distance, thereby obtaining a simplified scene set X S The method comprises the following steps:
wherein X is S Represents a scene; />
The wind power is the wind power of the T period in the scene k; s is the number of scenes in the reduced scene set; step 2.2: solving a unit combination: the day-ahead start-stop decision of a conventional unit can be described as: / >
The initial value of the start-stop state of the unit is zero; for X S All scenes in (U) g The value of (1) is required to meet the system rotation standby constraint and the unit operation constraint;
step 3: solving a multi-time scale scheduling model: on the basis of acquiring a day-ahead unit combination through a multi-scene analysis method, a day-ahead dynamic scheduling model, a day-ahead rolling scheduling model considering high energy load and a day-ahead rolling scheduling model are neededSolving the daily real-time static scheduling model respectively, wherein the method comprises the following steps of: step 3.1: solving a day-ahead scheduling model: the variables to be optimized of the day-ahead scheduling model are day-ahead planned output of a conventional unit and a wind farm, and an improved multi-target particle swarm algorithm is adopted to solve the variables; step 3.2: solving an intra-day rolling model: the rolling optimization is performed every 15min for 1h in the day, and the optimization period T intra For 4 time periods, variables to be optimized of the intra-day rolling model are the switching states of a conventional unit, the planned intra-day output of a wind power plant and the high-load energy load, and the following 0/1 state matrix is established aiming at the switching state variables of the high-load energy load:
the IMOPSO is adopted to solve the intra-day scheduling model, and the individual adopts a hybrid coding mode, and the structure is as follows: x is X m ＝[P NW B H ](30)，/>
Wherein P is NW Is a real number matrix; b (B) H A 0/1 discrete variable matrix; the solving step of the intra-day rolling model is the same as the pre-day schedule, except for individual X m B in (B) H The matrix is updated by adopting a speed and position updating formula of a BPSO algorithm; step 3.3, solving a real-time scheduling model: the real-time scheduling is static scheduling for 15min in the future, the daily rolling output of the conventional unit is corrected according to the latest ultra-short-term wind power predicted value so that the coal consumption increment and the wind abandoning cost of the unit are minimum, the variable to be optimized is the real-time planned output of the conventional unit and a wind farm, and the PSO algorithm is adopted for solving.
6. The large-scale flexible load resource oriented regulation and control system of claim 5, wherein: the multi-time scale coordinated scheduling model of the high-energy load in the step 1.2 specifically comprises the following steps:
step 1.21: day-ahead scheduling model: (1) objective function: day time operation cost F of wind-powered electricity generation-containing power system ahead The wind curtailment cost including the conventional unit power generation cost and the wind farm can be expressed as:
wherein T is the total time period number of day-ahead scheduling; s is S i The starting-up cost of the unit i is set; f (f) i The running cost function of the unit i; a, a i 、b i 、c i The running cost parameter of the unit i; c W Punishment cost is given for system unit abandoned wind; 
A predicted value of wind power before the day of a period t is used for the wind power plant; the system pollution emissions can be expressed as: />
Wherein: alpha i 、β i 、γ i 、δ i The characteristic parameters of the pollutant gas emission of the thermal power unit i are as follows; (2) constraint conditions: the system power balance constraint is:
the system rotation reserve constraint is: />
Wherein P is i,max 、P i,min The upper limit and the lower limit of the output power of the unit i are respectively set; ρ l A rotation reserve coefficient corresponding to the load demand; u (u) w The rotation reserve coefficient corresponding to wind power fluctuation is used; the wind farm output constraint is: />
The operation constraint of the thermal power generating unit is as follows:
in (1) the->
Respectively the continuous start-up and stop time of the thermal power generating unit i in the period t; />
Minimum on-off time of the unit i; d (D) i The falling climbing speed of the active force of the unit i is set;
step 1.22: rolling schedule model in day: (1) objective function: intra-day scheduling cost F considering high energy load intra The method comprises the steps of generating cost, wind abandoning cost and high-energy load operation cost of a conventional unit in a scheduling period, and can be expressed as follows:
wherein T is intra Scheduling the number of time periods for intra-day scrolling; />
Scheduling values for the thermal power generating unit i in the day of the period t; />
Respectively a predicted value and a scheduling value of wind power in the day of a period t; f (F) high The switching cost is high in energy load; n (N) H The number of the high-energy-carrying load groups which can be put into the system; c h Adjusting the cost for the unit of the h group high-energy load; b (B) h,t B is the switching state of the h group high-energy-carrying load in the period t h,t =1 represents that the h group high energy load is put into operation in the period t, B h,t =0 then indicates that the h group high energy load interrupts operation during period t; p (P) H Switching capacity for units with high energy load; the pollution emission target of the daily rolling schedule is consistent with the daily scheduling, and the daily unit scheduling output is replaced by the daily scheduling value; (2) constraint conditions: the system power balance constraint is: />
The wind farm output constraint is: />
The operation constraint of the thermal power generating unit is the same as that of the day-ahead scheduling; the operational constraints for high energy loads are: />
Wherein P is H,max 、P H,min The upper limit and the lower limit of the input capacity of the high energy load are respectively; m is M H,max The maximum number of times of switching is allowed in a scheduling period T for high-energy load; />
Continuous input and interruption time of the high-energy load h in the period t are respectively;
the minimum input time and the minimum interruption time of the high energy load h are respectively;
step 1.23: real-time scheduling model: (1) objective function: the real-time dispatching is to adjust the daily rolling dispatching output value of the machine set in the t+1 time period in the t time period, and the real-time dispatching cost F real The coal consumption and the wind disposal cost which comprise the unit output adjustment increase can be expressed as:
in (1) the->
A real-time scheduling value of the thermal power generating unit i in a t+1 period; />
Respectively a predicted value and a scheduling value of the real-time wind power in a t+1 period; (2) constraint conditions: the system power balance constraint is:/>
the wind farm output constraint is:
the real-time scheduling value of the thermal power generating unit can meet the output limit and the climbing limit in the formula (10).
7. A large scale flexible load resource oriented regulation and control system as claimed in claim 1, wherein: the ultra-short term regulation strategy system adopts an MPC-based flexible load and energy storage system ultra-short term regulation strategy model, and specifically comprises the following steps:
step a1: the ultra-short term regulation model based on MPC (model predictive control) comprises an overall target and constraint conditions; wherein the overall objective is: the ultra-short term optimization regulation and control based on MPC aims at utilizing load to maximize the consumption of renewable energy sources, reducing the power of an energy storage system, the power supply quantity of a large power grid and the wind and light discarding quantity, and the overall aim of minimizing a control system is as follows:
wherein F is a system optimization overall target; t represents time; p (P) 1 For power supplied by the grid to the load; p (P) 2 Power supplied to the load by the renewable energy source; p (P) 3 Charging the energy storage battery with power for a renewable energy source; p (P) RER Generating total power for renewable energy sources; the constraint conditions are as follows: adjustable flexible load constraint: the flexible load is mostly air conditioner load, in order to ensure user comfort and economy and flexible load working condition, the adjustable flexible load is limited in a specified range: p (P) LFmin ≤P LF (t)≤P LFmax (49) Wherein P is LF Is an adjustable flexible load; p (P) LFmin 、P LFmax Respectively the minimum value and the maximum value of the adjustable flexible load; power balance constraint: in order to ensure the normal operation of the electric equipment, the regulation and control of the flexible load and the energy storage system are required to meet the power balance of the power grid,namely: p (P) 1 (t)+P 2 (t)±P 4 (t)＝P LS (t)±P LF (t) (50) wherein P LS Is a non-adjustable load; energy storage system charge-discharge power and state of charge constraints: considering the problem of the service life of the energy storage system, in order to avoid the problems of overlarge charging and discharging power and over-charging and discharging, the charging and discharging power of the energy storage system should be limited in a specified range: />
SOC min ≤SOC(t)≤SOC max (52) Wherein->
A maximum value of charging power for the energy storage system; p (P) 4 Providing power to the energy storage system for the load; />
The maximum value of the absolute value of the discharge power of the energy storage system; SOC (State of Charge) min 、SOC max Respectively the minimum value and the maximum value of the charge state of the energy storage battery;
Step a2: ultra-short time scale optimization regulation: the MPC takes predicted values of wind power generation and photovoltaic power generation as input variables, actual measured values of the wind power generation and the photovoltaic power generation as initial values, and flexible load adjustment quantity and energy storage power in a limited time domain in the future as control variables to perform rolling optimization solution;
step a3: state space conversion: -converting the objective function (48) into a single state equation and the input quantity into a single vector x (k), and the output quantity into a single output vector y (k), the inputs and outputs being expressed as:
where x (k) =soc (k) is the value of SOC at time k; the relationship between the variables presented in the form of a state space is: />
In the method, in the process of the invention,
Step a4: model solving: according to the characteristics of the state space equation of the objective function and the constraint condition, a dynamic matrix control algorithm is adopted for solving, wherein the predictive output vector Y (k) can be expressed as follows by the definition of gain: y (k) =fx (k) +Φu (k) (63), wherein, 
The objective function is:
wherein (1)>
8. The large-scale flexible load resource oriented regulation and control system of claim 7, wherein: the step a2 specifically comprises the following steps:
step a2.1: prediction model: the flexible load adjustment quantity and the energy storage power are solved through rolling optimization, the power of a fan, a photovoltaic device and energy storage and the flexible load adjustment quantity in a limited future domain are predicted, and a prediction model is as follows:
wherein y (k+i|k) is the adjustment quantity of a fan, photovoltaic and energy storage active power and flexible load at the moment k+i in the future obtained by prediction at the moment k; y is 0 (k) Is the actual initial value; deltau (k+t|k) is the predicted future at time k [ k+ (t-1), k+t ]]The active output variable quantity in the time period is an optimized control variable; n (N) p Representing a prediction step size;
step a2.2: optimizing a daily function: equation (48) is an overall objective of the regulation strategy, and the regulation strategy based on the MPC needs to be optimized in each regulation period, so that an objective function is defined in each regulation period, and on the basis of the active output of wind power generation and photovoltaic power generation and the adjustable flexible load reference value, the ultra-short time scale optimization objective is set up to have minimum correction deviation of the active output, so that the ultra-short time scale active optimization scheduling secondary optimization performance index based on the MPC is established: minJ (k) =min (Y (k) -R (k)) T (Y (k) -R (k)) (54) wherein,
wherein P is LE Power is required for the load; vector Y (k) is the output value of the k-moment prediction model; vector R (k) is a planned value of output quantity, and comprises flexible load adjustment quantity of the generated energy of the fan and the photovoltaic power and energy storage charging and discharging quantity;
step a2.3: feedback correction: the prediction error is fed back, and the prediction output of the model is corrected through feedback information, so that closed loop optimization is formed: y is 0 (k+1)＝y real (k+1) (56) wherein y real (k+1) is the actual active value measured at time k+1; y is 0 (k+1) is an initial value of active force at the time of k+1.
9. A large scale flexible load resource oriented regulation and control system as claimed in claim 1, wherein: the flexible load control system adopts a flexible load aggregation control model based on cloud-edge cooperative technology, and specifically comprises the following steps:
step b1: temperature control flexible load model: the equivalent thermal parameter model can be used for simulating the thermal power process of flexible loads of an air conditioning unit, a heat pump, a water heater and a refrigerator, the air conditioning has periodic working characteristics when in operation, the indoor temperature fluctuates up and down near a certain temperature set value, when the air conditioning in a refrigerating state is started, the indoor temperature continuously drops, and when the temperature change reaches the lower limit value T of the boundary temperature set,min When the air conditioner is turned off; when the cavity is in the closed state, the temperature in the room continuously rises until reaching the upper limit value T of the boundary temperature set,max When the air conditioner is started again, the start-stop state variable s of the ith air conditioner at the time t+1 i,t+1 Indoor temperature T along with time T i,t The calculation formula of the change is as follows:
wherein s is i,t+1 =0 indicates that the air conditioner is turned off; s is(s) i,t+1 =1 indicates air conditioning start; indoor temperature T of ith air conditioner at time t+1 i,t+1 The calculation formula of (2) is as follows:
wherein: t (T) o,t+1 An outdoor temperature at time t+1; Δt is the simulation time interval; />
Step b2: electric automobile load model: the electric automobile is charged and discharged in the power grid through the power battery, wherein the most critical index is the battery state of charge, and the battery state of charge S of the electric automobile OC The calculation formula of (2) is as follows: (1) when the electric automobile is charged with constant power at the time t:
wherein, in the formula, < >>
The state of charge of the electric automobile i at the time t+delta t; />
The state of charge of the electric automobile i at the time t; p (P) c,i The charging power of the electric automobile i; η (eta) c,i The charging efficiency of the electric automobile i is; c (C) i The battery capacity of the electric automobile i; (2) when the electric automobile discharges with constant power at t time interval: />
Wherein P is d,i The discharge power of the electric automobile i; η (eta) d,i The discharge efficiency of the electric automobile i; (3) relationship between state of charge and mileage of electric vehicle: if the battery power of the electric vehicle is reduced at a constant rate in the running process, the relationship between the current charge state of the electric vehicle and the running mileage after the last charging is as follows: / >
In (1) the->
The electric automobile i is charged in the j-th network time period and leaves the charge state at the moment; />
The initial charge state of the electric automobile i in the j+1th on-line time period is set; d the driving mileage of the electric automobile i between the jth and the (j+1) th network time periods; d is the endurance mileage of the electric automobile i;
step b3: and (3) energy storage model: the charge and discharge of the energy storage are limited by the capacity of the grid-connected device, the charge state of the energy storage is also an important constraint of the operation of the energy storage, and the charge and discharge power P of the energy storage ES Defined as positive charge and negative discharge, assuming charging of the energy storage systemEfficiency eta c And discharge efficiency eta d The energy storage charging and discharging efficiency alpha is as follows when the energy storage charging and discharging efficiency alpha is unchanged in the operation process:
wherein S is OC (t) is the state of charge of the stored energy at time t; s is S OC (0) The energy storage is in a power-on state at the initial moment; p (P) ES (t) is the charge and discharge power of the stored energy at the moment t, written as S in discrete form OC (i)＝S OC (i-1)+α(i)P ES (i) Δt (71), the constraints that the energy storage discharge needs to satisfy are: />
Wherein P is ESmin 、P ESmax The upper limit and the lower limit of the energy storage charging and discharging power are respectively set; s is S OCmin 、S OCmax The upper limit and the lower limit of the energy storage charge state are respectively;
step b4: flexible load aggregation regulation strategy analysis: firstly, an optimization target curve of a regulating quantity is determined according to the adjustable potential of the elastic load in the range governed by a dispatching center or a load gatherer, a global optimization target of regulation is formulated according to an actual economical efficiency and a fluctuation minimization target, then, a load group participating in regulation is further determined according to the load characteristic and the electricity consumption behavior prediction result in the full time domain, regulation is sequentially carried out according to the state quantity of the elastic load, and finally, the economic benefit of load regulation is evaluated after the regulation of the elastic load is completed.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310142525.8A CN116207748A (en) | 2023-02-21 | 2023-02-21 | Regulation and control system for large-scale flexible load resources |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310142525.8A CN116207748A (en) | 2023-02-21 | 2023-02-21 | Regulation and control system for large-scale flexible load resources |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116207748A true CN116207748A (en) | 2023-06-02 |
Family
ID=86507277
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310142525.8A Withdrawn CN116207748A (en) | 2023-02-21 | 2023-02-21 | Regulation and control system for large-scale flexible load resources |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116207748A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117595345A (en) * | 2024-01-17 | 2024-02-23 | 国网山西省电力公司运城供电公司 | Work realization method and device of light storage straight-flexible system |
-
2023
- 2023-02-21 CN CN202310142525.8A patent/CN116207748A/en not_active Withdrawn
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117595345A (en) * | 2024-01-17 | 2024-02-23 | 国网山西省电力公司运城供电公司 | Work realization method and device of light storage straight-flexible system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113193547B (en) | Day-ahead-day cooperative scheduling method and system for power system considering uncertainty of new energy and load interval | |
| CN111697578B (en) | Multi-target energy storage-containing regional power grid operation control method | |
| CN111244988B (en) | Electric automobile considering distributed power supply and energy storage optimization scheduling method | |
| CN110829408B (en) | Multi-domain scheduling method considering energy storage power system based on power generation cost constraint | |
| CN109617138A (en) | A kind of independent energy management method for micro-grid considering stochastic prediction error | |
| CN105225022A (en) | A kind of economy optimizing operation method of cogeneration of heat and power type micro-capacitance sensor | |
| CN113381416B (en) | Peak regulation method and system with participation of multi-type flexible loads | |
| CN115498668A (en) | Optimization method of comprehensive energy system | |
| CN110889581A (en) | Electric vehicle-participated transformer area optimal scheduling method and system | |
| CN116760008A (en) | Multi-time-scale active and reactive coordination scheduling method considering load characteristics | |
| CN114421460A (en) | Multifunctional power grid dispatching system and method containing electric automobile aggregators | |
| CN110707756B (en) | Photo-thermal power station day-ahead peak shaving optimal control method for high-proportion wind power access power grid | |
| CN116207748A (en) | Regulation and control system for large-scale flexible load resources | |
| CN111682531A (en) | PL-IMOCS-based wind, light, water and fire primary energy complementation short-term optimization scheduling method and device | |
| CN115036914A (en) | Power grid energy storage double-layer optimization method and system considering flexibility and new energy consumption | |
| CN110336308B (en) | Opportunity constraint-based active power distribution network economic dispatching method | |
| CN109687518B (en) | Optimized scheduling method for household micro-grid system | |
| CN111934361A (en) | Source network coordinated peak regulation optimization strategy evaluation method | |
| CN116316812A (en) | Multi-time-scale risk scheduling method for high-proportion distributed energy access transmission and distribution network | |
| CN115619153A (en) | Response method and device considering distributed resource operation characteristics under dynamic aggregation | |
| Zec et al. | Load management in an off-grid hybrid PV–wind–battery system using the power flow control algorithm and fuzzy logic controller | |
| CN114400652A (en) | Multi-energy power generation optimization scheduling method considering active participation of nuclear power in peak shaving | |
| CN114398777A (en) | Power system flexibility resource allocation method based on Bashi game theory | |
| Zang et al. | An Economical Optimization Method for Active Power With Variable Droop Control Considering Frequency Regulation Costs in Integrated Energy Systems | |
| CN117293801B (en) | Source-load coordination scheduling method considering fine modeling of electric arc furnace load |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| WW01 | Invention patent application withdrawn after publication | ||
| WW01 | Invention patent application withdrawn after publication |
Application publication date: 20230602 |