CN114884108A - Source-grid-load-storage integrated micro-grid multi-time-scale energy management optimization method - Google Patents
Source-grid-load-storage integrated micro-grid multi-time-scale energy management optimization method Download PDFInfo
- Publication number
- CN114884108A CN114884108A CN202210668523.8A CN202210668523A CN114884108A CN 114884108 A CN114884108 A CN 114884108A CN 202210668523 A CN202210668523 A CN 202210668523A CN 114884108 A CN114884108 A CN 114884108A
- Authority
- CN
- China
- Prior art keywords
- grid
- power
- load
- scheduling plan
- source
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000003860 storage Methods 0.000 title claims abstract description 61
- 238000005457 optimization Methods 0.000 title claims abstract description 58
- 238000000034 method Methods 0.000 title claims abstract description 41
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 30
- 230000006870 function Effects 0.000 claims abstract description 16
- 230000008901 benefit Effects 0.000 claims abstract description 14
- 241000255581 Drosophila <fruit fly, genus> Species 0.000 claims abstract description 12
- 230000007613 environmental effect Effects 0.000 claims abstract description 12
- 238000013486 operation strategy Methods 0.000 claims abstract description 4
- 238000005096 rolling process Methods 0.000 claims abstract description 4
- 238000010248 power generation Methods 0.000 claims description 65
- 238000004146 energy storage Methods 0.000 claims description 40
- 238000007726 management method Methods 0.000 claims description 24
- 230000003993 interaction Effects 0.000 claims description 23
- 238000013528 artificial neural network Methods 0.000 claims description 9
- 230000007774 longterm Effects 0.000 claims description 9
- 238000009826 distribution Methods 0.000 claims description 8
- 230000002441 reversible effect Effects 0.000 claims description 8
- 241000255588 Tephritidae Species 0.000 claims description 6
- 210000004556 brain Anatomy 0.000 claims description 6
- 230000005611 electricity Effects 0.000 claims description 6
- 230000008451 emotion Effects 0.000 claims description 6
- 230000008569 process Effects 0.000 claims description 6
- 230000002452 interceptive effect Effects 0.000 claims description 5
- 238000011161 development Methods 0.000 claims description 4
- 239000003990 capacitor Substances 0.000 claims description 3
- 210000001638 cerebellum Anatomy 0.000 claims description 3
- 230000006872 improvement Effects 0.000 claims description 3
- 238000013507 mapping Methods 0.000 claims description 3
- 238000011160 research Methods 0.000 claims description 3
- 230000004044 response Effects 0.000 claims description 3
- 238000007619 statistical method Methods 0.000 claims description 3
- 238000010586 diagram Methods 0.000 description 5
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 3
- 229910052799 carbon Inorganic materials 0.000 description 3
- 238000004891 communication Methods 0.000 description 2
- 230000009977 dual effect Effects 0.000 description 2
- 230000010354 integration Effects 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000007599 discharging Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000009472 formulation Methods 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 230000002269 spontaneous effect Effects 0.000 description 1
- 230000009466 transformation Effects 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
- H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/004—Generation forecast, e.g. methods or systems for forecasting future energy generation
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/007—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources
- H02J3/0075—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources for providing alternative feeding paths between load and source according to economic or energy efficiency considerations, e.g. economic dispatch
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
- H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
- H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
-
- 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
-
- 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
- H02J2300/40—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation wherein a plurality of decentralised, dispersed or local energy generation technologies are operated simultaneously
-
- 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)
- Power Engineering (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention provides a multi-time scale energy management optimization method of a micro-grid integrated with source grid charge storage, which is used for researching an energy scheduling optimization strategy of the micro-grid integrated with source grid charge storage, designing required objective functions and constraint conditions on the basis of prediction of renewable energy power and load power of the micro-grid, and making a multi-time scale mutually-matched micro-grid energy scheduling plan by utilizing an improved drosophila optimization solving algorithm; the multi-time scale optimized scheduling determines the overall operation strategy of the whole microgrid by making a scheduling plan before week and before day, and then corrects the scheduling plan before day according to the scheduling plan made by rolling optimization in day, so as to make an energy scheduling plan which has the advantages of optimal operation economy, best environmental benefit and minimum power of a large grid connecting line as an optimization target of the microgrid system; the invention can carry out multi-time scale unified management on 'source network load storage' in the microgrid.
Description
Technical Field
The invention relates to the technical field of power grid operation, in particular to a multi-time scale energy management optimization method for a micro-grid with integrated source grid charge storage.
Background
Currently, the dual carbon target accelerates the revolution of the energy industry, while also providing new opportunities and new challenges for the development of new energy industries. The distributed power supplies such as wind and light and the energy storage device are effectively integrated to form a micro-grid energy management system with source-grid-charge-storage coordination interaction, so that the problem of dual randomness of 'source' output and 'charge' switching can be solved, and the local consumption capability of the system on new energy power generation is improved; meanwhile, energy loss is reduced, energy utilization efficiency is improved, operating cost of the micro-grid is reduced, economic return period of a user is shortened, use threshold of the user is reduced, comprehensive popularization of the new energy micro-grid in society is promoted, a person is helped to participate in double carbon, national energy transformation pace is accelerated, and double carbon targets are achieved.
Existing technology and the problems (two aspects, source network charge storage and time scale):
1. the traditional power grid dispatching mode is mainly a mode that a source moves along with load, and mainly aims at the change of the load, and meets the power balance of the power grid to the maximum extent by adjusting the power output of a power supply at the power generation side of the power grid. The method cannot adapt to the condition that the existing large amount of distributed wind, light and other new energy power generation is connected into the power grid. Randomness and uncontrollable nature of new energy power generation require mutual matching of 'source network charge storage' in a power system so as to guarantee safe and reliable operation of a power grid.
2. The energy management method of the micro-grid, one of the main components of the existing novel power system, is usually performed on 'source' and 'storage', the characteristics of 'charge' transferability and 'grid' communication are not considered, the energy of the micro-grid cannot be fully utilized, and the risk of power loss of the micro-grid also exists.
3. At present, the energy scheduling method of the micro-grid is mainly formulated based on multi-time scale, and most of the methods consider formulating a scheduling plan on the basis of real-time, short-term or ultra-short-term prediction, and divide the scheduling plan into the grades of time, minute, second and millisecond, so that although the reliability of the micro-grid operation can be ensured to a certain extent, the operation cost and difficulty of the actual operation of a fine scheduling plan and the service life of energy storage equipment which is influenced by frequent charging and discharging of energy storage equipment are ignored, and the influence of a large-time scale scheduling plan (a certain time period ahead) on the system operation economy is also ignored. An interaction plan with a large power grid is made in advance for a certain period and uploaded to relevant departments, so that communication between the micro power grid and the power distribution network is more friendly, and influence on fluctuation of power of a connecting line of the micro power grid and the power distribution network due to randomness of 'sources' such as wind, light and the like is avoided.
Disclosure of Invention
The invention provides a multi-time scale energy management optimization method for a micro-grid with integrated source grid charge storage, which can carry out multi-time scale unified management on 'source grid charge storage' in the micro-grid.
The invention adopts the following technical scheme.
In a topological structure of a micro-grid, a source is new energy, the grid is an alternating-current power distribution network, a load is a user load, and the load is stored as a hybrid energy storage system, the optimization method is a micro-grid energy scheduling optimization strategy research aiming at the source grid load and storage integration, a required objective function and constraint conditions are designed on the basis of micro-grid renewable energy power and load power prediction, and a micro-grid energy scheduling plan with multiple time scales matched with each other is made by utilizing an improved drosophila optimization solving algorithm; the multi-time scale optimized scheduling determines the overall operation strategy of the whole microgrid by making a scheduling plan before week and before day, and then corrects the scheduling plan before day according to the scheduling plan made by rolling optimization in day, so as to make an energy scheduling plan which has the advantages of optimal operation economy, best environmental benefit and minimum power of a large grid connecting line as an optimization target of the microgrid system;
the optimization method comprises the following steps;
s1, determining a power prediction algorithm according to different object characteristics of the new energy of the alternating current distribution network;
step S2, establishing an optimized scheduling strategy of three time scales;
s3, performing application solution by using a drosophila optimization method based on reverse learning and sine and cosine optimization;
step S4, predicting the power of source and load before week;
s5, making a once-a-week system scheduling plan for source network load storage interaction;
step S6, predicting the power of source and load in the day ahead;
step S7, making a secondary scheduling plan of a day-ahead system for source load storage interaction;
step S8, predicting power in the day;
step S9, making a three-time scheduling plan of a daily system for source load storage interaction;
and step S10, uploading the finished final scheduling strategy to a micro-grid operation management platform of source grid load storage coordination interaction, and updating the scheduling plan periodically.
The prediction in the step S1 comprises new energy output prediction and load prediction, wherein the new energy is wind power generation or photovoltaic power generation;
the new energy output prediction adopts a statistical method for prediction, namely, the output of wind power generation and photovoltaic power generation is predicted based on numerical weather forecast and historical data statistics; obtaining the mapping relation between the environmental variable and the output power of the wind power generation and the photovoltaic power generation directly through the historical output data of the wind power generation and the photovoltaic power generation or by using the historical weather data, and predicting; in long-term and short-term prediction, a fuzzy brain emotion learning neural network with strong generalization capability is adopted, and in ultra-short-term prediction, a recursion cerebellum model neural network with strong dynamic characteristic and high response speed is adopted for prediction;
the load prediction method is to count the load historical data to predict the load power of the user and predict by adopting a fuzzy brain emotion learning neural network with strong generalization capability.
In step S2, the optimized scheduling strategies of three time scales include a primary scheduling plan, a secondary scheduling plan, and a tertiary scheduling plan of the system;
the primary scheduling plan is a system primary scheduling plan for 'source network load storage' interaction is formulated by analyzing the general purchase and total sale, the self-generation and self-utilization and the surplus network surfing in the existing new energy commercial operation mode in China and predicting the output and load conditions of wind power generation and photovoltaic power generation in the next week of the system through long-term prediction, so that a 'network' side scheduling plan is obtained in advance, and a 'network' side scheduling plan is formulated to reduce the impact of a micro-grid on a large power grid;
the secondary scheduling plan is a second source, load and storage interactive system secondary scheduling plan which is used for predicting the output and load conditions of wind power generation and photovoltaic power generation in the day ahead of the system in a short-term mode aiming at the randomness and the volatility of the wind power generation and the photovoltaic power generation in the microgrid;
the third scheduling plan is used for predicting the output and load conditions of wind power generation and photovoltaic power generation in a system day by carrying out ultra-short term prediction;
in step S2, the objective function is adopted as
In the formula, f is a target value of a dispatching plan; d is the time period of the micro-grid scheduling cycle; alpha (alpha) ("alpha") 1 ～α 4 Respectively for each scheduling period 1 Environmental benefit goal f 2 Tie stability objective with large grid f 3 in-System stability goal f 4 The weight coefficient is determined according to the actual situation of each scheduling plan;
the constraints for optimizing the scheduling policy in step S2 include the following:
the output power of the new energy of wind power generation and photovoltaic power generation can not exceed the upper and lower power limits, and is expressed by a formula as follows:
P i，min ≤P i (t)≤P i，max
a second formula;
in the formula P i，max P i，min The power supply power upper and lower limits are respectively;
the sum of the output power of a power supply in the microgrid, the output power of stored energy and the alternating current power of a large power grid incorporated with the microgrid should be equal to the load power, namely the power balance in the microgrid should be kept, and the microgrid is expressed by a formula
P pv (t)+P wind (t)+P battery (t)+P g (t)＝P load (t)
A second formula;
in the formula P pv (t) is the photovoltaic actual output power; p wind (t) wind power actual output power; p battery (t) is the actual output power of the stored energy; p g (t) is the alternating current power of the micro-grid and the large grid; p load (t) is the load power;
the charge and discharge power of the hybrid energy storage system should be within a constraint range in the charge and discharge process, and the normal operation and service life of the hybrid energy storage system are affected by the over-high or over-low charge of the stored energy, which is expressed by a formula
In the formula P battery，min Storing the minimum charging power for energy; p battery，max Storing the maximum charging power for energy storage; p battery (t) is energy storage power, and the discharge is positive and the charge is negative; SOC (system on chip) min Energy storage minimum state of charge; SOC (t) the state of charge at the time of energy storage t; SOC max Energy storage maximum state of charge.
In the step S3, aiming at the problem that the generation of the drosophila algorithm initial population individuals is random generation within a given range, which causes the initial individuals to have larger randomness and uncertainty, the algorithm optimization and convergence performance are improved by improving the initialization population through a reverse learning method, and the improvement of local development and global exploration capacity is realized; meanwhile, aiming at the problem that the fruit fly algorithm is easy to fall into a local extreme value and the convergence precision is reduced due to the fact that the fruit fly algorithm cannot fully utilize the population information, the whole search and the local search of the balanced algorithm after the error generated in the optimization process is improved are reduced by utilizing a sine and cosine optimization strategy, and the performance of the algorithm is further improved.
In step S4, each day of the week is divided into four time periods: namely 8-12 points of early peak, 16-22 points of late peak, 12-16 points of flat section, 22 points of valley section and 8 points of next day; the predicted data takes 1h as resolution, each time period is an operation cycle, and the data of each time period is predicted before the week.
In step S5, the net power curve fluctuation rate of the average power of each of the "source", "grid", "load" and "storage" in each time period is minimized through the scheduling plan, so that the scheduling plan of the "grid" side can be obtained in advance, the scheduling plan of the "grid" side is made, and the impact of the microgrid on the large power grid is reduced; and simultaneously, establishing that the operation economy of the micro-grid is optimal: and (3) consuming new energy as much as possible through the electricity purchasing cost of the large power grid, the loss of energy storage and charge and discharge and the income obtained by selling electricity of the micro-grid, and planning by one-time scheduling with the objective function of minimum interactive volatility with the grid.
In step S6, the day is divided into 96-hour time periods, one time period being 15 min. The predicted data takes 15min as resolution, 1h as running period, and the hourly data is predicted in the day ahead.
In step S7, on the basis of a primary scheduling plan, it is set that "source, load, store" does not interact with "grid" side to reduce the impact of the microgrid on the large power grid, and further, it is formulated that the economy of the microgrid is optimal: the energy storage system reduces the charge and discharge loss of a storage battery in energy storage, and the secondary scheduling plan takes the minimum net power of the average power of a source, a load and the storage in each time period, the complete output of wind power generation and photovoltaic power generation, the minimum transferable load capacity and the like as objective functions.
In step S8, the predicted data has a resolution of 1min and an operation cycle of 15 min. Predicting data per minute in real time, correcting and executing a scheduling strategy per minute, specifically: and when a 1min time period is entered, the data of the first 15min is used for predicting the data of the future 15min, but the scheduling plan is executed only in the first time period.
In step S9, on the basis of the secondary scheduling plan of the system, a tertiary scheduling plan that is optimal in terms of the economy of the microgrid is made, that is,: the three-time scheduling plan takes the minimum net power of average power in each time period, the minimum wind-light complete output, the minimum migratable load capacity and the like as objective functions in the day; the energy storage element with high power density comprises a super capacitor.
Aiming at the energy management method of the existing microgrid, the invention provides an energy management method for carrying out multi-time scale unified management on 'source grid charge storage' in the microgrid; the invention aims at two commercial operation modes of the existing new energy in China: the 'general purchase and general marketing', the 'spontaneous self-use and margin Internet access' are analyzed, the wind-light output and load conditions of the system in the next week are required to be 'long-term predicted', and a 'source network load storage' interactive system long-term scheduling plan is made, so that a 'network' side scheduling plan can be obtained in advance, a 'network' side scheduling plan is made, the plan can be submitted to relevant departments in advance, a power generation and utilization plan in the next week of the micro-grid is informed, and the impact of the micro-grid on the large power grid is reduced.
According to the invention, by a method of combining three time scales of long-term prediction, short-term prediction and ultra-short-term prediction, and simultaneously considering the accuracy of 'source' and 'load' prediction information, an energy scheduling plan with the minimum power of a large power grid connecting line as an optimization target, which has the advantages of optimal operation economy and best environmental benefit of a micro-grid system, is made, and an improved drosophila algorithm is provided for optimization solution, so that the problems and the defects in the prior art are solved.
The invention can ensure that the energy state of energy storage equipment and the like in the system is in a safe working range, prolong the service life of the equipment in the system, improve the economy, the environmental protection and the reliability of the operation of the micro-grid and provide guidance for the actual operation of the source grid and storage integrated micro-grid system.
Drawings
The invention is described in further detail below with reference to the following figures and detailed description:
FIG. 1 is a topological schematic diagram of a micro-grid system integrating source grid loading and storage;
FIG. 2 is a schematic diagram illustrating coordination and interaction between source grid and storage grid in a microgrid;
FIG. 3 is a schematic diagram of a source grid load storage integrated microgrid strategy model and functional architecture;
FIG. 4 is a schematic flow diagram of an energy management strategy within a microgrid;
fig. 5 is another flow diagram of an energy management strategy within a microgrid.
Detailed Description
As shown in the figure, in a topological structure of a micro-grid, a source is new energy, a grid is an alternating-current power distribution network, a load is a user load, and the load is stored as a hybrid energy storage system, the optimization method is a micro-grid energy scheduling optimization strategy research aiming at the source grid load and storage integration, a required objective function and constraint conditions are designed on the basis of micro-grid renewable energy power and load power prediction, and a micro-grid energy scheduling plan with multiple time scales matched with each other is worked out by utilizing an improved drosophila optimization solution algorithm; the multi-time scale optimized scheduling determines the overall operation strategy of the whole microgrid by making a scheduling plan before week and before day, and then corrects the scheduling plan before day according to the scheduling plan made by rolling optimization in day, so as to make an energy scheduling plan which has the advantages of optimal operation economy, best environmental benefit and minimum power of a large grid connecting line as an optimization target of the microgrid system;
the optimization method comprises the following steps;
s1, determining a power prediction algorithm according to different object characteristics of the new energy of the alternating current distribution network;
step S2, establishing an optimized scheduling strategy of three time scales;
s3, performing application solution by using a drosophila optimization method based on reverse learning and sine and cosine optimization;
step S4, predicting the power of source and load before week;
s5, making a once-a-week system scheduling plan for source network load storage interaction;
step S6, predicting the power of source and load in the day ahead;
step S7, making a secondary scheduling plan of a day-ahead system for source load storage interaction;
step S8, predicting power in the day;
step S9, making a three-time scheduling plan of a daily system for source load storage interaction;
and step S10, uploading the finished final scheduling strategy to a micro-grid operation management platform of source grid load storage coordination interaction, and updating the scheduling plan periodically.
The prediction in the step S1 comprises new energy output prediction and load prediction, wherein the new energy is wind power generation or photovoltaic power generation;
the new energy output prediction adopts a statistical method for prediction, namely, the output of wind power generation and photovoltaic power generation is predicted based on numerical weather forecast and historical data statistics; obtaining the mapping relation between the environmental variable and the output power of the wind power generation and the photovoltaic power generation directly through the historical output data of the wind power generation and the photovoltaic power generation or by using the historical weather data, and predicting; in long-term and short-term prediction, a fuzzy brain emotion learning neural network with strong generalization capability is adopted, and in ultra-short-term prediction, a recursion cerebellum model neural network with strong dynamic characteristic and high response speed is adopted for prediction;
the load prediction method is to count the load historical data to predict the load power of the user and predict by adopting a fuzzy brain emotion learning neural network with strong generalization capability.
In step S2, the optimized scheduling strategies of three time scales include a primary scheduling plan, a secondary scheduling plan, and a tertiary scheduling plan of the system;
the primary scheduling plan is a system primary scheduling plan for 'source network load storage' interaction is formulated by analyzing the general purchase and total sale, the self-generation and self-utilization and the surplus network surfing in the existing new energy commercial operation mode in China and predicting the output and load conditions of wind power generation and photovoltaic power generation in the next week of the system through long-term prediction, so that a 'network' side scheduling plan is obtained in advance, and a 'network' side scheduling plan is formulated to reduce the impact of a micro-grid on a large power grid;
the secondary scheduling plan is a second system secondary scheduling plan for interaction of source, load and storage, and is used for predicting the randomness and volatility of wind power generation and photovoltaic power generation in a microgrid in a short period, predicting the output and load conditions of the wind power generation and the photovoltaic power generation in the day ahead of the system and making a second system secondary scheduling plan for interaction of source, load and storage;
the third scheduling plan is used for predicting the output and load conditions of wind power generation and photovoltaic power generation in a system day by carrying out ultra-short term prediction;
in step S2, the objective function is adopted as
In the formula, f is a target value of a dispatching plan; d is the time period of the micro-grid scheduling cycle; alpha is alpha 1 ～α 4 Respectively for each scheduling period 1 Environmental benefit goal f 2 Tie stability objective with large grid f 3 in-System stability goal f 4 The weight coefficient is determined according to the actual situation of each scheduling plan;
the constraints for optimizing the scheduling policy in step S2 include the following:
the output power of the new energy of wind power generation and photovoltaic power generation can not exceed the upper and lower power limits, and is expressed by a formula as follows:
P i，min ≤P i (t)≤P i，max
a second formula;
in the formula P i，max P i，min The power supply power upper and lower limits are respectively;
the sum of the output power of a power supply in the microgrid, the output power of stored energy and the alternating current power of a large power grid incorporated with the microgrid should be equal to the load power, namely the power balance in the microgrid should be kept, and the microgrid is expressed by a formula
P pv (t)+P wind (t)+P battery (t)+P g (t)＝P load (t)
A second formula;
in the formula P pv (t) is the photovoltaic actual output power; p wind (t) wind power actual output power; p battery (t) is the actual output power of the stored energy; p g (t) is the alternating current power of the micro-grid and the large grid; p load (t) is the load power;
the charge and discharge power of the hybrid energy storage system should be within a constraint range in the charge and discharge process, and the normal operation and service life of the hybrid energy storage system are affected by the over-high or over-low charge of the stored energy, which is expressed by a formula
In the formula P battery,min Storing the minimum charging power for energy; p battery,max Storing the maximum charging power for energy storage; p battery (t) is energy storage power, and the discharge is positive and the charge is negative; SOC min Energy storage minimum state of charge; SOC (t) the state of charge at the time of energy storage t; SOC max Energy storage maximum state of charge.
In the step S3, aiming at the problem that the generation of the drosophila algorithm initial population individuals is random generation within a given range, which causes the initial individuals to have larger randomness and uncertainty, the algorithm optimization and convergence performance are improved by improving the initialization population through a reverse learning method, and the improvement of local development and global exploration capacity is realized; meanwhile, aiming at the problem that the fruit fly algorithm is easy to fall into a local extreme value and the convergence precision is reduced due to the fact that the fruit fly algorithm cannot fully utilize the population information, the whole search and the local search of the balanced algorithm after the error generated in the optimization process is improved are reduced by utilizing a sine and cosine optimization strategy, and the performance of the algorithm is further improved.
In step S4, each day of the week is divided into four time periods: namely 8-12 points of early peak, 16-22 points of late peak, 12-16 points of flat section, 22 points of valley section and 8 points of next day; the predicted data takes 1h as resolution, each time period is an operation cycle, and the data of each time period is predicted before the week.
In step S5, the net power curve fluctuation rate of the average power of each of the "source", "grid", "load" and "storage" in each time period is minimized through the scheduling plan, so that the scheduling plan of the "grid" side can be obtained in advance, the scheduling plan of the "grid" side is made, and the impact of the microgrid on the large power grid is reduced; and simultaneously, establishing that the operation economy of the micro-grid is optimal: and (3) consuming new energy as much as possible through the electricity purchasing cost of the large power grid, the loss of energy storage and charge and discharge and the income obtained by selling electricity of the micro-grid, and planning by one-time scheduling with the objective function of minimum interactive volatility with the grid.
In step S6, the day is divided into 96-hour time periods, one time period being 15 min. The predicted data takes 15min as resolution, 1h as running period, and the hourly data is predicted in the day ahead.
In step S7, on the basis of a primary scheduling plan, it is set that "source, load, store" does not interact with "grid" side to reduce the impact of the microgrid on the large power grid, and further, it is formulated that the economy of the microgrid is optimal: the energy storage system reduces the charge and discharge loss of a storage battery in energy storage, and the secondary scheduling plan takes the minimum net power of the average power of a source, a load and the storage in each time period, the complete output of wind power generation and photovoltaic power generation, the minimum transferable load capacity and the like as objective functions.
In step S8, the predicted data has a resolution of 1min and a running period of 15 min. Predicting data per minute in real time, correcting and executing a scheduling strategy per minute, specifically: and when a 1min time period is entered, the data of the first 15min is used for predicting the data of the future 15min, but the scheduling plan is executed only in the first time period.
In step S9, on the basis of the secondary scheduling plan of the system, a tertiary scheduling plan that is optimal in terms of the economy of the microgrid is made, that is,: the three-time scheduling plan takes the minimum net power of average power in each time period, the minimum wind-light complete output, the minimum migratable load capacity and the like as objective functions in the day; the energy storage element with high power density comprises a super capacitor.
In this example, in step S3, in the scheduling policy optimization algorithm, a RL-SC foa (based On Reverse Learning and Sine Cosine Fly optimization) is proposed, that is, the application solution is performed by using a drosophila optimization method based On Reverse Learning and Sine and Cosine optimization. The FOA algorithm is one of representative algorithms of group intelligent optimization algorithms, and has the advantages of simple structure, easiness in practical application, high solving speed, low calculation cost and the like.
And step S10, finishing the final scheduling strategy formulation, and updating the scheduling plan every min after uploading to the micro-grid operation management platform of the source grid load storage coordination interaction.
Claims (10)
1. In a micro-grid topological structure, a source is new energy, a grid is an alternating-current power distribution network, load is user load, and storage is a hybrid energy storage system, and the method is characterized in that: the optimization method is a research aiming at a micro-grid energy scheduling optimization strategy integrating source grid load storage, a required objective function and a constraint condition are designed on the basis of micro-grid renewable energy power and load power prediction, and a micro-grid energy scheduling plan with multiple time scales matched with each other is made by utilizing an improved drosophila optimization solving algorithm; the multi-time scale optimized scheduling determines the overall operation strategy of the whole microgrid by making a scheduling plan before week and before day, and then corrects the scheduling plan before day according to the scheduling plan made by rolling optimization in day, so as to make an energy scheduling plan which has the advantages of optimal operation economy, best environmental benefit and minimum power of a large grid connecting line as an optimization target of the microgrid system;
the optimization method comprises the following steps;
s1, determining a power prediction algorithm according to different object characteristics of the new energy of the alternating current distribution network;
step S2, establishing an optimized scheduling strategy of three time scales;
s3, performing application solution by using a drosophila optimization method based on reverse learning and sine and cosine optimization;
step S4, predicting the power of source and load before week;
s5, making a once-a-week system scheduling plan for source network load storage interaction;
step S6, predicting the power of source and load in the day ahead;
step S7, making a secondary scheduling plan of a day-ahead system for source load storage interaction;
step S8, predicting power in the day;
step S9, making a three-time scheduling plan of a daily system for source load storage interaction;
and step S10, uploading the finished final scheduling strategy to a micro-grid operation management platform of source grid load storage coordination interaction, and updating the scheduling plan periodically.
2. The source grid charge-storage integrated microgrid multi-time scale energy management optimization method of claim 1, characterized in that: the prediction in the step S1 comprises new energy output prediction and load prediction, wherein the new energy is wind power generation or photovoltaic power generation;
the new energy output prediction adopts a statistical method for prediction, namely, the output of wind power generation and photovoltaic power generation is predicted based on numerical weather forecast and historical data statistics; obtaining the mapping relation between the environmental variable and the output power of the wind power generation and the photovoltaic power generation directly through the historical output data of the wind power generation and the photovoltaic power generation or by using the historical weather data, and predicting; in long-term and short-term prediction, a fuzzy brain emotion learning neural network with strong generalization capability is adopted, and in ultra-short-term prediction, a recursion cerebellum model neural network with strong dynamic characteristic and high response speed is adopted for prediction;
the load prediction method is to count the load historical data to predict the load power of the user and predict by adopting a fuzzy brain emotion learning neural network with strong generalization capability.
3. The source grid charge-storage integrated microgrid multi-time scale energy management optimization method of claim 1, characterized in that: in step S2, the optimized scheduling strategies of three time scales include a primary scheduling plan, a secondary scheduling plan, and a tertiary scheduling plan of the system;
the primary scheduling plan is a system primary scheduling plan for 'source network load storage' interaction is formulated by analyzing the general purchase and total sale, the self-generation and self-utilization and the surplus network surfing in the existing new energy commercial operation mode in China and predicting the output and load conditions of wind power generation and photovoltaic power generation in the next week of the system through long-term prediction, so that a 'network' side scheduling plan is obtained in advance, and a 'network' side scheduling plan is formulated to reduce the impact of a micro-grid on a large power grid;
the secondary scheduling plan is a second system secondary scheduling plan for interaction of source, load and storage, and is used for predicting the randomness and volatility of wind power generation and photovoltaic power generation in a microgrid in a short period, predicting the output and load conditions of the wind power generation and the photovoltaic power generation in the day ahead of the system and making a second system secondary scheduling plan for interaction of source, load and storage; the third scheduling plan is used for predicting the output and load conditions of wind power generation and photovoltaic power generation in a system day by carrying out ultra-short term prediction;
in step S2, the objective function is adopted as
In the formula, f is a target value of a dispatching plan; d is the time period of the micro-grid scheduling cycle; alpha is alpha 1 ～α 4 Respectively for each scheduling period 1 Environmental benefit goal f 2 Tie stability objective with large grid f 3 in-System stability goal f 4 The weight coefficient is determined according to the actual situation of each scheduling plan;
the constraints for optimizing the scheduling policy in step S2 include the following:
the new energy output power of wind power generation and photovoltaic power generation can not exceed the upper and lower power limits, and is expressed by a formula as follows:
P i，min ≤P i (t)≤P i，max a second formula;
in the formula P i，max P i，min The power supply power upper and lower limits are respectively;
the sum of the output power of a power supply in the microgrid, the output power of stored energy and the alternating current power of a large power grid incorporated with the microgrid should be equal to the load power, namely the power balance in the microgrid should be kept, and the microgrid is expressed by a formula
P pv (t)+P wind (t)+P battery (t)+P g (t)＝P load (t) formula two;
in the formula P pv (t) is the photovoltaic actual output power; p wind (t) wind power actual output power; p battery (t) is the actual output power of the stored energy; p g (t) is the alternating current power of the micro-grid and the large grid; p load (t) is the load power;
the charge and discharge power of the hybrid energy storage system should be within a constraint range in the charge and discharge process, and the normal operation and service life of the hybrid energy storage system are affected by the over-high or over-low charge of the stored energy, which is expressed by a formula
In the formula P battery,min Storing the minimum charging power for energy; p battery,max Storing the maximum charging power for energy storage; p battery (t) is energy storage power, and the discharge is positive and the charge is negative; SOC min Energy storage minimum state of charge; SOC (t) the state of charge at the time of energy storage t; SOC max Energy storage maximum state of charge.
4. The source grid charge-storage integrated microgrid multi-time scale energy management optimization method of claim 1, characterized in that: in the step S3, aiming at the problem that the generation of the drosophila algorithm initial population individuals is random generation within a given range, which causes the initial individuals to have larger randomness and uncertainty, the algorithm optimization and convergence performance are improved by improving the initialization population through a reverse learning method, and the improvement of local development and global exploration capacity is realized; meanwhile, aiming at the problem that the fruit fly algorithm is easy to fall into a local extreme value and the convergence precision is reduced due to the fact that the fruit fly algorithm cannot fully utilize the population information, the whole search and the local search of the balanced algorithm after the error generated in the optimization process is improved are reduced by utilizing a sine and cosine optimization strategy, and the performance of the algorithm is further improved.
5. The source grid charge-storage integrated microgrid multi-time scale energy management optimization method of claim 1, characterized in that: in step S4, each day of the week is divided into four time periods: namely 8-12 points of early peak, 16-22 points of late peak, 12-16 points of flat section, 22 points of valley section and 8 points of next day; the predicted data takes 1h as resolution, each time period is an operation cycle, and the data of each time period is predicted before the week.
6. The source grid charge-storage integrated microgrid multi-time scale energy management optimization method of claim 1, characterized in that: in step S5, the net power curve fluctuation rate of the average power of each of the "source", "grid", "load" and "storage" in each time period is minimized through the scheduling plan, so that the scheduling plan of the "grid" side can be obtained in advance, the scheduling plan of the "grid" side is made, and the impact of the microgrid on the large power grid is reduced; and simultaneously, establishing that the operation economy of the micro-grid is optimal: and (3) consuming new energy as much as possible through the electricity purchasing cost of the large power grid, the loss of energy storage and charge and discharge and the income obtained by selling electricity of the micro-grid, and planning by one-time scheduling with the objective function of minimum interactive volatility with the grid.
7. The source grid charge-storage integrated microgrid multi-time scale energy management optimization method of claim 1, characterized in that: in step S6, the day is divided into 96-hour time periods, one time period being 15 min. The predicted data takes 15min as resolution, 1h as running period, and the hourly data is predicted in the day ahead.
8. The source grid charge-storage integrated microgrid multi-time scale energy management optimization method of claim 1, characterized in that: in step S7, on the basis of a primary scheduling plan, it is set that "source, load, store" does not interact with "grid" side to reduce the impact of the microgrid on the large power grid, and further, it is formulated that the economy of the microgrid is optimal: the energy storage system reduces the charge and discharge loss of a storage battery in energy storage, and the secondary scheduling plan takes the minimum net power of the average power of a source, a load and the storage in each time period, the complete output of wind power generation and photovoltaic power generation, the minimum transferable load capacity and the like as objective functions.
9. The source grid charge-storage integrated microgrid multi-time scale energy management optimization method of claim 1, characterized in that: in step S8, the predicted data has a resolution of 1min and a running period of 15 min. Predicting data per minute in real time, correcting and executing a scheduling strategy per minute, specifically: and when a 1min time period is entered, the data of the first 15min is used for predicting the data of the future 15min, but the scheduling plan is executed only in the first time period.
10. The source grid charge-storage integrated microgrid multi-time scale energy management optimization method of claim 1, characterized in that: in step S9, on the basis of the secondary scheduling plan of the system, a tertiary scheduling plan that is optimal in terms of the economy of the microgrid is made, that is,: the three-time scheduling plan takes the minimum net power of average power in each time period, the minimum wind-light complete output, the minimum migratable load capacity and the like as objective functions in the day; the energy storage element with high power density comprises a super capacitor.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210668523.8A CN114884108A (en) | 2022-06-14 | 2022-06-14 | Source-grid-load-storage integrated micro-grid multi-time-scale energy management optimization method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210668523.8A CN114884108A (en) | 2022-06-14 | 2022-06-14 | Source-grid-load-storage integrated micro-grid multi-time-scale energy management optimization method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114884108A true CN114884108A (en) | 2022-08-09 |
Family
ID=82681864
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210668523.8A Pending CN114884108A (en) | 2022-06-14 | 2022-06-14 | Source-grid-load-storage integrated micro-grid multi-time-scale energy management optimization method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114884108A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117614039A (en) * | 2024-01-24 | 2024-02-27 | 国网浙江省电力有限公司宁波供电公司 | Distributed resource management method, device, computer equipment and storage medium |
| CN118017604A (en) * | 2024-04-09 | 2024-05-10 | 江苏谷峰电力科技股份有限公司 | Intelligent micro-grid energy management system based on wind-solar storage integration |
| CN118017604B (en) * | 2024-04-09 | 2024-06-04 | 江苏谷峰电力科技股份有限公司 | Intelligent micro-grid energy management system based on wind-solar storage integration |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107196294A (en) * | 2017-06-16 | 2017-09-22 | 国网江苏省电力公司电力科学研究院 | Micro-capacitance sensor Multiple Time Scales adaptive energy dispatching method under the net load interaction pattern of source |
| CN108695907A (en) * | 2018-06-26 | 2018-10-23 | 四川大学 | A kind of micro-capacitance sensor Multiple Time Scales Optimization Scheduling |
| CN109301853A (en) * | 2018-12-17 | 2019-02-01 | 国网江苏省电力公司经济技术研究院 | A kind of micro-capacitance sensor Multiple Time Scales energy management method for stabilizing power swing |
| CN113937796A (en) * | 2021-09-15 | 2022-01-14 | 东北电力大学 | Multi-time scale optimization method for wind, light, storage and storage combined system |
| CN114021361A (en) * | 2021-11-10 | 2022-02-08 | 国网冀北电力有限公司 | Multi-time scale optimal scheduling method and system for load of heat storage and power generation boiler |
| CN114079285A (en) * | 2020-08-13 | 2022-02-22 | 国网江苏省电力有限公司 | Power grid dispatching optimization system and method based on multiple time scales |
-
2022
- 2022-06-14 CN CN202210668523.8A patent/CN114884108A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107196294A (en) * | 2017-06-16 | 2017-09-22 | 国网江苏省电力公司电力科学研究院 | Micro-capacitance sensor Multiple Time Scales adaptive energy dispatching method under the net load interaction pattern of source |
| CN108695907A (en) * | 2018-06-26 | 2018-10-23 | 四川大学 | A kind of micro-capacitance sensor Multiple Time Scales Optimization Scheduling |
| CN109301853A (en) * | 2018-12-17 | 2019-02-01 | 国网江苏省电力公司经济技术研究院 | A kind of micro-capacitance sensor Multiple Time Scales energy management method for stabilizing power swing |
| CN114079285A (en) * | 2020-08-13 | 2022-02-22 | 国网江苏省电力有限公司 | Power grid dispatching optimization system and method based on multiple time scales |
| CN113937796A (en) * | 2021-09-15 | 2022-01-14 | 东北电力大学 | Multi-time scale optimization method for wind, light, storage and storage combined system |
| CN114021361A (en) * | 2021-11-10 | 2022-02-08 | 国网冀北电力有限公司 | Multi-time scale optimal scheduling method and system for load of heat storage and power generation boiler |
Non-Patent Citations (1)
| Title |
|---|
| 严海波;康林贤;周冬;: "考虑随机性的微电网日前调度与储能优化模型", 电网与清洁能源, no. 11, 25 November 2019 (2019-11-25), pages 61 - 65 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117614039A (en) * | 2024-01-24 | 2024-02-27 | 国网浙江省电力有限公司宁波供电公司 | Distributed resource management method, device, computer equipment and storage medium |
| CN118017604A (en) * | 2024-04-09 | 2024-05-10 | 江苏谷峰电力科技股份有限公司 | Intelligent micro-grid energy management system based on wind-solar storage integration |
| CN118017604B (en) * | 2024-04-09 | 2024-06-04 | 江苏谷峰电力科技股份有限公司 | Intelligent micro-grid energy management system based on wind-solar storage integration |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US20200161867A1 (en) | Method, system and storage medium for load dispatch optimization for residential microgrid | |
| CN107565607B (en) | Micro-grid multi-time scale energy scheduling method based on real-time electricity price mechanism | |
| CN107423852A (en) | A kind of light storage combined plant optimizing management method of meter and typical scene | |
| CN107706932B (en) | A kind of energy method for optimizing scheduling based on dynamic self-adapting fuzzy logic controller | |
| CN116436008A (en) | Power dispatching method and terminal for optical storage charging station | |
| CN114336599B (en) | Independent micro-grid dynamic optimization scheduling method | |
| CN113807569A (en) | Fully distributed cooperative optimization method for multi-source energy storage type microgrid | |
| CN108075471B (en) | Multi-objective constraint optimization power grid scheduling strategy based on stochastic power output prediction | |
| CN114819373A (en) | Energy storage planning method for sharing hybrid energy storage power station based on cooperative game | |
| CN115205068A (en) | Energy storage optimal peak-valley time interval dividing method considering net load demand distribution | |
| CN114884108A (en) | Source-grid-load-storage integrated micro-grid multi-time-scale energy management optimization method | |
| CN114336762A (en) | Day-ahead scheduling energy storage configuration optimization method for wind-solar power generation and power grid load fluctuation | |
| TWI639962B (en) | Particle Swarm Optimization Fuzzy Logic Control Charging Method Applied to Smart Grid | |
| CN105514986A (en) | DER user bidding grid-connection method based on virtual power plant technology | |
| CN115912419A (en) | Heat storage-electricity storage cooperative scheduling method based on load aggregation quotient | |
| CN115577905A (en) | Peak regulation energy optimization scheduling method for cascade hydropower station group | |
| Arcos-Aviles et al. | Fuzzy control-based energy management system for interconnected residential microgrids using the forecasts of power generation and load demand | |
| CN109754128B (en) | Wind/light/storage/diesel microgrid optimization configuration method considering typical scene of meteorological fluctuation characteristic difference | |
| CN113255957A (en) | Quantitative optimization analysis method and system for uncertain factors of comprehensive service station | |
| Ren et al. | Decision-making approach in charging mode for electric vehicle based on cumulative prospect theory | |
| Jain et al. | A data driven approach for scheduling the charging of electric vehicles | |
| Jiang | Power distribution strategies to improve power distribution efficiency | |
| CN113675866B (en) | Dynamic gridding pyramid scheduling method for large-scale electric automobile | |
| Xuan et al. | Research on short-term load forecasting model of integral energy system considering demand side response | |
| Li et al. | Influence on Stability Analysis in Distributed Smart Grids Using Computer Aimed Digital Decision Trees |
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 |