CN111600303A - Grid-connected type wind/light/storage micro-grid optimization configuration method based on improved multivariate universe algorithm - Google Patents
Grid-connected type wind/light/storage micro-grid optimization configuration method based on improved multivariate universe algorithm Download PDFInfo
- Publication number
- CN111600303A CN111600303A CN202010545215.7A CN202010545215A CN111600303A CN 111600303 A CN111600303 A CN 111600303A CN 202010545215 A CN202010545215 A CN 202010545215A CN 111600303 A CN111600303 A CN 111600303A
- Authority
- CN
- China
- Prior art keywords
- grid
- universe
- algorithm
- micro
- 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.)
- Pending
Links
- 238000005457 optimization Methods 0.000 title claims abstract description 19
- 238000003860 storage Methods 0.000 title claims abstract description 16
- 238000000034 method Methods 0.000 title claims abstract description 13
- JJWKPURADFRFRB-UHFFFAOYSA-N carbonyl sulfide Chemical compound O=C=S JJWKPURADFRFRB-UHFFFAOYSA-N 0.000 claims abstract description 13
- 238000013486 operation strategy Methods 0.000 claims abstract description 9
- 230000005611 electricity Effects 0.000 claims abstract description 3
- 230000035772 mutation Effects 0.000 claims description 2
- 238000010248 power generation Methods 0.000 description 30
- 238000004146 energy storage Methods 0.000 description 15
- 238000005516 engineering process Methods 0.000 description 2
- 238000012423 maintenance Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 238000013459 approach Methods 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000007599 discharging Methods 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000003912 environmental pollution Methods 0.000 description 1
- 238000002789 length control Methods 0.000 description 1
- 239000011541 reaction mixture Substances 0.000 description 1
- 238000004904 shortening Methods 0.000 description 1
- 238000012795 verification Methods 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
- H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
-
- 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
- H02J3/381—Dispersed generators
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
-
- 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
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/34—Parallel operation in networks using both storage and other dc sources, e.g. providing buffering
- H02J7/35—Parallel operation in networks using both storage and other dc sources, e.g. providing buffering with light sensitive cells
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02S—GENERATION OF ELECTRIC POWER BY CONVERSION OF INFRARED RADIATION, VISIBLE LIGHT OR ULTRAVIOLET LIGHT, e.g. USING PHOTOVOLTAIC [PV] MODULES
- H02S10/00—PV power plants; Combinations of PV energy systems with other systems for the generation of electric power
- H02S10/10—PV power plants; Combinations of PV energy systems with other systems for the generation of electric power including a supplementary source of electric power, e.g. hybrid diesel-PV energy systems
- H02S10/12—Hybrid wind-PV energy systems
-
- 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
- H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/22—The renewable source being solar energy
- H02J2300/24—The renewable source being solar energy of photovoltaic origin
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/28—The renewable source being wind energy
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/50—Photovoltaic [PV] energy
- Y02E10/56—Power conversion systems, e.g. maximum power point trackers
-
- 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
- Y02E70/00—Other energy conversion or management systems reducing GHG emissions
- Y02E70/30—Systems combining energy storage with energy generation of non-fossil origin
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Life Sciences & Earth Sciences (AREA)
- Sustainable Development (AREA)
- Sustainable Energy (AREA)
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
Abstract
The invention discloses a grid-connected type wind/light/storage micro-grid optimization configuration method, and particularly relates to a grid-connected type wind/light/storage micro-grid optimization configuration method based on an improved multivariate universe algorithm. It comprises the following steps: an optimized configuration model with the electricity consumption cost, the renewable energy utilization rate and the load loss rate of the microgrid as multiple targets is established, an optimized operation strategy of a grid-connected wind/light/storage microgrid is provided, a Levy flight strategy and differential evolution are introduced to improve a multivariate cosmic algorithm, and the improved Levy flight differential multivariate cosmic algorithm is adopted to solve the capacity configuration of each distributed power supply in the model. The method can effectively reflect the running condition of the micro-grid, and provides a certain reference for the optimal configuration of the capacity of the distributed power supply of the micro-grid.
Description
Technical Field
The invention relates to a wind/light/storage grid-connected type microgrid optimization configuration method, in particular to a grid-connected type wind/light/storage microgrid optimization configuration method based on an improved multivariate universe algorithm.
Background
The development of renewable energy power generation technology and micro-grid technology effectively relieves the problems of energy supply shortage and environmental pollution. Due to the randomness and the uncontrollable property of wind and light resources, the wind and light distributed power supply and the energy storage capacity are reasonably configured in the grid-connected wind/light/energy storage micro-grid, so that the initial investment, the operation maintenance cost and the like of the micro-grid can be reduced, the reliability of micro-grid power supply and the operation safety can be improved, and the utilization rate of renewable energy in the micro-grid can be improved.
At present, the optimization configuration research of a micro-grid comprising a distributed power supply becomes a hot spot of the research at home and abroad, and a certain progress is made. The method mainly takes the output of the distributed power supply, the operation of an energy storage device, the reliability of a system and the like as constraints, takes the economical efficiency, the environmental protection and the like of the system as optimization targets, and then applies an intelligent optimization algorithm to solve the optimal configuration scheme of the distributed power supply. However, the system model is different in size, innovation points and characteristics cannot be embodied, related documents are mainly applied to an advanced algorithm, and optimization of configuration results cannot be embodied in a proper micro-grid operation environment.
Disclosure of Invention
In order to solve the technical problem of optimization configuration of the grid-connected type wind/light/storage micro-grid, the invention provides an improved multi-universe algorithm and a corresponding micro-grid optimization operation strategy, and discloses a novel optimization configuration method of the grid-connected type wind/light/storage micro-grid.
The technical scheme for solving the technical problems is as follows:
establishing an optimized configuration model with the electricity consumption cost of the micro-grid, the utilization rate of renewable energy sources and the load loss rate as multiple targets;
an optimized operation strategy of a grid-connected wind/light/storage micro-grid is provided;
improving a multi-element universe algorithm by introducing a Levy flight strategy and differential evolution;
and solving the capacity configuration of each distributed power supply in the model by adopting an improved Levis flight difference multivariate cosmic algorithm.
Drawings
FIG. 1 is a block diagram of a microgrid optimization operation strategy proposed by the present invention
FIG. 2 is a flow chart of the improved multivariate universe algorithm of the present invention
Detailed Description
The method comprises the following steps: establishing a system model, establishing a grid-connected micro-grid optimization configuration model containing wind power generation, photovoltaic power generation and energy storage by taking system reliability as a constraint condition, and including the power consumption cost f of the micro-grid1Renewable energy utilization ratio f2And rate of loss of load f3. In the power consumption cost model, the transaction condition of a micro-grid and a large-grid and the government subsidy cost of renewable energy power generation are considered;
in the formula (1), CinsTo initial investment costs, CrepFor equipment replacement costs, COMFor equipment operating maintenance costs, CtraFor the electric quantity trade cost of wind-light storage micro-grid and grid, CsubIs subsidy for government of new energy power generation, wherein W is total annual power generation amount, ICRFIs an equal-year value coefficient.
In the formula (2), P1(t),P2And (t) the output power of wind power and photovoltaic at the moment t respectively. PLAnd (t) is the load demand power at time t.
In the formula (3), PLR(T)Load power, P, cut off for wind-solar storage micro-grid at time tLAnd (T) is the total load power of the wind-solar energy storage micro-grid at the moment T, and T is 8760.
Step two: an optimized operation strategy is provided, the reasonable operation strategy can improve the utilization rate of wind-solar distributed energy, reduce the charging and discharging times of an energy storage battery, prolong the service life of the battery, reduce the power exchange between an alternating current bus and a direct current bus, reduce the energy consumption, reduce the using number of power conversion devices and improve the economy of a micro-grid.
Step three: the improved algorithm, the Levy flight strategy, is a strategy for searching in small steps and occasionally in large steps. After the Rich-dimensional flight strategy is introduced into the multi-element universe algorithm, one part of solutions can be updated near the optimal universe position, the local search speed is improved, the other part of solutions can be updated in the universe far enough away from the optimal universe, and the problem of local optimal trapping is avoided. The space position updating formula based on the Laiwei flight is as follows:
in the formula (4), the reaction mixture is,
representing the position of the optimal universe after the update of the Laiwei flight, wherein α is step length control quantity;
representing point-to-point multiplication; l (λ) represents the levey flight model with a parameter λ, where λ is a constant. Wherein the Laevir flight expression is as follows
In formula (5): in the formula, λ is usually [1,2] and is a standard gamma function.
The differential evolution algorithm has the characteristics of rapid convergence, strong robustness and the like. The method is introduced into a multi-universe algorithm, and the variation operation of differential evolution expands the optimization range and further reduces the local optimal probability. And selecting operation of the differential evolution algorithm, reserving the cosmic position with high fitness, guiding the cosmic position to approach to the optimal solution quickly, shortening the search time of the optimal cosmic position and improving the convergence speed of the algorithm.
Step four: and solving the capacity configuration of each distributed power supply in the model by adopting an improved multi-universe algorithm, and applying the configuration result to the microgrid optimization operation strategy for verification.
The improved cloudy universe algorithm comprises the following specific steps:
the method comprises the following steps: initializing parameters, and defining a solving dimension D, a universe number N, a wormhole existence probability WEP, a travel distance rate TDR, a maximum iteration step number L, a boundary upper limit ub and a boundary lower limit lb;
step two: calculating the initial position of the universe and a boundary value;
step three: calculating the expansion rate of each universe, and determining the current optimal universe according to an optimization principle;
step four: judging the current cosmic expansion rate, if the current cosmic expansion rate is less than the standard expansion rate, selecting a white hole serial number according to the roulette, and exchanging the positions of the black hole and the white hole. If the expansion rate is larger than the standard expansion rate, performing positive search or negative search on the optimal universe attachment according to the WEP (probability of universe existence);
step five: recombining the multi-element universe, and updating the changed universe position by the aid of flight of the Lai dimension;
step six: updating the wormhole existence probability WEP and the travel distance rate TDR;
step seven: and carrying out differential mutation and selection operation on the recombined universe.
Step eight: and judging whether the maximum iteration times is reached, if so, outputting the optimal expansion rate and the optimal cosmic position, and otherwise, skipping to the third step.
The following describes the optimized operation strategy of the present invention in further detail with reference to the accompanying drawings:
1) the wind power generation and photovoltaic power generation power is larger than the load demand power, the electric quantity of the energy storage battery is within the constraint of the state of charge, the system power difference is larger than the maximum charging power of the storage battery, and the system power is excessive. The wind power generation and the photovoltaic power generation supply power to the load, and simultaneously charge the battery and sell the power to the large power grid according to the system constraint condition.
2) The power of the wind power generation and the photovoltaic power generation is larger than the power required by the load, the electric quantity of the energy storage battery is within the constraint of the state of charge, and the power difference of the system is smaller than the maximum charging power of the battery, so that the wind power generation and the photovoltaic power generation supply power to the load and charge the battery at the same time.
3) And if the power of the wind power generation and the photovoltaic power generation is greater than the power required by the load and the energy storage battery is in a full-charge state, the wind power generation and the photovoltaic power generation supply power to the load, and the surplus electric quantity is sold to a large power grid according to the power selling constraint.
4) The power of wind power generation and photovoltaic power generation does not meet the power required by the load, the electric quantity of the energy storage battery is within the constraint of the state of charge, the power difference of the system is greater than the maximum discharge power of the storage battery, and the system cannot meet the load requirement. And according to the power purchasing power constraint, the system purchases power for the large power grid, and the wind power generation, the photovoltaic power generation, the energy storage battery and the large power grid supply power to the load at the same time.
5) The wind power generation and photovoltaic power generation power does not meet the load demand power, the electric quantity of the energy storage battery is within the charge state constraint, the system power difference is smaller than the maximum discharge power of the storage battery, and the system can meet the load demand. The wind power generation, photovoltaic power generation and energy storage battery supply power to the load.
6) The wind power generation and photovoltaic power generation power does not meet the load demand power, the energy storage battery is insufficient in electric quantity, at the moment, the system purchases power to the large power grid according to the power purchasing power constraint, the wind power generation, the photovoltaic power generation and the large power grid simultaneously supply power to the load, and the large power grid charges the energy storage battery.
Claims (4)
1. An optimized configuration model with the electricity consumption cost of the micro-grid, the utilization rate of renewable energy sources and the load loss rate as multiple targets is established.
2. And providing an optimized operation strategy of a grid-connected wind/light/storage micro-grid.
3. Improving a multi-element universe algorithm by introducing a Levy flight strategy and differential evolution;
the adopted improved multivariate universe algorithm comprises the following steps:
the method comprises the following steps: initializing parameters, and defining a solving dimension D, a universe number N, a wormhole existence probability WEP, a travel distance rate TDR, a maximum iteration step number L, a boundary upper limit ub and a boundary lower limit lb;
step two: calculating the initial position of the universe and a boundary value;
step three: calculating the expansion rate of each universe, and determining the current optimal universe according to an optimization principle;
step four: judging the current cosmic expansion rate, if the current cosmic expansion rate is less than the standard expansion rate, selecting a white hole serial number according to the roulette, and exchanging the positions of the black hole and the white hole. If the expansion rate is larger than the standard expansion rate, performing positive search or negative search on the optimal universe attachment according to the WEP (probability of universe existence);
step five: recombining the multi-element universe, and updating the changed universe position by the aid of flight of the Lai dimension;
step six: updating the wormhole existence probability WEP and the travel distance rate TDR;
step seven: and carrying out differential mutation and selection operation on the recombined universe.
Step eight: and judging whether the maximum iteration times is reached, if so, outputting the optimal expansion rate and the optimal cosmic position, and otherwise, skipping to the third step.
4. And solving the capacity configuration of each distributed power supply in the model by adopting an improved Levis flight difference multivariate cosmic algorithm.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010545215.7A CN111600303A (en) | 2020-06-15 | 2020-06-15 | Grid-connected type wind/light/storage micro-grid optimization configuration method based on improved multivariate universe algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010545215.7A CN111600303A (en) | 2020-06-15 | 2020-06-15 | Grid-connected type wind/light/storage micro-grid optimization configuration method based on improved multivariate universe algorithm |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111600303A true CN111600303A (en) | 2020-08-28 |
Family
ID=72184258
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010545215.7A Pending CN111600303A (en) | 2020-06-15 | 2020-06-15 | Grid-connected type wind/light/storage micro-grid optimization configuration method based on improved multivariate universe algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111600303A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113177860A (en) * | 2021-04-22 | 2021-07-27 | 湘潭大学 | Improved ant lion algorithm-based micro-grid optimization scheduling method with electric automobile participation |
| CN113858200A (en) * | 2021-09-29 | 2021-12-31 | 长春师范大学 | Group robot control method for improving multi-universe inspired by foraging behavior of slime mold |
| CN116404646A (en) * | 2023-06-08 | 2023-07-07 | 国网江西省电力有限公司电力科学研究院 | Power system scheduling method and system considering standby risk |
| CN117439190A (en) * | 2023-10-26 | 2024-01-23 | 华中科技大学 | Water, fire and wind system dispatching method, device, equipment and storage medium |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110942205A (en) * | 2019-12-05 | 2020-03-31 | 国网安徽省电力有限公司 | Short-term photovoltaic power generation power prediction method based on HIMVO-SVM |
| CN110969639A (en) * | 2019-11-21 | 2020-04-07 | 陕西师范大学 | Image segmentation method based on LFMVO optimization algorithm |
-
2020
- 2020-06-15 CN CN202010545215.7A patent/CN111600303A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110969639A (en) * | 2019-11-21 | 2020-04-07 | 陕西师范大学 | Image segmentation method based on LFMVO optimization algorithm |
| CN110942205A (en) * | 2019-12-05 | 2020-03-31 | 国网安徽省电力有限公司 | Short-term photovoltaic power generation power prediction method based on HIMVO-SVM |
Non-Patent Citations (3)
| Title |
|---|
| 方晓玉等: "一种改进的混合灰狼优化支持向量机预测算法及应用", 《激光与光电子学进展》 * |
| 王荣等: "基于改进莱维飞行粒子群算法的光伏系统MPPT方法", 《南昌大学学报(工科版)》 * |
| 窦晓波等: "并网型风光储微电网容量改进优化配置方法", 《电力自动化设备》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113177860A (en) * | 2021-04-22 | 2021-07-27 | 湘潭大学 | Improved ant lion algorithm-based micro-grid optimization scheduling method with electric automobile participation |
| CN113858200A (en) * | 2021-09-29 | 2021-12-31 | 长春师范大学 | Group robot control method for improving multi-universe inspired by foraging behavior of slime mold |
| CN116404646A (en) * | 2023-06-08 | 2023-07-07 | 国网江西省电力有限公司电力科学研究院 | Power system scheduling method and system considering standby risk |
| CN116404646B (en) * | 2023-06-08 | 2023-10-20 | 国网江西省电力有限公司电力科学研究院 | Power system scheduling method and system considering standby risk |
| CN117439190A (en) * | 2023-10-26 | 2024-01-23 | 华中科技大学 | Water, fire and wind system dispatching method, device, equipment and storage medium |
| CN117439190B (en) * | 2023-10-26 | 2024-06-11 | 华中科技大学 | Water, fire and wind system dispatching method, device, equipment and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111600303A (en) | Grid-connected type wind/light/storage micro-grid optimization configuration method based on improved multivariate universe algorithm | |
| US9985438B2 (en) | Optimization method for independent micro-grid system | |
| CN105811409B (en) | A kind of microgrid multiple target traffic control method containing hybrid energy storage system of electric automobile | |
| CN110138006B (en) | Multi-microgrid coordinated optimization scheduling method considering new energy electric vehicle | |
| CN105868844A (en) | Multi-target operation scheduling method for micro-grid with electric vehicle hybrid energy storage system | |
| CN111737884B (en) | Multi-target random planning method for micro-energy network containing multiple clean energy sources | |
| CN105225022A (en) | A kind of economy optimizing operation method of cogeneration of heat and power type micro-capacitance sensor | |
| CN113326467B (en) | Multi-target optimization method, storage medium and optimization system for multi-station fusion comprehensive energy system based on multiple uncertainties | |
| CN111293682B (en) | Multi-microgrid energy management method based on cooperative model predictive control | |
| CN110705863A (en) | Energy optimization scheduling device, equipment and medium | |
| CN109473976A (en) | A kind of supply of cooling, heating and electrical powers type microgrid energy dispatching method and system | |
| CN111160636B (en) | CCHP type micro-grid scheduling optimization method | |
| CN111355270B (en) | Island micro-grid group capacity optimization configuration method | |
| CN110783959A (en) | New forms of energy power generation system's steady state control system | |
| CN111224432B (en) | Micro-grid optimal scheduling method and device | |
| CN104578145A (en) | Intelligent electricity consumption oriented continuous task type load energy control method | |
| CN108512238A (en) | Smart home two benches Optimization Scheduling based on Demand Side Response | |
| Wang et al. | A hybrid transmission network in pelagic islands with submarine cables and all-electric vessel based energy transmission routes | |
| JP6146624B1 (en) | Energy management system | |
| CN116128101A (en) | Virtual power plant optimization scheduling model construction method | |
| CN110334856A (en) | A kind of wind-light storage method for planning capacity based on carbon transaction mechanism | |
| Song et al. | Multi-objective optimization and long-term performance evaluation of a hybrid solar-hydrogen energy system with retired electric vehicle batteries for off-grid power and heat supply | |
| CN115622104A (en) | Mobile energy storage planning configuration method for active power distribution network | |
| CN115940284A (en) | Operation control strategy of new energy hydrogen production system considering time-of-use electricity price | |
| CN102403930B (en) | Independent type photovoltaic power generation system and capacity optimization method |
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 | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20200828 |
|
| WD01 | Invention patent application deemed withdrawn after publication |