CN112883630B - Multi-microgrid system day-ahead optimization economic dispatching method for wind power consumption - Google Patents
Multi-microgrid system day-ahead optimization economic dispatching method for wind power consumption Download PDFInfo
- Publication number
- CN112883630B CN112883630B CN202110353189.2A CN202110353189A CN112883630B CN 112883630 B CN112883630 B CN 112883630B CN 202110353189 A CN202110353189 A CN 202110353189A CN 112883630 B CN112883630 B CN 112883630B
- Authority
- CN
- China
- Prior art keywords
- grid
- micro
- microgrid
- load
- power
- 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.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 33
- 238000005457 optimization Methods 0.000 title claims abstract description 19
- 238000010248 power generation Methods 0.000 claims abstract description 50
- 239000002245 particle Substances 0.000 claims abstract description 48
- 230000005611 electricity Effects 0.000 claims abstract description 13
- 239000007789 gas Substances 0.000 claims description 91
- 239000002918 waste heat Substances 0.000 claims description 35
- 238000010438 heat treatment Methods 0.000 claims description 20
- 238000010521 absorption reaction Methods 0.000 claims description 18
- 238000001816 cooling Methods 0.000 claims description 16
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 claims description 14
- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 claims description 10
- 239000003546 flue gas Substances 0.000 claims description 10
- 230000006870 function Effects 0.000 claims description 10
- 239000003345 natural gas Substances 0.000 claims description 7
- 230000008878 coupling Effects 0.000 claims description 6
- 238000010168 coupling process Methods 0.000 claims description 6
- 238000005859 coupling reaction Methods 0.000 claims description 6
- 238000007599 discharging Methods 0.000 claims description 6
- 238000005057 refrigeration Methods 0.000 claims description 6
- 238000004364 calculation method Methods 0.000 claims description 5
- 230000005540 biological transmission Effects 0.000 claims description 3
- 239000000446 fuel Substances 0.000 claims description 3
- 239000002737 fuel gas Substances 0.000 claims description 3
- 230000003993 interaction Effects 0.000 claims description 3
- 239000011164 primary particle Substances 0.000 claims description 2
- 238000010606 normalization Methods 0.000 abstract description 4
- 230000007613 environmental effect Effects 0.000 abstract description 3
- 239000002699 waste material Substances 0.000 description 5
- 230000006978 adaptation Effects 0.000 description 3
- 238000013528 artificial neural network Methods 0.000 description 3
- 239000003344 environmental pollutant Substances 0.000 description 3
- 231100000719 pollutant Toxicity 0.000 description 3
- 241000254173 Coleoptera Species 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000003912 environmental pollution Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 238000010845 search algorithm Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000001360 synchronised effect Effects 0.000 description 1
- 230000008685 targeting Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/25—Design optimisation, verification or simulation using particle-based methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/04—Constraint-based CAD
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2113/00—Details relating to the application field
- G06F2113/04—Power grid distribution networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/06—Power analysis or power optimisation
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
- Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Evolutionary Computation (AREA)
- General Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Software Systems (AREA)
- Artificial Intelligence (AREA)
- General Health & Medical Sciences (AREA)
- Mathematical Physics (AREA)
- Computational Linguistics (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- Biophysics (AREA)
- Biomedical Technology (AREA)
- Data Mining & Analysis (AREA)
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- Geometry (AREA)
- Computer Hardware Design (AREA)
- Medical Informatics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention discloses a daily optimization economic dispatching method of a multi-micro-grid system for wind power consumption, which comprises the following steps: establishing a multi-microgrid system optimization scheduling model for wind power consumption: taking wind power consumption into consideration, taking constraint conditions of power and load acquisition in a multi-microgrid system, and taking the minimum total cost of power generation and the minimum total cost of pollution gas emission treatment of each microgrid in the multi-microgrid system within 24 hours as targets, optimizing daily economic dispatch of the multi-microgrid system, and establishing a multi-microgrid system optimization dispatch model for wind power consumption; model solving and solving optimization: and optimizing the solving set by adopting the improved multi-objective particle swarm algorithm to obtain an optimal solution set, and obtaining an optimal result in the optimal solution set by utilizing the shortest spatial normalization distance. According to the invention, the trade electricity price among the micro-grid systems is optimized, the unit output among different micro-grids is coordinated, the phenomenon of 'wind abandoning' existing in the multi-micro-grid system is solved, the requirements of multiple energy sources are met, and the overall economical efficiency and environmental friendliness are achieved.
Description
Technical Field
The invention relates to the field of power distribution network optimal scheduling, in particular to a daily optimal economic scheduling method for a multi-microgrid system for wind power consumption.
Background
In recent years, the development and utilization of clean renewable energy sources are emphasized by various countries, and most countries have put forth policies related to new energy power generation, and the core idea is to guide the development of new energy power generation, replace traditional thermal power generation with wind power and photovoltaic power generation without pollution to the environment, and finally make efforts for solving global problems such as energy crisis, greenhouse effect, atmospheric pollution and the like. However, in the process of renewable energy power generation development, a plurality of problems to be solved urgently appear, such as intermittent wind power generation, the power generation level of a wind farm is generally higher, the capacity of a single wind power generator is already up to 10MW at present, large impact is caused on a power system when high-power wind power enters the network, the wind power rejection rate is also high, when a micro-grid independently operates, the occurrence of a wind rejection phenomenon caused by excessive wind power generation is likely to occur, the generation of new energy power is also likely to be insufficient, and a treatment mode of increasing a diesel engine is adopted, so that high power generation cost and high environmental pollution are generated. According to the national energy bureau data, the national wind power on-line electric quantity in the last half year 2015 is increased by 20.7% in the same ratio, but the waste wind electric quantity is increased by 6.8% in the same ratio, the average waste wind power rate is up to 15.2%, and the waste power rate is far higher than the waste power rate of the synchronous photovoltaic power generation. Therefore, how to consume high-power wind power is a problem to be solved.
Disclosure of Invention
The technical purpose is that: aiming at the defects of excessive wind power generation, high power generation cost and serious environmental pollution in the prior art, the invention discloses a daily optimization economic dispatching method of a multi-microgrid system for wind power consumption.
The technical scheme is as follows: in order to achieve the technical purpose, the invention adopts the following technical scheme.
A daily optimization economic dispatching method for a multi-micro-grid system for wind power consumption comprises the following steps:
s1, establishing a multi-microgrid system optimization scheduling model for wind power consumption: considering wind power consumption and electric energy exchange of a large power grid to a multi-microgrid system, wherein the consideration of wind power consumption specifically refers to electric energy generated by utilizing wind energy on the premise of predicting wind power; based on power and load acquisition constraint conditions in the multi-microgrid system, controlling daily economic dispatch of the multi-microgrid system by taking the minimum total cost of power generation and the minimum total cost of pollution gas emission treatment of each microgrid in the multi-microgrid system within 24 hours as targets, and establishing a multi-microgrid system optimization scheduling model for wind power consumption;
s2, solving and optimizing the model by adopting an improved multi-target particle swarm algorithm: converting the power and load constraint conditions in the multi-microgrid system into equality constraint and inequality constraint, taking the output of all output devices in the multi-microgrid system at each moment as an unknown variable, taking the exchange electric quantity and the exchange electric price at each moment between the microgrids in the multi-microgrid system as the unknown variable, setting the dimensions of all the variables as the space dimensions of each particle in a multi-target particle swarm algorithm, setting the total power generation cost and the total pollution gas emission treatment cost of the multi-microgrid system as the objective function of each particle, solving and optimizing an optimization scheduling model of the multi-microgrid system by using the improved multi-target particle swarm algorithm, and obtaining the optimal solution by using the indexes of minimum total power generation cost and minimum total pollution gas emission treatment cost of the multi-microgrid system.
Preferably, the improved multi-target particle swarm algorithm in step S2 includes: and introducing a opposition learning strategy to initialize the initial position of each particle in the particle swarm, wherein the initial position of each particle is the initial value of all unknown variables, each particle comprises the objective function of all micro-grids in the multi-micro-grid system, and the objective function of each micro-grid comprises the self power generation cost and the pollution gas emission treatment cost.
Preferably, the initializing the initial position of each particle in the particle swarm by introducing the opposite learning strategy specifically includes:
setting a particle population to contain S individuals, initializing the position of each individual in the particle population, wherein the dimension of each individual is D dimension:wherein->l i 、u i The lower and upper limits of the represented i-th dimensional variable;
building opposite points:combining the S individuals of the primary particle group to obtain 2S individuals;
x is to be i And (3) withThe obtained fitness values are compared, if +.>Is a non-dominant solution, and X i The fitness value of (2) is the dominant solution, then +.>Substituted for X i The method comprises the steps of carrying out a first treatment on the surface of the If->Is the dominant solution, and X i If the fitness value of (2) is a non-dominant solution, judging whether to update X by adopting a random function method i 。
Preferably, the multi-microgrid system in the step S1 includes a first microgrid, a second microgrid and a third microgrid which are connected by electric energy, the first microgrid includes only electric loads, and the first microgrid exchanges and supplies electric energy with the second microgrid and the third microgrid by means of the electric loads; the second micro-grid comprises a thermal load, an electric load and a cold load, wherein the thermal load, the electric load and the cold load are coupled, and the second micro-grid is subjected to electric energy exchange with the first micro-grid and the third micro-grid in a mode of combining the electric load, the thermal load and the cold load; the third micro-grid comprises an electric load and a cold load, and electric energy exchange and supply are carried out with the first micro-grid and the second micro-grid through the electric load and the cold load.
Preferably, the electrical load comprises a battery, a fan, a photovoltaic cell, and a gas turbine; the heat load comprises a gas boiler, a waste heat boiler and a heat exchange device; the cold load comprises an electric refrigerator and an absorption refrigerator;
the thermal, electrical and cold couplings specifically refer to: the gas turbine consumes natural gas to generate electric energy and generates flue gas waste heat, the flue gas waste heat can enter a waste heat boiler, and the waste heat boiler can partially enter a heat exchange device to supply heat load, and the other part of the heat can enter an absorption refrigerator to supply cold load.
Preferably, the step S1 targets a minimum total cost of power generation and a minimum total cost of pollution gas emission treatment for each micro grid in the multi-micro grid system within 24 hours, wherein the total cost of power generation includes a fuel cost, an operation management cost, a cost of electric energy trade between a large grid and the micro grid, and a cost of electric energy trade between the micro grid and the micro grid, and the pollution gas emission refers to pollution gas generated by each micro grid in the multi-micro grid system while generating power.
Preferably, in the step S1, the total cost of power generation and the total cost of pollution gas emission treatment of each micro grid in the multi-micro grid system within 24 hours are targeted, and the functional formula of the targets is:
wherein F is 1，m (x) For the total cost of power generation for the mth microgrid in a multi-microgrid system over 24 hours, m=1, 2,..m, represents the mth microgrid in the multi-microgrid system, i=1, 2,..n, represents the i-th genset in the microgrid, t=1, 2,..24, represents the t-th moment, C i，t C, generating power and operating management cost generated by the ith generating set at the t moment in the micro-grid grid，t For the power generation and operation management cost generated by the large power grid at the t moment, P j，t C for the exchange electric quantity of the jth micro-grid and the mth micro-grid at the t moment j，t For the unit price of the electricity exchanged by the jth micro-grid and the mth micro-grid at the t moment, F 2，m (x) For the emission of a contaminated gas in 24 hours for the mth microgrid in a multi-microgrid system, t=24, representing 24 hours a day, h=1, 2..r, representing the emission of the h contaminated gas in a multi-microgrid system, β i，h Represents the emission coefficient, beta, of the h-th polluted gas emitted by the i-th generator set in the micro-grid grid，h Represents the emission coefficient lambda of the h-th polluted gas discharged by a large power grid i，h Unit treatment cost, lambda for discharging h-th polluted gas to ith generator set in micro-grid grid，h Unit treatment cost for discharging h-th polluted gas for large power grid, P i，t，m And P grid，t The electric energy generated by the ith generator set of the mth micro-grid at the moment t and the electric energy generated by the large grid at the moment t are respectively indicated.
Preferably, the medium constraint in the step S2 is balance between supply and demand of electric energy, and specifically includes supply and demand balance among thermal load, electric load and cold load, and the inequality constraint includes transmission power limit between micro-grids and large-grid, storage battery capacity limit and output size limit of a generator set in the micro-grids.
Preferably, the equation constraint is calculated by the following formula:
Q EC (t)＝p EC (t)η EC
Q AC (t)＝P GT (t)η GT η WH η cooling η AC
Q GB (t)＝F GB (t)L NG η GB
Q HX (t)＝P GT (t)η GT η WH η heating η HX
Q EC (t)+Q AC (t)＝P cooling (t)
Q GB (t)+Q HX (t)＝P heating (t)
wherein p is EC (t)、η EC Respectively representing the electric power and the energy efficiency ratio consumed by the electric refrigerator; q (Q) EC (t) represents the refrigeration capacity produced by the electric refrigerator; q (Q) AC (t) represents the output refrigerating capacity of the absorption refrigerator; p (P) GT (t)、η GT Respectively representing the power generation amount and the thermoelectric ratio, eta at the time t of the gas turbine WH Represents the efficiency, eta of the waste heat boiler cooling Represents the ratio, eta, of the waste heat of the gas turbine for refrigeration AC The energy efficiency ratio of the absorption refrigerator is shown; f (F) GB (t)、L NG 、η GB The natural gas consumption, the gas heating value and the gas boiler efficiency of the gas boiler at the time t are respectively shown; η (eta) heating 、η HX The ratio of the waste heat of the fuel gas to heat and the energy efficiency ratio of the heat exchange device are respectively shown; q (Q) GB (t) is the heating capacity of the gas boiler; q (Q) HX (t) the heating capacity of the heat exchange device; p (P) cooling (t)、P heating (t) and P i (t) is the cold load power, the hot load power, and the electric load power, respectively; p (P) GT.m (t)、P WT.m (t)、P GRID (t) A method of producing a solid-state image sensorGenerating capacity of gas turbine and generating power of fan of mth micro-gridThe amount and grid interaction amount and the exchange electric quantity of other micro-grids to the mth micro-grid, m=1, 2.
Preferably, the calculation formula of the inequality constraint is:
P i，min ≤P i，t ≤P i，max
P grid，min ≤P grid，t ≤P grid，max
P ES，min ≤P ES，t ≤P ES，max
E min ≤E t ≤E max
wherein i=1, 2,..n, N represents the i-th genset in the microgrid, t=1, 2,..24, at time t, P i，min 、P i，t And P i，max Respectively representing the minimum active power, the actual active power and the maximum active power of the ith generating set at the t moment in the micro-grid, P grid，min 、P grid，t And P grid，max Respectively representing the minimum active power, the actual active power and the maximum active power exchanged by the large power grid and the micro power grid at the t moment, P ES，min 、P ES，t And P ES，max Respectively representing the minimum active power, the actual active power and the maximum active power of the storage battery at the t moment, E min 、E t And E is max The minimum value of the battery capacity, the use value of the capacity at the time t, and the maximum value are shown, respectively.
The beneficial effects are that: according to the invention, the transaction electricity prices among the multiple micro-grid systems are optimized, the unit output among different micro-grid systems is coordinated and scheduled, the phenomenon of 'wind abandon' existing in the multiple micro-grid systems can be effectively solved, the requirements of multiple energy sources are met, and the overall economical efficiency and environmental friendliness are achieved.
Drawings
FIG. 1 is a flow chart of the overall method of the present invention;
FIG. 2 is a flowchart of an improved multi-objective particle swarm algorithm according to the present invention;
fig. 3 is a schematic structural diagram of a multi-micro network system according to an embodiment of the present invention.
Detailed Description
The invention relates to a method for optimizing economic dispatch of a multi-micro-grid system for wind power consumption, which is further described and explained below with reference to the accompanying drawings and embodiments.
As shown in the attached figure 1, the daily optimization economic dispatching method of the multi-micro-grid system for wind power consumption comprises the following steps:
step one, establishing a multi-microgrid system optimization scheduling model for wind power consumption: considering wind power consumption and electric energy exchange of a large power grid to a multi-microgrid system, wherein the consideration of wind power consumption specifically refers to electric energy generated by utilizing wind energy on the premise of predicting wind power; a micro grid refers to a small power system, and a large grid refers to a large power system with which energy can be exchanged. In the present invention the large grid only considers the electrical energy exchanged with the micro-grid and not the power generation equipment. The method comprises the steps of optimizing daily economic dispatching of a multi-microgrid system by taking constraint conditions of power and load acquisition in the multi-microgrid system and taking the minimum total power generation cost and the minimum pollution gas emission of each microgrid in the multi-microgrid system within 24 hours as targets, and establishing a multi-microgrid system optimizing dispatching model for wind power consumption; and taking the trading electricity price of the electric quantity between the micro-grids as an optimization variable, and simultaneously taking the cold, heat and electric coupling between the units into consideration. The day-ahead economy mainly refers to: in 24 hours a day, the time scale is 1 hour, and the power generation cost and pollution control cost of all power generation equipment are managed.
Consider wind power consumption to be specific to: on the premise of predicting wind power, the electric energy generated by wind energy is utilized. The prediction mode mainly comprises the following steps: a combined prediction model for improving BP neural network based on time sequence and longhorn beetle whisker search algorithm is provided. Firstly, obtaining two wind power prediction models by respectively utilizing a time sequence method and a BP neural network improved by a longhorn beetle whisker search algorithm, secondly, searching weight coefficients of the two models by utilizing a particle swarm algorithm according to the principle of minimum error square sum, establishing a combined prediction model of the time sequence and the improved BP neural network, and finally, carrying out data analysis, prediction and comparison on the basis of data of a micro-grid. For the content of the prediction mode, reference may be made to the paper "predicting short-term wind power based on a combined model" published in 10 months in 2020.
The multi-microgrid system comprises a first microgrid, a second microgrid and a third microgrid which are connected through electric energy, wherein the first microgrid only comprises an electric load, and the first microgrid exchanges and supplies electric energy with the second microgrid and the third microgrid in an electric load mode; the second micro-grid comprises a thermal load, an electric load and a cold load, wherein the thermal load, the electric load and the cold load are coupled, and the second micro-grid is subjected to electric energy exchange with the first micro-grid and the third micro-grid in a mode of combining the electric load, the thermal load and the cold load; the third micro-grid comprises an electric load and a cold load, and electric energy exchange and supply are carried out with the first micro-grid and the second micro-grid through the electric load and the cold load.
The thermal load, electrical load and cold load coupling modes of the multi-micro-grid system comprise: the gas turbine consumes natural gas to generate electric energy and generates heat, and part of the heat in the waste heat boiler enters the heat exchange device to supply heat load, and the other part of the heat enters the absorption refrigerator to supply cold load.
The electric load, namely the power supply device is a storage battery, a fan, a photovoltaic cell and a gas turbine;
the heat load, i.e. the heating device, comprises: the heat exchange device mainly generates heat through heat in the waste heat boiler to supply heat load, and the waste heat boiler is used for collecting flue gas waste heat of the gas turbine;
the cooling load, i.e. the cooling device comprises: the electric refrigerator and the absorption refrigerator are characterized in that the absorption refrigerator is mainly used for supplying cold load through heat of a waste heat boiler, and the waste heat boiler is used for collecting flue gas waste heat of a gas turbine;
in addition, the gas turbine is also called a coupling device, and the gas turbine can consume natural gas to generate electric energy and simultaneously discharge flue gas waste heat with high temperature.
The coupling device in the invention is mainly a gas turbine, and in the initial development stage of the micro-grid, the gas turbine is mainly used for producing electric energy, and the discharged flue gas waste heat is not utilized, but the discharged flue gas waste heat is collected and conveyed to the waste heat boiler, so that the cold load and the heat load can be supplied, and the burden of an electric refrigerator and the gas boiler is reduced.
The output of the heat load and the cold load adopts a mode of 'hot fixed electricity' and 'cold fixed electricity'. Taking gas turbine, absorption refrigerator, heat exchanger, gas boiler and electric refrigerator as examples, the output of gas turbine is fixed, so the output of absorption refrigerator and heat exchanger can be obtained, and the output of gas boiler and electric refrigerator can be calculated by subtracting the output of both heat load and cold load.
Targeting the minimum total cost of power generation and the minimum total cost of pollution gas emission treatment within 24 hours for each micro grid in the multi-micro grid system, wherein the total cost of power generation comprises fuel cost, operation management cost, electric energy transaction cost between a large grid and the micro grid and between the micro grid and the micro grid, and the pollution gas emission refers to pollution gas generated by each micro grid in the multi-micro grid system during power generation, and the calculation formula is as follows:
wherein F is 1，m (x) The method is a solution represented by x for the total power generation cost of an mth micro-grid in the multi-micro-grid system within 24 hours, and the power output of all output devices in the current micro-grid at each moment, the exchange electric quantity and the exchange electricity price at each moment are used as one particle in a multi-target particle swarm in the next step. m=1, 2,..m, M, i=1, 2,..n, N, t=1, 2,..24, t, C, at time t, represents the M-th microgrid in the multi-microgrid system i，t Generating and operating tube for ith generating set in micro-grid at the t momentManagement cost, C grid，t For the power generation and operation management cost generated by the large power grid at the t moment, P j，t C for the exchange electric quantity of the jth micro-grid and the mth micro-grid at the t moment j，t For the unit price of the electricity exchanged by the jth micro-grid and the mth micro-grid at the t moment, F 2，m (x) For the emission of a pollutant gas within 24 hours for the mth microgrid in a multi-microgrid system, t=24, representing 24 hours a day, h=1, 2..r, representing the emission of the h pollutant gas in the multi-microgrid system, the type of pollutant gas comprising NOx, SO 2 ；β i，h Represents the emission coefficient, beta, of the h-th polluted gas emitted by the i-th generator set in the micro-grid grid，h Represents the emission coefficient lambda of the h-th polluted gas discharged by a large power grid i，h Unit treatment cost, lambda for discharging h-th polluted gas to ith generator set in micro-grid grid，h Unit treatment cost for discharging h-th polluted gas for large power grid, P i，t，n，m And P grid，t，m The electric energy generated by the ith generator set of the mth micro-grid at the moment t and the electric energy generated by the large grid at the moment t are respectively indicated.
The equality constraint is balance between supply and demand of electric energy, and specifically comprises supply and demand balance among thermal load, electric load and cold load, and the inequality constraint comprises transmission power limit between micro-grids and between the micro-grids and a large grid, storage battery capacity limit and output size limit of a generator set in the micro-grids. The equation constraint is calculated as:
Q EC (t)＝p EC (t)η EC (3)
Q AC (t)＝P GT (t)η GT η WH η cooling η AC (4)
Q GB (t)＝F GB (t)L NG η GB (5)
Q HX (t)＝P GT (t)η GT η WH η heating η HX (6)
Q EC (t)+Q AC (t)＝P cooling (t) (7)
Q GB (t)+Q HX (t)＝P heating (t) (8)
wherein p is EC (t)、η EC Respectively representing the electric power and the energy efficiency ratio consumed by the electric refrigerator; q (Q) EC (t) represents the refrigeration capacity produced by the electric refrigerator; q (Q) AC (t) represents the output refrigerating capacity of the absorption refrigerator; p (P) GT (t)、η GT Respectively representing the power generation amount and the thermoelectric ratio, eta at the time t of the gas turbine WH Represents the efficiency, eta of the waste heat boiler cooling Represents the ratio, eta, of the waste heat of the gas turbine for refrigeration AC The energy efficiency ratio of the absorption refrigerator is shown; f (F) GB (t)、L NG 、η GB The natural gas consumption, the gas heating value and the gas boiler efficiency of the gas boiler at the time t are respectively shown; η (eta) heating 、η HX The ratio of the waste heat of the fuel gas to heat and the energy efficiency ratio of the heat exchange device are respectively shown; q (Q) GB (t) is the heating capacity of the gas boiler; q (Q) HX (t) the heating capacity of the heat exchange device; p (P) cooling (t)、p Heating (t) and P i (t) is the cold load power, the hot load power, and the electric load power, respectively; p (P) GT.m (t)、P WT.m (t)、P GRID (t) A method of producing a solid-state image sensorThe power generation amount of the gas turbine, the power generation amount of the fan and the power grid interaction amount of the mth micro-grid and the exchange power amount of other micro-grids to the mth micro-grid are respectively, m=1, 2.
The calculation formula of the inequality constraint is:
P i，min ≤P i，t ≤P i，max (10)
P grid，mi n≤P grid，t ≤P grid，max (11)
P ES，min ≤P ES，t ≤P ES，max (12)
E min ≤E t ≤E max (13)
wherein i=1, 2,..n, N represents the i-th genset in the microgrid, t=1, 2,..24, at time t, P i，min 、P i，t And P i，max Respectively representing the minimum active power, the actual active power and the maximum active power of the ith generating set at the t moment in the micro-grid, P grid，min 、P grid，t And P grid，max Respectively representing the minimum active power, the actual active power and the maximum active power exchanged by the large power grid and the micro power grid at the t moment, P ES，min 、P ES，t And P ES，max Respectively representing the minimum active power, the actual active power and the maximum active power of the storage battery at the t moment, E min 、E t And E is max The minimum value of the battery capacity, the use value of the capacity at the time t, and the maximum value are shown, respectively.
Solving and optimizing the model by adopting an improved multi-target particle swarm algorithm: converting the power and load constraint conditions in the multi-microgrid system into equality constraint and inequality constraint, taking the output of all output devices in the multi-microgrid system at each moment as an unknown variable, taking the exchange electric quantity and the exchange electric price at each moment between the microgrids in the multi-microgrid system as the unknown variable, setting the dimensions of all the variables as the space dimensions of each particle in a multi-target particle swarm algorithm, setting the total power generation cost and the total pollution gas emission treatment cost of the multi-microgrid system as the objective function of each particle, solving and optimizing an optimization scheduling model of the multi-microgrid system by using the improved multi-target particle swarm algorithm, and obtaining the optimal solution by using the indexes of minimum total power generation cost and minimum total pollution gas emission treatment cost of the multi-microgrid system.
As shown in fig. 3, an improved multi-objective particle swarm algorithm is adopted to obtain an optimal solution set for a multi-micro-grid system optimal scheduling model, and the optimal result in the optimal solution set is obtained by using the spatial shortest normalization distance. And initializing the initial position of each particle in the particle swarm by introducing a opposition learning strategy, expanding the searching range as much as possible, and obtaining the best solution in the optimal solution set by using the shortest spatial normalization distance. The specific process is as follows:
1. introducing a opposition learning strategy to initialize the initial position of each particle in the particle swarm to generate an initial scheduling scheme, which specifically comprises the following steps:
first, a particle population is set to contain S individuals, the position of each individual in the particle population is initialized, and the dimension of each individual is D dimension:
wherein the method comprises the steps ofl i 、u i The lower and upper limits of the represented i-th dimensional variable;
secondly, constructing opposite points:
thus, a total of 2S individuals can be obtained. X is to be i And (3) withThe obtained fitness values are compared, if +.>Is a non-dominant solution, and X i The fitness value of (2) is the dominant solution, then +.>Substituted for X i But if->Is the dominant solution, and X i The adaptation value of (a) is a non-dominant solution, then the following is adoptedMethod for judging whether X is to be updated or not by machine function i I.e. k=rand (0, 1), when K is equal to or higher than 0.5, then +.>Substituted for X i If less than 0.5, X is maintained i Is unchanged. Thus, the distribution uniformity of the individual in the solution space can be ensured, and the solution range is further enlarged.
2. The initial solution is subjected to dominant relation calculation to select a non-dominant solution to enter an external file;
3. updating the speed and the position of the particle swarm by using a Metropolis criterion;
4. randomly selecting a non-dominant solution from the external file to be compared with the new solution, and adding the new solution into the external file if the new solution is not dominant by the original solution;
5. the external files are subjected to the re-ordering, and non-dominant solutions are selected to be left in the external files;
6. sorting the number of external file solutions exceeding the capacity congestion distance, and deleting redundant solutions;
7. judging whether the maximum algebra is reached, if so, obtaining the best solution in the optimal solution set by using the shortest spatial normalization distance, and outputting the optimal solution; if not, returning to the step 3, and updating the speed and the position of the particle swarm again.
The method for absorbing wind power mainly refers to the joint scheduling of 3 micro-grids, the exchange of electric energy can be carried out between the micro-grids and the large grid, redundant wind energy can be transmitted to places with insufficient energy, the generation of polluted gas can be avoided, meanwhile, the electricity price of the exchanged electric energy is optimized as one of optimization variables, and the multi-objective particle swarm algorithm is improved, so that the searching range is enlarged. By optimizing the exchange electricity price among the systems, economical efficiency and environmental protection are both considered under the condition of ensuring the balance of supply and demand. The invention effectively solves the problem of wind abandoning, increases the using strength of new energy and avoids the waste of energy.
Examples:
in this embodiment, a schematic structural diagram of the multi-microgrid system is shown in fig. 3, wherein a heat load is supplied through a gas boiler, a gas turbine and a heat exchange device, and a cooling load is supplied through an absorption refrigerator and an electric refrigerator. The multi-microgrid system comprises 3 microgrids, and the microgrid 1 only needs to consider electric loads, so that the multi-microgrid system comprises a storage battery, a photovoltaic cell and a fan; the micro grid 2 needs to consider the heat, electricity, and cooling load, the micro grid 3 considers the cooling load and the electricity load, the heat load is supplied by the gas boiler and the gas turbine, and the cooling load is supplied by the absorption refrigerator and the electric refrigerator.
The electric energy in the micro-grid 1 is supplied by means of a battery, a fan, photovoltaic cells, an active distribution network and an electric energy exchange between the micro-grid 2 and the micro-grid 3.
The electric energy in the micro-grid 2 is supplied by means of electric energy between fans, accumulators, gas turbines, active distribution networks and other micro-grids. The cold load in the micro-grid 2 can be supplied by an electric refrigerator on the one hand, and flue gas waste heat generated by the gas turbine is collected by a waste heat boiler on the other hand, and then the heat in the waste heat boiler is converted by an absorption refrigerator. The heat load can be heated by the gas boiler on the one hand and by the heat in the waste heat boiler on the other hand.
The supply of electrical energy in the micro-grid 3 is exchanged via gas turbines, fans, active distribution networks and other micro-grids. The heat load can be heated by the gas boiler on the one hand and by the heat in the waste heat boiler on the other hand.
The foregoing is only a preferred embodiment of the invention, it being noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the present invention, and such modifications and adaptations are intended to be comprehended within the scope of the invention.
Claims (6)
1. A daily optimization economic dispatching method of a multi-microgrid system for wind power consumption is characterized by comprising the following steps of:
s1, establishing a multi-microgrid system optimization scheduling model for wind power consumption: considering wind power consumption and electric energy exchange of a large power grid to a multi-microgrid system, wherein the consideration of wind power consumption specifically refers to electric energy generated by utilizing wind energy on the premise of predicting wind power; based on power and load acquisition constraint conditions in the multi-microgrid system, controlling daily economic dispatch of the multi-microgrid system by taking the minimum total cost of power generation and the minimum total cost of pollution gas emission treatment of each microgrid in the multi-microgrid system within 24 hours as targets, and establishing a multi-microgrid system optimization scheduling model for wind power consumption;
s2, solving and optimizing the model by adopting an improved multi-target particle swarm algorithm: converting the power and load constraint conditions in the multi-microgrid system into equality constraint and inequality constraint, taking the output of all output devices in the multi-microgrid system at each moment as an unknown variable, taking the exchange electric quantity and the exchange electric price at each moment between the microgrids in the multi-microgrid system as the unknown variable, setting the dimensions of all the variables as the space dimensions of each particle in a multi-target particle swarm algorithm, setting the total power generation cost and the total pollution gas emission treatment cost of the multi-microgrid system as the objective function of each particle, solving and optimizing an optimized scheduling model of the multi-microgrid system by using the improved multi-target particle swarm algorithm, and obtaining an optimal solution by using the indexes of minimum total power generation cost and minimum total pollution gas emission treatment cost of the multi-microgrid system;
the step S1 targets that the total cost of power generation and the total cost of pollution gas emission treatment of each micro grid in the multi-micro grid system within 24 hours are minimum, wherein the total cost of power generation comprises fuel cost, operation management cost, electric energy transaction cost between a large grid and the micro grid and between the micro grid and the micro grid, and the pollution gas emission refers to pollution gas generated by each micro grid in the multi-micro grid system during power generation;
in the step S1, the total cost of power generation and the total cost of pollution gas emission treatment of each micro grid in the multi-micro grid system within 24 hours are targeted, and the functional formula of the targets is as follows:
wherein F is 1，m (x) For the total cost of power generation for the mth microgrid in a multi-microgrid system over 24 hours, m=1, 2,..m, represents the mth microgrid in the multi-microgrid system, i=1, 2,..n, represents the i-th genset in the microgrid, t=1, 2,..24, represents the t-th moment, C i，t C, generating power and operating management cost generated by the ith generating set at the t moment in the micro-grid grid，t For the power generation and operation management cost generated by the large power grid at the t moment, P j，t C for the exchange electric quantity of the jth micro-grid and the mth micro-grid at the t moment j，t For the unit price of the electricity exchanged by the jth micro-grid and the mth micro-grid at the t moment, F 2，m (x) For the emission of a contaminated gas in 24 hours for the mth microgrid in a multi-microgrid system, t=24, representing 24 hours a day, h=1, 2..r, representing the emission of the h contaminated gas in a multi-microgrid system, β i，h Represents the emission coefficient, beta, of the h-th polluted gas emitted by the i-th generator set in the micro-grid grid，h Represents the emission coefficient lambda of the h-th polluted gas discharged by a large power grid i，h Unit treatment cost, lambda for discharging h-th polluted gas to ith generator set in micro-grid grid，h Unit treatment cost for discharging h-th polluted gas for large power grid, P i，t，m And P grid，t The electric energy generated by the ith generator set of the mth micro-grid at the moment t and the electric energy generated by the large grid at the moment t are respectively represented;
the improved multi-target particle algorithm in the step S2 includes: introducing a opposition learning strategy to initialize the initial position of each particle in the particle swarm, wherein the initial position of each particle is the initial value of all unknown variables, each particle comprises the objective function of all micro-grids in the multi-micro-grid system, and the objective function of each micro-grid comprises the self power generation cost and the pollution gas emission treatment cost;
the method for initializing the initial position of each particle in the particle swarm by introducing the opposite learning strategy specifically comprises the following steps:
setting a particle population to contain S individuals, initializing the position of each individual in the particle population, wherein the dimension of each individual is D dimension:wherein->l i 、u i The lower and upper limits of the represented i-th dimensional variable;
building opposite points:combining the S individuals of the primary particle group to obtain 2S individuals;
x is to be i And (3) withThe obtained fitness values are compared, if +.>Is a non-dominant solution, and X i The fitness value of (2) is the dominant solution, then +.>Substituted for X i The method comprises the steps of carrying out a first treatment on the surface of the If->Is the dominant solution, and X i If the fitness value of (2) is a non-dominant solution, judging whether to update X by adopting a random function method i 。
2. The day-ahead optimized economic dispatch method for a multi-microgrid system for wind power consumption according to claim 1, wherein the method comprises the following steps: the multi-microgrid system in the step S1 comprises a first microgrid, a second microgrid and a third microgrid which are connected through electric energy, wherein the first microgrid only comprises an electric load, and the first microgrid exchanges and supplies electric energy with the second microgrid and the third microgrid in an electric load mode; the second micro-grid comprises a thermal load, an electric load and a cold load, wherein the thermal load, the electric load and the cold load are coupled, and the second micro-grid exchanges and supplies electric energy with the first micro-grid and the third micro-grid in a mode of combining the electric load, the thermal load and the cold load; the third micro-grid comprises an electric load and a cold load, and electric energy exchange and supply are carried out with the first micro-grid and the second micro-grid through the electric load and the cold load.
3. The day-ahead optimized economic dispatch method for a multi-microgrid system for wind power consumption according to claim 2, wherein the method comprises the following steps of: the electric load comprises a storage battery, a fan, a photovoltaic cell and a gas turbine; the heat load comprises a gas boiler, a waste heat boiler and a heat exchange device; the cold load comprises an electric refrigerator and an absorption refrigerator;
the thermal, electrical and cold couplings specifically refer to: the gas turbine consumes natural gas to generate electric energy and generates flue gas waste heat, the flue gas waste heat can enter a waste heat boiler, and the waste heat boiler can partially enter a heat exchange device to supply heat load, and the other part of the heat can enter an absorption refrigerator to supply cold load.
4. The day-ahead optimized economic dispatch method for a multi-microgrid system for wind power consumption according to claim 1, wherein the method comprises the following steps: the medium constraint in the step S2 is balance between supply and demand of electric energy, and specifically includes supply and demand balance among thermal load, electric load and cold load, and the inequality constraint includes transmission power limitation between micro-grids and a large grid, storage battery capacity limitation and output size limitation of a generator set in the micro-grids.
5. The method for day-ahead optimized economic dispatch of a multi-microgrid system for wind power consumption according to claim 4, wherein the equation constraint is calculated according to the following formula:
Q EC (t)＝p EC (t)η EC
Q AC (t)＝P GT (t)η GT η WH η cooling η AC
Q GB (t)＝F GB (t)L NG η GB
Q HX (t)＝P GT (t)η GT η WH η heating η HX
Q EC (t)+Q AC (t)＝P cooling (t)
Q GB (t)+Q HX (t)＝P heating (t)
wherein p is EC (t)、η EC Respectively representing the electric power and the energy efficiency ratio consumed by the electric refrigerator; q (Q) EC (t) represents the refrigeration capacity produced by the electric refrigerator; q (Q) AC (t) represents the output refrigerating capacity of the absorption refrigerator; p (P) GT (t)、η GT Respectively representing the power generation amount and the thermoelectric ratio, eta at the time t of the gas turbine WH Represents the efficiency, eta of the waste heat boiler cooling Represents the ratio, eta, of the waste heat of the gas turbine for refrigeration AC The energy efficiency ratio of the absorption refrigerator is shown; f (F) GB (t)、L NG 、η GB The natural gas consumption, the gas heating value and the gas boiler efficiency of the gas boiler at the time t are respectively shown; η (eta) heating 、η HX The ratio of the waste heat of the fuel gas to heat and the energy efficiency ratio of the heat exchange device are respectively shown; q (Q) GB (t) is the heating capacity of the gas boiler; q (Q) HX (t) the heating capacity of the heat exchange device; p (P) cooling (t)、P heating (t) and P i (t) is the cold load power, the hot load power, and the electric load power, respectively; p (P) GT.m (t)、P WT.m (t)、P GRID (t) A method of producing a solid-state image sensorThe power generation amount of the gas turbine, the power generation amount of the fan and the power grid interaction amount of the mth micro-grid and the exchange power amount of other micro-grids to the mth micro-grid are respectively, m=1, 2.
6. The method for day-ahead optimized economic dispatch of a multi-microgrid system for wind power consumption according to claim 4, wherein the calculation formula of the inequality constraint is:
P i，min ≤P i，t ≤P i，max
P grid，min ≤P grid，t ≤P grid，max
P ES，min ≤P ES，t ≤P ES，max
E min ≤E t ≤E max
wherein i=1, 2,..n, N represents the i-th genset in the microgrid, t=1, 2,..24, at time t, P i，min 、P i，t And P i，max Respectively representing the minimum active power, the actual active power and the maximum active power of the ith generating set at the t moment in the micro-grid, P grid，min 、P grid，t And P grid，max Respectively representing the minimum active power, the actual active power and the maximum active power exchanged by the large power grid and the micro power grid at the t moment, P ES，min 、P ES，t And p ES，max Respectively representing the minimum active power, the actual active power and the maximum active power of the storage battery at the t moment, E min 、E t And E is max The minimum value of the battery capacity, the use value of the capacity at the time t, and the maximum value are shown, respectively.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110353189.2A CN112883630B (en) | 2021-03-31 | 2021-03-31 | Multi-microgrid system day-ahead optimization economic dispatching method for wind power consumption |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110353189.2A CN112883630B (en) | 2021-03-31 | 2021-03-31 | Multi-microgrid system day-ahead optimization economic dispatching method for wind power consumption |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112883630A CN112883630A (en) | 2021-06-01 |
| CN112883630B true CN112883630B (en) | 2023-10-31 |
Family
ID=76040418
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110353189.2A Active CN112883630B (en) | 2021-03-31 | 2021-03-31 | Multi-microgrid system day-ahead optimization economic dispatching method for wind power consumption |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112883630B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114035434B (en) * | 2021-11-22 | 2023-09-01 | 西南石油大学 | Operation optimization method of gas-steam combined cycle power generation system |
| CN115169950B (en) * | 2022-07-26 | 2023-03-24 | 山东大学 | Distributed cooperation method and system for electric-gas system based on multi-parameter planning |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101833607A (en) * | 2010-05-11 | 2010-09-15 | 天津大学 | Multi-objective hybrid particle swam optimization design method for double-fed wind power generator |
| CN105958482A (en) * | 2016-05-31 | 2016-09-21 | 天津天大求实电力新技术股份有限公司 | Micro-grid optimization method based on good point set quantum particle swarm algorithm |
| CN108229025A (en) * | 2018-01-04 | 2018-06-29 | 东南大学 | A kind of more microgrid active distribution system economic optimization dispatching methods of supply of cooling, heating and electrical powers type |
| CN108717594A (en) * | 2018-04-16 | 2018-10-30 | 东南大学 | A kind of more micro-grid system economic optimization dispatching methods of supply of cooling, heating and electrical powers type |
| CN110504684A (en) * | 2019-08-21 | 2019-11-26 | 东北大学 | A kind of more micro-grid systems in region Optimization Scheduling a few days ago |
| CN110571863A (en) * | 2019-08-06 | 2019-12-13 | 国网山东省电力公司经济技术研究院 | Distributed power supply maximum acceptance capacity evaluation method considering flexibility of power distribution network |
| CN111478312A (en) * | 2019-11-20 | 2020-07-31 | 国网河北省电力有限公司电力科学研究院 | Comprehensive energy cluster coordination control method for improving power grid stability |
| CN112069720A (en) * | 2020-09-18 | 2020-12-11 | 西安交通大学 | Comprehensive energy system and operation optimization method thereof |
| CN112329260A (en) * | 2020-11-24 | 2021-02-05 | 国网黑龙江省电力有限公司电力科学研究院 | Multi-energy complementary micro-grid multi-element multi-target optimization configuration and optimization operation method |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9367108B2 (en) * | 2012-06-28 | 2016-06-14 | Nec Corporation | Reduction of operational cost using energy storage management and demand response |
-
2021
- 2021-03-31 CN CN202110353189.2A patent/CN112883630B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101833607A (en) * | 2010-05-11 | 2010-09-15 | 天津大学 | Multi-objective hybrid particle swam optimization design method for double-fed wind power generator |
| CN105958482A (en) * | 2016-05-31 | 2016-09-21 | 天津天大求实电力新技术股份有限公司 | Micro-grid optimization method based on good point set quantum particle swarm algorithm |
| CN108229025A (en) * | 2018-01-04 | 2018-06-29 | 东南大学 | A kind of more microgrid active distribution system economic optimization dispatching methods of supply of cooling, heating and electrical powers type |
| CN108717594A (en) * | 2018-04-16 | 2018-10-30 | 东南大学 | A kind of more micro-grid system economic optimization dispatching methods of supply of cooling, heating and electrical powers type |
| CN110571863A (en) * | 2019-08-06 | 2019-12-13 | 国网山东省电力公司经济技术研究院 | Distributed power supply maximum acceptance capacity evaluation method considering flexibility of power distribution network |
| CN110504684A (en) * | 2019-08-21 | 2019-11-26 | 东北大学 | A kind of more micro-grid systems in region Optimization Scheduling a few days ago |
| CN111478312A (en) * | 2019-11-20 | 2020-07-31 | 国网河北省电力有限公司电力科学研究院 | Comprehensive energy cluster coordination control method for improving power grid stability |
| CN112069720A (en) * | 2020-09-18 | 2020-12-11 | 西安交通大学 | Comprehensive energy system and operation optimization method thereof |
| CN112329260A (en) * | 2020-11-24 | 2021-02-05 | 国网黑龙江省电力有限公司电力科学研究院 | Multi-energy complementary micro-grid multi-element multi-target optimization configuration and optimization operation method |
Non-Patent Citations (3)
| Title |
|---|
| Economic-environmental dispatch of microgrid based on improved quantum particle swarm optimization;Zhao Xin-gang et al.;《Energy》;1-15 * |
| 基于蜂群算法的冷热电联供型微网优化调度研究;王文静;《中国优秀硕士学位论文全文数据库 工程科技II辑》;C042-2018 * |
| 电力系统无功优化多目标处理与算法改进;陈前宇 等;《电力系统保护与控制》;129-135 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112883630A (en) | 2021-06-01 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111445090B (en) | Double-layer planning method for off-grid type comprehensive energy system | |
| CN108154309B (en) | Energy internet economic dispatching method considering multi-load dynamic response of cold, heat and electricity | |
| CN103151797B (en) | Multi-objective dispatching model-based microgrid energy control method under grid-connected operation mode | |
| CN107958300A (en) | A kind of more microgrid interconnected operation coordinated scheduling optimization methods for considering interactive response | |
| CN108009693A (en) | Grid-connected micro-capacitance sensor dual blank-holder based on two-stage demand response | |
| CN108229025A (en) | A kind of more microgrid active distribution system economic optimization dispatching methods of supply of cooling, heating and electrical powers type | |
| Liu et al. | Co-optimization of a novel distributed energy system integrated with hybrid energy storage in different nearly zero energy community scenarios | |
| CN111737884B (en) | Multi-target random planning method for micro-energy network containing multiple clean energy sources | |
| CN112883630B (en) | Multi-microgrid system day-ahead optimization economic dispatching method for wind power consumption | |
| CN106779471A (en) | A kind of multipotency interconnects alternating current-direct current mixing micro-capacitance sensor system and Optimal Configuration Method | |
| CN114330827B (en) | Distributed robust self-scheduling optimization method for multi-energy flow virtual power plant and application thereof | |
| CN111668878A (en) | Optimal configuration method and system for renewable micro-energy network | |
| CN111047097A (en) | Day-to-day rolling optimization method for comprehensive energy system | |
| Kang et al. | Feed-forward active operation optimization for CCHP system considering thermal load forecasting | |
| CN115099007B (en) | Comprehensive energy system optimized operation method based on comprehensive cost-energy consumption curve | |
| CN115170343A (en) | Distributed resource and energy storage collaborative planning method for regional comprehensive energy system | |
| CN111585279A (en) | Microgrid optimization scheduling method based on new energy consumption | |
| CN117081143A (en) | Method for promoting coordination and optimization operation of park comprehensive energy system for distributed photovoltaic on-site digestion | |
| CN115241923A (en) | Micro-grid multi-objective optimization configuration method based on snake optimization algorithm | |
| CN116432824A (en) | Comprehensive energy system optimization method and system based on multi-target particle swarm | |
| CN115759610A (en) | Multi-target planning method for source-grid and storage cooperation of power system and application thereof | |
| Ren et al. | Life-cycle-based multi-objective optimal design and analysis of distributed multi-energy systems for data centers | |
| Mohamed et al. | Modelling and environmental/economic power dispatch of microgrid using multiobjective genetic algorithm optimization | |
| CN113255224A (en) | Energy system configuration optimization method based on glowworm-illuminant algorithm | |
| CN116502921A (en) | Park comprehensive energy system optimization management system and coordination scheduling method thereof |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |