CN108376989B - Battery energy storage power station partition control method and system based on multiple intelligent agents - Google Patents
Battery energy storage power station partition control method and system based on multiple intelligent agents Download PDFInfo
- Publication number
- CN108376989B CN108376989B CN201810149296.1A CN201810149296A CN108376989B CN 108376989 B CN108376989 B CN 108376989B CN 201810149296 A CN201810149296 A CN 201810149296A CN 108376989 B CN108376989 B CN 108376989B
- Authority
- CN
- China
- Prior art keywords
- energy storage
- storage unit
- reference value
- intelligent
- unit area
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000004146 energy storage Methods 0.000 title claims abstract description 433
- 238000000034 method Methods 0.000 title claims abstract description 36
- 238000005192 partition Methods 0.000 title claims description 10
- 238000007599 discharging Methods 0.000 claims abstract description 53
- 239000002245 particle Substances 0.000 claims description 70
- 238000004364 calculation method Methods 0.000 claims description 22
- 238000012544 monitoring process Methods 0.000 claims description 21
- 238000013500 data storage Methods 0.000 claims description 20
- 238000004422 calculation algorithm Methods 0.000 claims description 18
- 238000007726 management method Methods 0.000 claims description 12
- 238000005457 optimization Methods 0.000 claims description 8
- 210000000352 storage cell Anatomy 0.000 claims description 8
- 238000009825 accumulation Methods 0.000 claims description 5
- 238000012163 sequencing technique Methods 0.000 claims description 3
- 238000010248 power generation Methods 0.000 description 24
- 238000010586 diagram Methods 0.000 description 10
- 230000006870 function Effects 0.000 description 9
- 238000004590 computer program Methods 0.000 description 7
- 238000005516 engineering process Methods 0.000 description 7
- 230000008569 process Effects 0.000 description 5
- 238000012545 processing Methods 0.000 description 4
- 238000003860 storage Methods 0.000 description 4
- 238000011160 research Methods 0.000 description 3
- 238000013459 approach Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 238000009826 distribution Methods 0.000 description 2
- 238000012423 maintenance Methods 0.000 description 2
- 238000004088 simulation Methods 0.000 description 2
- 230000003044 adaptive effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 210000004027 cell Anatomy 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
Images
Classifications
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
-
- H02J3/382—
Abstract
A battery energy storage power station zone control method and system based on multi-agent includes: the energy storage power station main intelligent agent calculates the charge and discharge power reference values of the intelligent agents in all the energy storage unit areas according to the superior scheduling information; the energy storage power station main intelligent body compares the charging and discharging power reference value with the received working state information uploaded by all the energy storage unit area intelligent bodies, adjusts the charging and discharging power reference value according to the comparison result, and issues the charging and discharging power reference value to the energy storage unit area intelligent bodies; the energy storage unit area intelligent body issues a control instruction to the energy storage unit according to the adjusted charge-discharge power reference value and the energy storage parameter information of the energy storage unit issued by the energy storage power station main intelligent body; and the energy storage unit carries out charging and discharging according to the control instruction. The technical scheme of the invention reasonably distributes the output of the energy storage power station according to the working state of the PCS, so that the PCS keeps higher working efficiency.
Description
Technical Field
The invention relates to the fields of large-scale energy storage technology and new energy power generation, in particular to a battery energy storage power station partition control method and system based on multiple intelligent agents.
Background
With the continuous development of new energy technology, solar energy and wind energy become representatives of novel energy sources by the advantages of cleanness, no pollution, renewability and the like, and the development of photovoltaic power generation and wind power generation is promoted by the appearance of a large-scale energy storage system. The large-scale energy storage system can be matched with photovoltaic and wind turbine generators to realize functions of smooth output, peak clipping and valley filling, tracking planned output and the like, so that the controllability of power generation is increased, the randomness and the volatility of a power generation system are reduced, and the grid-connected capability of wind and light power generation is improved.
Because different energy storage unit areas formed by the PCS and the battery packs controlled by the PCS in the energy storage system generate inconsistent SOC differences along with the operation of the system, the control on the output power of the energy storage unit areas is influenced, and the original preset control requirement cannot be met. Therefore, a more stable, efficient and reliable energy storage power station control system and method are needed to cooperate with wind power and photovoltaic power to complete the power generation task of the whole power generation system. At present, Multi-Agent System (MAS) technology is applied to the fields of load prediction, power market simulation, micro-grids, fault location, active power distribution networks and the like. The Institute of Electrical and Electronics Engineers (IEEE) intelligent system has established a special working group to research the popularization and application of multi-agent technology in power systems.
Compared with other fields, the research of applying the multi-agent technology to establish the large-scale battery energy storage power station coordination control and energy management method is not mature enough. When the operation of a large-scale battery energy storage power station is controlled, a network structure system is complex, and the problem that centralized optimization control is difficult to develop exists.
Disclosure of Invention
The research for applying multi-agent technology to establish a large-scale battery energy storage power station coordination control and energy management method for solving the problems is not mature enough. The invention provides a partition control method and a system based on a multi-agent battery energy storage power station, which solve the problems of complex network structure system and the like when a large-scale battery energy storage power station is operated and controlled,
the technical scheme of the invention is as follows:
a battery energy storage power station zone control method based on multiple intelligent agents comprises the following steps:
the energy storage power station main intelligent agent calculates the charge and discharge power reference values of the intelligent agents in all the energy storage unit areas according to the superior scheduling information;
the main intelligent body of the energy storage power station compares the charging and discharging power reference value with the received working state information uploaded by the intelligent bodies of all the energy storage unit areas, adjusts the charging and discharging power reference value according to the comparison result, and issues the charging and discharging power reference value to the intelligent bodies of the energy storage unit areas;
the energy storage unit area intelligent body sends a control instruction to the energy storage unit according to the adjusted charge-discharge power reference value and the energy storage parameter information of the energy storage unit sent by the energy storage power station main intelligent body;
and the energy storage unit carries out charging and discharging according to the control instruction.
Preferably, the method for calculating the charging and discharging power reference values of the energy storage unit area intelligent agents by the energy storage power station main intelligent agent according to the superior scheduling information comprises the following steps:
and the main intelligent body of the energy storage power station calculates the charge-discharge power reference value of the intelligent body of the energy storage unit area according to the new energy power information and the power information of the scheduling requirement.
Preferably, the energy storage power station main intelligent agent compares the charging and discharging power reference value with the received working state information uploaded by all the energy storage unit area intelligent agents, adjusts the charging and discharging power reference value according to the comparison result, and issues the charging and discharging power reference value to the energy storage unit area intelligent agents, and the method includes:
the energy storage unit area intelligent agent determines a working state by calculating the maximum chargeable and dischargeable power and the equivalent SOC and uploads the working state to the energy storage station main intelligent agent;
and the main intelligent body of the energy storage power station determines an adjusted charging and discharging power reference value by comparing the charging and discharging power reference value with the working state information uploaded by the intelligent body of the energy storage unit area, and issues the adjusted charging and discharging power reference value to the intelligent body of the working energy storage unit area.
Preferably, the main agent of the energy storage power station determines the adjusted charging and discharging power reference value by comparing the charging and discharging power reference value with the working state information uploaded by the agent of the energy storage cell region, and issues the adjusted charging and discharging power reference value to the agent of the working energy storage cell region, and the method includes:
the energy storage power station main intelligent body sorts the energy storage unit areas according to the maximum chargeable and dischargeable power according to the working state information uploaded by the energy storage unit area intelligent body; accumulating the maximum chargeable and dischargeable power of the intelligent agent in each energy storage unit area according to the sequencing result;
comparing the accumulated result with a charge-discharge power reference value every time the maximum chargeable-discharge power of the intelligent body in the energy storage unit area is accumulated;
when the accumulated result is smaller than the charge-discharge power reference value and the number of the energy storage unit areas corresponding to the accumulation is smaller than the number of all the energy storage unit areas, continuing to accumulate;
when the accumulated result is smaller than the charge and discharge power reference value and the accumulated number of the corresponding energy storage unit areas is equal to the number of all the energy storage unit areas, issuing an adjusted charge and discharge power reference value;
when the accumulated result is greater than the charge-discharge power reference value, issuing the charge-discharge power reference value;
and determining the intelligent agent issued to the working energy storage unit area based on the accumulation result. Preferably, the issuing of the control instruction to the energy storage unit by the energy storage unit area agent according to the adjusted charge-discharge power reference value and the energy storage parameter information of the energy storage unit issued by the energy storage station main agent includes:
and the energy storage unit area intelligent body sends the adjusted charge and discharge power reference value and the state information of the energy storage unit to the control instruction of the energy storage unit through multi-particle group algorithm optimizing calculation according to the energy storage power station main intelligent body.
Another objective of the present invention is to provide a battery energy storage power station partition control system based on multiple intelligent agents, which includes: the energy storage power station comprises an energy storage power station main intelligent body, an energy storage unit area intelligent body and an energy storage unit;
the energy storage power station main intelligent body is used for calculating charge and discharge power reference values of all the energy storage unit area intelligent bodies according to the superior scheduling information; receiving working state information uploaded by all the energy storage unit area intelligent agents, comparing the working state information with the charge and discharge power reference value, adjusting the charge and discharge power reference value according to a comparison result, and issuing the adjusted charge and discharge power reference value to the energy storage unit area intelligent agents;
the energy storage unit area intelligent body is used for issuing a control instruction to the energy storage unit according to the adjusted charge-discharge power reference value and the energy storage parameter information of the energy storage unit issued by the energy storage power station main intelligent body;
and the energy storage unit is used for receiving the control command to perform a charging and discharging task.
Preferably, the energy storage power station main intelligent agent includes: calculation control module and data storage module
The calculation control module is used for calculating the scheduling information of the energy storage unit area and judging the energy storage unit area which works correspondingly;
the data storage module is used for storing new energy power information and power information of scheduling requirements;
preferably, the energy storage cell region intelligent agent includes: the system comprises an energy storage monitoring unit, an energy storage data storage management unit and a multi-band particle swarm calculation unit;
the energy storage monitoring unit is used for acquiring energy storage parameter information of the energy storage unit area;
the energy storage data storage management unit is used for receiving the monitoring information of the energy storage monitoring unit;
and the multi-band particle swarm computing unit is used for optimizing and computing the control instruction of the energy storage unit.
Preferably, the energy storage unit includes: PCS and a battery pack.
Compared with the closest prior art, the technical scheme provided by the invention has the following beneficial effects:
according to the technical scheme, the energy storage power station main intelligent bodies and the energy storage unit area intelligent bodies are coordinately distributed, so that the centralized optimization and control of the energy storage system are realized, meanwhile, the control method provided by the invention has the characteristics of strong expandability, high control precision and the like, the error between new energy and a scheduling instruction is fully reduced, and the tracking power generation planning capability of new energy power generation comprising the energy storage system is improved.
The technical scheme provided by the invention is combined with a multi-agent particle swarm algorithm, a plurality of energy storage unit area agents are established, the total energy storage requirement at each moment is primarily distributed to each energy storage unit according to the working state of PCS in the energy storage unit by taking the working efficiency of the energy storage PCS as a standard to be maximized, and meanwhile, the total energy storage requirement is distributed to each energy storage unit area agent again by using the principles of competition among the agents, self-learning and the like so as to complete the working tasks of energy storage and power generation. And the output power of the energy storage power station is reasonably distributed according to the working state of the PCS, so that the PCS keeps higher working efficiency. And controlling the SOC of the energy storage system to keep a consistent or higher tracking power generation plan by adjusting parameter information of the multi-agent particle swarm optimization.
Drawings
FIG. 1 is a diagram of a large scale battery energy storage power station control architecture based on a multi-agent particle swarm in accordance with the present invention;
FIG. 2 is a flow chart of a multi-agent particle swarm algorithm applicable to a large-scale battery energy storage power station of the present invention;
FIG. 3 is a diagram of PCS usage versus operating efficiency of the present invention;
FIG. 4 is a flow chart of a battery energy storage power station zone control method based on multi-agent.
Detailed Description
For a better understanding of the present invention, reference is made to the following description taken in conjunction with the accompanying drawings and examples.
In a large-scale battery energy storage system, the structure among energy storage units is complex, the number of battery packs and PCS is large, and the variables influencing the control method are large, aiming at the characteristic of an energy storage power station, the invention adopts the following scheme:
a large-scale energy storage power station control system based on a multi-agent particle swarm technology is characterized in that energy storage unit area agents are arranged by taking a transformer as a unit, each PCS and battery pack connected with the low-voltage side of each transformer serve as one particle in an energy storage unit, and each energy storage unit area agent forms a particle swarm. Due to the fact that a plurality of energy storage unit area intelligent agents are arranged, the whole energy storage system can form a particle swarm of the plurality of intelligent agents on the whole, namely a multi-intelligent-agent particle swarm. All the intelligent agents in the system coordinate to jointly complete power generation and energy storage tasks.
And the intelligent agent of the energy storage unit area inputs power scheduling instruction information and energy storage parameters provided by the energy storage system. The energy storage parameters include: the energy storage system is charged and discharged to limit power, the energy storage system SOC, the energy storage system limit SOC, the energy storage system output power and the PCS working state parameters. And the intelligent body in the energy storage unit area outputs power control signals including power control signals of the PCS under each transformer, the SOC of the battery pack and the like. The power control signals of the intelligent bodies in the energy storage unit area act on the power generation control device of the power station, the intelligent bodies are independent, and the power generation task of the power generation system can be independently completed through the matching between the intelligent bodies in the energy storage unit area.
From fig. 4, it can be seen that a battery energy storage power station zone control method based on multi-agent includes:
the energy storage power station main intelligent agent calculates the charge and discharge power reference values of the intelligent agents in all the energy storage unit areas according to the superior scheduling information;
the main intelligent bodies of the energy storage power station compare the charging and discharging power reference values with the received working state information uploaded by the intelligent bodies of all the energy storage unit areas, adjust the charging and discharging power reference values according to the comparison result, and issue the charging and discharging power reference values to the intelligent bodies of the energy storage unit areas;
the energy storage unit area intelligent body issues a control instruction to the energy storage unit according to the adjusted charge-discharge power reference value issued by the energy storage power station main intelligent body and the energy storage parameter information of the energy storage unit;
and the energy storage unit carries out charging and discharging according to the control instruction. .
The energy storage power station main intelligent agent calculates the charge and discharge power reference values of the intelligent agents in all the energy storage unit areas according to the superior scheduling information, and the method comprises the following steps:
and the main intelligent body of the energy storage power station calculates the charge-discharge power reference value of the intelligent body of the energy storage unit area according to the new energy power information and the power information of the scheduling requirement.
The energy storage power station main intelligent body compares the charging and discharging power reference value with the received working state information uploaded by all the energy storage unit area intelligent bodies, adjusts the charging and discharging power reference value according to the comparison result, and issues the charging and discharging power reference value to the energy storage unit area intelligent bodies, and the method comprises the following steps:
the energy storage unit area intelligent agent determines a working state by calculating the maximum chargeable and dischargeable power and the equivalent SOC and uploads the working state to the energy storage power station main intelligent agent;
and the main intelligent body of the energy storage power station determines the adjusted charging and discharging power reference value by comparing the charging and discharging power reference value with the working state information uploaded by the intelligent body of the energy storage unit area, and sends the adjusted charging and discharging power reference value to the intelligent body of the working energy storage unit area.
The main intelligent agent of energy storage power station confirms the charge-discharge power reference value of adjustment through the operating condition information of comparing charge-discharge power reference value and energy storage unit district intelligent agent upload to issue to the energy storage unit district intelligent agent of work, include:
the energy storage power station main intelligent body sorts the energy storage unit areas according to the maximum chargeable and dischargeable power according to the working state information uploaded by the energy storage unit area intelligent body;
accumulating the maximum chargeable and dischargeable power of the intelligent bodies in each energy storage unit area according to the sequencing result;
comparing the accumulated result with a charge-discharge power reference value every time the maximum chargeable-discharge power of the intelligent body in the energy storage unit area is accumulated;
when the accumulated result is smaller than the charge-discharge power reference value and the number of the accumulated corresponding energy storage unit areas is smaller than the number of all the energy storage unit areas, continuing to accumulate;
when the accumulated result is smaller than the charge and discharge power reference value and the accumulated number of the corresponding energy storage unit areas is equal to the number of all the energy storage unit areas, issuing an adjusted charge and discharge power reference value;
when the accumulated result is greater than the charge-discharge power reference value, issuing the charge-discharge power reference value;
and determining the intelligent agent issued to the working energy storage unit area based on the accumulation result.
The energy storage unit area intelligent body issues a control instruction to the energy storage unit according to the adjusted charge-discharge power reference value and the energy storage parameter information of the energy storage unit issued by the energy storage power station main intelligent body, and the method comprises the following steps:
and the energy storage unit area intelligent bodies send control instructions to the energy storage units through multi-particle group algorithm optimization calculation according to the adjusted charge and discharge power reference values sent by the energy storage power station main intelligent bodies and the state information of the energy storage units.
Specifically, in the multi-agent particle swarm optimization, the dimension of the generated particles represents the number to be solved, namely the number of the agents in the energy storage unit area. Based on a multi-agent particle swarm algorithm, the large-scale energy storage power station partition control method is provided with the following flows:
(1) the large-scale battery energy storage power station receives a planned power generation task, distributes the power generation task to each energy storage unit area intelligent body, and meanwhile, the energy storage unit area intelligent bodies acquire state information such as battery pack SOC.
(2) The main intelligent agent of the energy storage station and the intelligent agents of the energy storage unit areas mutually negotiate and communicate, and the output power of the PCS in the intelligent agents of the energy storage unit areas is distributed and determined according to the real-time condition of the SOC of each unit battery pack and the working state parameters of the PCS in each unit area.
(3) And the PCS under each energy storage unit receives a power output instruction to complete a power generation or energy storage task.
In the flow (2) of the large-scale energy storage power station control method based on the multi-agent particle swarm, the flow for determining the charging and discharging power of the energy storage power station is as follows:
(a) and each energy storage unit area intelligent agent receives the energy storage task sent by scheduling, and determines the working state (charging/discharging) of the energy storage system and the total charging and discharging power at the current moment.
(b) And each energy storage unit area intelligent body receives PCS working state parameter information, counts the information of the maximum chargeable and dischargeable power of the unit area, the average SOC of the unit area and the like, and determines the energy storage unit area participating in charging and discharging at the current moment.
(c) And the energy storage unit intelligent bodies in the energy storage unit areas combine the energy storage scheduling instruction and the related energy storage parameter information, determine the energy storage units participating in charging and discharging at the current moment according to the charging condition and the discharging condition, and preliminarily allocate the power reference values of the unit areas.
(d) And calculating a fine-tuning interval of the multi-agent particle swarm algorithm, and optimally calculating the final power reference value to be sent by each PCS corresponding to the agents in each energy storage unit area in the fine-tuning interval.
In the process (a) for determining the output power of the energy storage system, the power output of the energy storage system is determined according to the following method:
P bess ＝P plan -P 0 (1)
wherein, P 0 For real-time output of power (photovoltaic, wind power) for new energy, P plan For scheduling the demand, P bess And the charging and discharging power is supplied to the energy storage power station.
In the process (b) of determining the output power of the energy storage system, the energy storage unit area intelligent agent updates the working state parameter lambda of each PCS at the current moment according to the working state of each unit area. Under the condition that the PCS works at the maximum efficiency and the working state parameters of the PCS in each energy storage unit area are considered, the maximum chargeable and dischargeable power of each energy storage unit area is calculated. The formula is as follows:
the subscript i is the serial number of the energy storage unit area, and i is 1,2, … and n; j is PCS number in a certain energy storage unit area, and j is 1,2, …, m; p is max_i The maximum chargeable and dischargeable power is the ith energy storage unit area; p is max_ij The maximum chargeable and dischargeable power is the jth PCS in the ith energy storage unit area; eta is the power utilization rate of the energy storage PCS, and lambda is the state parameter of the energy storage PCS.
And eta is the utilization rate of the energy storage PCS, and refers to the ratio of the working power of the PCS to the maximum output power. Practice shows that PCS works in an interval with the utilization rate of 10% -90% and has the highest efficiency. For illustration, η is 0.9 in this method.
Meanwhile, the intelligent agents of the energy storage unit areas sort the equivalent SOC of the battery pack of the PCS of the energy storage unit areas from small to large during charging and from large to small during discharging according to the state parameters (fault, maintenance and the like/normal) of the PCS. The equivalent SOC is calculated as (3):
wherein m is the number of PCS in the intelligent body of a certain energy storage unit area. Accumulating the maximum chargeable and dischargeable power of the first L energy storage unit areas according to the sorting sequence, wherein the maximum chargeable and dischargeable power is as follows:
when P is max <P bess And L is<When n is less than the total number of the energy storage units, continuously accumulating;
when P is max <P bess When L is n, P max Namely, the energy storage power station reaches the limit of the maximum chargeable and dischargeable power at the current moment, and can enable P bess ＝P max ；
When P is max >P bess And stopping time and accumulating, and recording the numbers of the corresponding L energy storage unit areas. Meanwhile, setting the working state parameter lambda of each PCS in all the energy storage unit areas which are not recorded with numbers to 0, namely updating the working state parameter lambda once.
In the energy storage system output power determination process (c), the power output of the intelligent agent in each energy storage unit area is determined according to the following method:
wherein, P i * An initial reference value of the output power of the energy storage unit i, i is 1, 2. The parameter lambda is the working state parameter of the PCS, is 0 under the condition of fault, maintenance or non-participation in the control at the moment, and is 1 when the PCS can be normally used.
In the energy storage system output power determination process (d), the energy storage unit area calculates the multi-agent particle swarm algorithm fine-tuning interval according to the maximum allowable charge-discharge power and capacity of each governed PCS and the real-time condition of the battery pack SOC. When calculating the fine-tuning interval, firstly presetting an SOC reference value SOC ref And the equivalent SOC of the battery pack of each energy storage unit is adjusted, so that the SOC average value of each PCS can gradually approach and basically keep consistent in the control range of the intelligent agent in each energy storage unit area after running for a certain time. Power fine tuning interval
Will be calculated according to the following formula:
wherein, K i Is a coefficient determined according to the average SOC of the cell when
Hour K Agnet_i 1, otherwise K Agnet_i ＝-1；
And adjusting power for the preset energy storage unit. When coefficient K Agnet_i When all are positive or negative, the fine tuning interval is determined according to the following formula:
wherein the content of the first and second substances,
after sorting according to the SOC of the intelligent agent in the ith energy storage unit area according to the size, obtaining a new SOC reference value by taking an intermediate value
And (5) determining.
Finally, the upper limit and the lower limit of the power fine-tuning interval of the multi-agent particle swarm algorithm are determined according to the following method:
and in the fine-tuning interval of the multi-agent particle swarm algorithm, the agents in each energy storage unit area optimally calculate the charge and discharge power of the energy storage subsystem corresponding to each energy storage unit area by adopting the multi-agent particle swarm algorithm. In the multi-agent particle swarm algorithm, the charging and discharging power reference value of the ith energy storage unit area
Capacity C of energy storage battery pack i Energy storage battery pack
Upper and lower limits of fine-tuning interval
And the SOC and the maximum allowable charging and discharging power limit of each PCS in the control range of the ith energy storage unit area
And substituting the power into a multi-agent particle swarm algorithm to obtain the charging and discharging power of each PCS in the control range of the ith energy storage unit agent at the current moment. Meanwhile, the intelligent agent of the energy storage unit area generates a control instruction of each PCS and sends the control instruction to the control module of each PCS under the agent to control the PCS to complete the power generation or energy storage task. The objective function and constraint conditions of the multi-agent particle swarm algorithm are as follows:
G bess ＝min(ω 1 F 1 +ω 2 F 2 ) (9)
F 1 ＝|P i (t)-P i * (t)| (10)
wherein: i is the number of agents in the energy storage cell region, and i is 1,2,3 … n, j is the number of PCS in the control range of each energy storage cell agent, j is 1,2,3 … m. m represents the number of PCS. P i * Distributing an initial value for the power of the ith energy storage unit intelligent agent at the current moment; p ij The current moment power command value of the jth PCS in the ith energy storage unit intelligent agent is obtained;
the average value of the overall SOC of the ith energy storage unit intelligent agent at a moment is obtained; SOC ij (t-1) is the SOC average value of the jth PCS in the ith energy storage unit intelligent agent at the previous moment; c i The sum of the energy storage capacity in the ith energy storage unit intelligent body; c ij The energy storage capacity of the jth PCS in the ith energy storage unit intelligent body is summed; and lambda is the working state parameter of the jth PCS in the ith energy storage unit intelligent body.
In the above formula, G bess For multi-agent particle swarm algorithm objective function, F 1 Is the difference between the discharge power of each unit area of the energy storage power station and the reference value of the power of the unit area of the energy storage power station at the current moment, F 2 For the average value of the whole SOC of the energy storage power station and the preset SOC reference value (such as SOC) ref 0.5), and ω is a weight coefficient used to measure the bias of the energy storage power station to adjust the SOC or to track the scheduling. Therefore, the SOC of each energy storage unit is close to the same value and is close to the reference value as much as possible after the particle swarm optimization iteration and the simulation operationAnd (4) approaching. Meanwhile, the sum of the output of each energy storage unit and the photovoltaic output is close to a scheduling instruction value sent by a superior energy storage main intelligent body within an allowable range.
The multi-agent particle swarm algorithm comprises the following overall steps of:
1) and reading configuration parameters of the energy storage system, including the number of the particle swarm, parameters of each particle swarm and particles thereof, a power generation plan and the real-time power of new energy. The particle swarm number is the number of transformers in the energy storage system, and the parameters of the particles in the particle swarm are the number of converters, the maximum power, the capacity, the battery pack capacity and the battery pack SOC.
2) And initializing a particle swarm, namely preliminarily setting the power sent out by the next time precision of each PCS in the energy storage unit according to the SOC of the battery pack, the maximum power of the PCS and the energy storage scheduling requirement.
3) And particle swarm competition, proposing a fitness function by combining the characteristics of the energy storage power station, calculating the fitness of each particle swarm, comparing each particle swarm with 8 surrounding particle swarms, and updating the particle swarms if the fitness of the 8 surrounding particle swarms is optimal.
4) And (4) updating the particle swarm, wherein if the fitness of a certain neighbor among 8 neighbors around the particle is better than that of the particle, the particle is updated. The specific update is as follows: a particle value is equal to a particle fitness initial value + (a random number of 0-1) × (an optimal neighbor value-a particle fitness initial value); if all eight neighbors are less adaptive than the particle itself, no update is performed.
5) And (3) self-learning of the particle swarm, setting a proper search range for each particle swarm, optimizing according to a fitness function, comparing the obtained new particle swarm fitness with the original particle swarm fitness, and replacing the current value of the particle swarm if the result is superior to the current fitness of the particle swarm.
6) And iterating until the iteration times are finished. After multiple iterations, the optimal individual in the modern particle swarm can approach the optimal value of the solution space within the corresponding precision.
Another objective of the present invention is to provide a battery energy storage power station partition control system based on multiple intelligent agents, which includes: the energy storage power station comprises an energy storage power station main intelligent body, an energy storage unit area intelligent body and an energy storage unit;
the two modules are explained further below:
the energy storage power station main intelligent bodies are used for calculating charge and discharge power reference values of the intelligent bodies in all the energy storage unit areas according to the superior scheduling information; receiving working state information uploaded by all the intelligent agents in the energy storage unit area, comparing the working state information with the charge and discharge power reference value, adjusting the charge and discharge power reference value according to a comparison result, and issuing the adjusted charge and discharge power reference value to the intelligent agents in the energy storage unit area;
the energy storage unit area intelligent body is used for issuing a control instruction to the energy storage unit according to the adjusted charge-discharge power reference value and the energy storage parameter information of the energy storage unit issued by the energy storage power station main intelligent body;
and the energy storage unit is used for receiving the control command to perform a charging and discharging task.
Energy storage power station owner agent includes: the device comprises a calculation control module and a data storage module;
the calculation control module is used for calculating the scheduling information of the energy storage unit area and judging the energy storage unit area which works correspondingly;
the data storage module is used for storing the power information of the new energy and the power information of the scheduling requirement;
energy storage unit district intelligent agent includes: the system comprises an energy storage monitoring unit, an energy storage data storage management unit and a multi-band particle swarm calculation unit;
the energy storage monitoring unit is used for acquiring energy storage parameter information of the energy storage unit area;
the energy storage data storage management unit is used for receiving the monitoring information of the energy storage monitoring unit;
and the multi-band particle swarm calculation unit is used for optimizing and calculating the control instruction of the energy storage unit.
An energy storage unit comprising: a PCS, and a battery pack.
Specifically, as shown in fig. 1, the energy storage system is divided into an energy storage station main intelligent agent and N energy storage unit area intelligent agents by using a transformer as a unit, each energy storage unit includes PCS and battery packs in different numbers, and as shown in fig. 1, each energy storage unit area intelligent agent belongs to a parallel structure, and the whole system includes: the intelligent energy storage system comprises an energy storage power station main intelligent body, a calculation control unit and a data storage unit inside the intelligent body, an energy storage unit area intelligent body, an energy storage monitoring unit inside the intelligent body, a plurality of PCS and battery packs, an energy storage data storage management unit and a multi-intelligent-body particle swarm calculation control unit.
(1) The energy storage power station main intelligent body is used for receiving power information of new energy (photovoltaic/wind power) and dispatching requirements and storing the power information into a data storage unit of the energy storage power station main intelligent body, calculating total power which should be sent out by an energy storage system through a calculation control unit, judging available PCS in each energy storage unit and performing primary distribution on each energy storage unit and PCS power through reading energy storage system state information provided by energy storage monitoring units in each energy storage power station sub intelligent body.
(2) And the energy storage monitoring unit in the intelligent agent of the energy storage unit area is responsible for acquiring information such as power of the PCS in the energy storage unit, working state parameters of each PCS, SOC (state of charge) of the battery pack and the like in real time and transmitting the information to the data storage unit in the main intelligent agent of the energy storage station. And meanwhile, the system is also responsible for receiving control instructions issued by the particle swarm calculation control units of the multi-agent and controlling the power sent by each PCS to complete a power generation plan. The energy storage monitoring unit is communicated with the energy storage data storage management unit, and the acquired information is stored.
(3) And the multi-agent particle swarm calculation control unit in the intelligent agent in the energy storage unit area is responsible for calculating and finally distributing the power reference value of each energy storage unit and the PCS thereof by using the multi-agent particle swarm algorithm and returning the power reference value to the energy storage monitoring unit. And the multi-agent particle swarm calculation control unit simultaneously sends the calculated power information to the energy storage data storage management unit for storage.
(4) An energy storage data storage management unit in the energy storage unit area intelligent bodies is responsible for receiving monitoring information of the energy storage monitoring units of the energy storage unit area intelligent bodies; and the multi-agent particle swarm calculation control unit calculates the obtained power information. The information is stored and managed according to certain time precision for being called in future detection or other situations.
(5) The PCS and the battery packs in the energy storage cell area intelligent bodies are used for completing the power generation task of the energy storage power station, wherein the PCS in each energy storage cell area intelligent body can be different in quantity and working state, and the current SOC values of the battery packs among the PCS can be different. And each PCS and battery pack are monitored and controlled by the energy storage monitoring unit in real time. The algorithm can be used for carrying out differential treatment according to the difference between the energy storage elements in the energy storage power station, and is independently controlled, so that the difference between the energy storage elements is reduced along with the control effect.
Fig. 2 shows a control flow chart of a large-scale battery energy storage power station based on multi-agent particle swarm, and the control steps are as follows:
(1) and the main intelligent body of the energy storage power station calculates a total power reference value which should be sent by the energy storage power station at the current moment according to the scheduling requirement and the photovoltaic/wind power real-time data. And preliminarily distributing the power generation tasks of the energy storage units according to the quantity of the available PCS by combining the state information of the energy storage units. Here, the PCS utilization rate is 0.9, and may practically take any value between 0.1 and 0.9, and may vary according to practical situations, and the relationship between the PCS utilization rate and the usage efficiency is shown in fig. 3.
(2) And calculating the interval of the particle swarm optimization calculation output power by the intelligent agents in the energy storage unit areas according to the PCS output power reference value preliminarily distributed by the main intelligent agent of the energy storage power station. And calculating the reference value of the power which is finally transmitted by each PCS in each energy storage unit area intelligent agent.
(3) And the energy storage unit area intelligent agents are communicated with the multi-intelligent-agent particle swarm calculation control unit, whether the output power reference value of each PCS exceeds the maximum output power limit or not is judged, if the output power reference value exceeds the maximum output power limit, the PCS can be distributed according to the maximum output power, and meanwhile, the distributed information is sent to the energy storage unit area intelligent agents.
(4) The energy storage unit area intelligent body controls power sent by each PCS in the energy storage unit through the energy storage monitoring unit, and simultaneously sends the power and the battery pack state information to the data storage unit.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The present invention is not limited to the above embodiments, and any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention are included in the scope of the claims of the present invention which are filed as the application.
Claims (7)
1. A battery energy storage power station zone control method based on multi-agent is characterized by comprising the following steps:
the energy storage power station main intelligent agent calculates the charge and discharge power reference values of the intelligent agents in all the energy storage unit areas according to the superior scheduling information;
the main intelligent body of the energy storage power station compares the charging and discharging power reference value with the received working state information uploaded by the intelligent bodies of all the energy storage unit areas, adjusts the charging and discharging power reference value according to the comparison result, and issues the charging and discharging power reference value to the intelligent bodies of the energy storage unit areas;
the energy storage unit area intelligent body sends a control instruction to the energy storage unit according to the adjusted charge-discharge power reference value and the energy storage parameter information of the energy storage unit sent by the energy storage power station main intelligent body;
the energy storage unit carries out charging and discharging according to the control instruction;
the energy storage power station main intelligent body compares the charging and discharging power reference value with the received working state information uploaded by all the energy storage unit area intelligent bodies, adjusts the charging and discharging power reference value according to the comparison result, and issues the charging and discharging power reference value to the energy storage unit area intelligent bodies, and the method comprises the following steps:
the energy storage unit area intelligent agent determines a working state by calculating the maximum chargeable and dischargeable power and the equivalent SOC and uploads the working state to the energy storage station main intelligent agent;
the main intelligent body of the energy storage power station determines an adjusted charging and discharging power reference value by comparing the charging and discharging power reference value with the working state information uploaded by the intelligent body of the energy storage unit area, and issues the adjusted charging and discharging power reference value to the intelligent body of the working energy storage unit area;
the main intelligent agent of energy storage power station confirms the charge-discharge power reference value of adjustment through the operating condition information of comparing charge-discharge power reference value and energy storage unit district intelligent agent upload to issue to the energy storage unit district intelligent agent of work, include:
the energy storage power station main intelligent bodies sequence the energy storage unit areas according to the maximum chargeable and dischargeable power according to the working state information uploaded by the energy storage unit area intelligent bodies;
accumulating the maximum chargeable and dischargeable power of the intelligent agent in each energy storage unit area according to the sequencing result;
comparing the accumulated result with a charge-discharge power reference value every time the maximum chargeable-discharge power of the intelligent body in the energy storage unit area is accumulated;
when the accumulated result is smaller than the charge-discharge power reference value and the number of the energy storage unit areas corresponding to the accumulation is smaller than the number of all the energy storage unit areas, continuing to accumulate;
when the accumulated result is smaller than the charge and discharge power reference value and the accumulated number of the corresponding energy storage unit areas is equal to the number of all the energy storage unit areas, issuing an adjusted charge and discharge power reference value;
when the accumulated result is greater than the charge-discharge power reference value, issuing the charge-discharge power reference value; and determining the intelligent agent to be issued to the working energy storage unit area based on the accumulation result.
2. The multi-agent-based battery energy storage power station partition control method of claim 1, wherein the energy storage power station main agent calculates the charge and discharge power reference values of all the energy storage unit area agents according to the superior scheduling information, comprising:
and the energy storage power station main intelligent body calculates the charging and discharging power reference value of the energy storage unit area intelligent body according to the new energy power information and the power information of the dispatching requirement.
3. The multi-agent-based battery energy storage power station partition control method of claim 1, wherein the energy storage unit area agents issue control instructions to the energy storage units according to the adjusted charge-discharge power reference values and the energy storage parameter information of the energy storage units issued by the energy storage station main agents, and the method comprises the following steps:
and the energy storage unit area intelligent body calculates and sends a control instruction to the energy storage unit through multi-particle group algorithm optimization according to the adjusted charge and discharge power reference value sent by the energy storage power station main intelligent body and the state information of the energy storage unit.
4. A multi-agent based battery energy storage power station zone control system using the multi-agent based battery energy storage power station zone control method of claim 1, comprising: the energy storage power station comprises an energy storage power station main intelligent body, an energy storage unit area intelligent body and an energy storage unit;
the energy storage power station main intelligent body is used for calculating charge and discharge power reference values of all the energy storage unit area intelligent bodies according to the superior scheduling information; receiving working state information uploaded by all the intelligent agents in the energy storage unit area, comparing the working state information with the charge and discharge power reference value, adjusting the charge and discharge power reference value according to a comparison result, and issuing the adjusted charge and discharge power reference value to the intelligent agents in the energy storage unit area;
the energy storage unit area intelligent body is used for issuing a control instruction to the energy storage unit according to the adjusted charge-discharge power reference value and the energy storage parameter information of the energy storage unit issued by the energy storage power station main intelligent body;
and the energy storage unit is used for receiving the control command to perform a charging and discharging task.
5. The multi-agent based battery energy storage power station zone control system of claim 4, wherein the energy storage power station master agent comprises: the device comprises a calculation control module and a data storage module;
the calculation control module is used for calculating the scheduling information of the energy storage unit area and judging the energy storage unit area which works correspondingly;
and the data storage module is used for storing the new energy power information and the power information of the scheduling requirement.
6. The multi-agent based battery energy storage power station partition control system of claim 4, wherein said energy storage cell area agent comprises: the system comprises an energy storage monitoring unit, an energy storage data storage management unit and a multi-band particle swarm calculation unit;
the energy storage monitoring unit is used for acquiring energy storage parameter information of the energy storage unit area;
the energy storage data storage management unit is used for receiving the monitoring information of the energy storage monitoring unit;
and the multi-band particle swarm calculation unit is used for optimizing and calculating the control instruction of the energy storage unit.
7. The multi-agent based battery energy storage power station zone control system of claim 4, wherein said energy storage unit comprises: a PCS, and a battery pack.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810149296.1A CN108376989B (en) | 2018-02-13 | 2018-02-13 | Battery energy storage power station partition control method and system based on multiple intelligent agents |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810149296.1A CN108376989B (en) | 2018-02-13 | 2018-02-13 | Battery energy storage power station partition control method and system based on multiple intelligent agents |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108376989A CN108376989A (en) | 2018-08-07 |
| CN108376989B true CN108376989B (en) | 2022-07-26 |
Family
ID=63018002
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810149296.1A Active CN108376989B (en) | 2018-02-13 | 2018-02-13 | Battery energy storage power station partition control method and system based on multiple intelligent agents |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108376989B (en) |
Families Citing this family (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109361228B (en) * | 2018-09-17 | 2021-12-24 | 中国电力科学研究院有限公司 | Multi-agent control system and method based on distributed battery energy storage |
| CN109617247B (en) * | 2018-12-27 | 2021-06-29 | 广东电网有限责任公司 | Energy storage system control method and equipment |
| CN109904852B (en) * | 2019-04-19 | 2021-03-16 | 华北电力大学（保定） | Active load-based direct current power grid virtual energy storage control method and system |
| CN111555321A (en) * | 2020-04-20 | 2020-08-18 | 上海豫源电力科技有限公司 | Power distribution method and device of PCS (Power conversion System) in energy storage combined thermal power frequency modulation |
| CN112531828B (en) * | 2020-11-26 | 2023-02-10 | 许继集团有限公司 | Electrochemical energy storage power station BMS cooperative control system and method |
| CN112529732A (en) * | 2020-12-04 | 2021-03-19 | 华自科技股份有限公司 | Energy storage unit charging and discharging control method and device, computer equipment and storage medium |
| CN112838605A (en) * | 2020-12-04 | 2021-05-25 | 国网江苏省电力有限公司连云港供电分公司 | Energy storage power station coordination control and energy management and operation method based on multi-agent particle swarm optimization |
| CN112688308A (en) * | 2020-12-11 | 2021-04-20 | 珠海格力电器股份有限公司 | Electric energy scheduling system, method, device and controller |
| CN112928767B (en) * | 2021-03-29 | 2022-06-14 | 广东电网有限责任公司电力科学研究院 | Distributed energy storage cooperative control method |
| CN113253736B (en) * | 2021-06-17 | 2021-11-09 | 上海思岚科技有限公司 | Multi-robot scheduling method and equipment |
| CN113487207B (en) * | 2021-07-16 | 2022-06-28 | 重庆大学 | Multi-target energy management system in multi-energy community based on multi-agent system optimal user cluster |
| CN114123280B (en) * | 2021-11-26 | 2023-06-27 | 国网甘肃省电力公司电力科学研究院 | Battery energy storage power station energy management method considering system efficiency |
| CN115395537B (en) * | 2022-04-22 | 2023-03-24 | 深圳市金典电子科技有限公司 | Safe low-voltage apparatus of energy storage system power supply |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106786697A (en) * | 2016-12-29 | 2017-05-31 | 中国电力科学研究院 | A kind of extensive battery energy storage power station monitoring system and method based on many agencies |
| WO2017154115A1 (en) * | 2016-03-08 | 2017-09-14 | 株式会社東芝 | Storage battery apparatus, storage battery system, method, and control program |
| WO2017169655A1 (en) * | 2016-03-30 | 2017-10-05 | 三洋電機株式会社 | Power supply system, control system and power control method for power supply system |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104967138A (en) * | 2015-07-16 | 2015-10-07 | 河南行知专利服务有限公司 | Energy storage power station |
| KR101980687B1 (en) * | 2015-07-28 | 2019-05-22 | 엘에스산전 주식회사 | Energy Management System |
-
2018
- 2018-02-13 CN CN201810149296.1A patent/CN108376989B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2017154115A1 (en) * | 2016-03-08 | 2017-09-14 | 株式会社東芝 | Storage battery apparatus, storage battery system, method, and control program |
| WO2017169655A1 (en) * | 2016-03-30 | 2017-10-05 | 三洋電機株式会社 | Power supply system, control system and power control method for power supply system |
| CN106786697A (en) * | 2016-12-29 | 2017-05-31 | 中国电力科学研究院 | A kind of extensive battery energy storage power station monitoring system and method based on many agencies |
Non-Patent Citations (1)
| Title |
|---|
| 含分布式电源的配电网保护与优化运行研究;尚瑨;《工程科技Ⅱ辑》;20170228;全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108376989A (en) | 2018-08-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108376989B (en) | Battery energy storage power station partition control method and system based on multiple intelligent agents | |
| CN108376990B (en) | Control method and system of energy storage power station | |
| CN107994595B (en) | Peak clipping and valley filling control method and system applying control method | |
| CN109345019B (en) | Improved particle swarm algorithm-based micro-grid economic dispatching optimization strategy | |
| CN110826880B (en) | Active power distribution network optimal scheduling method for large-scale electric automobile access | |
| CN108551176B (en) | Energy storage battery system capacity configuration method combined with energy storage balancing technology | |
| CN108336766A (en) | A kind of energy-storage system multiple agent cooperative control method and device | |
| CN113054688B (en) | Renewable energy cluster output distributed coordination control method considering uncertainty | |
| CN109378863B (en) | Power grid real-time power generation control optimization decision method based on weight and constraint correlation adjustment | |
| CN112952862B (en) | Hybrid energy storage frequency division coordination controller for stabilizing wind power fluctuation and implementation method | |
| CN110474353B (en) | Layered energy storage system and power grid frequency modulation coordination control method participated in by layered energy storage system | |
| CN114583733A (en) | Echelon utilization energy storage system control method, system, equipment and storage medium | |
| CN110137981A (en) | A kind of distributed energy storage polymerizer AGC method based on consistency algorithm | |
| CN114899856A (en) | Method, system, equipment and medium for adjusting power of electric vehicle charging pile | |
| CN115114854A (en) | Two-stage self-organizing optimization aggregation method and system for distributed resources of virtual power plant | |
| CN113285475A (en) | Multi-energy-storage joint optimization active regulation and control method based on edge cloud cooperative computing | |
| CN116742678A (en) | Comprehensive energy management system of master-slave control architecture and predictive control method | |
| CN115333111A (en) | Multi-region power grid collaborative optimization method, system, equipment and readable storage medium | |
| CN110729759B (en) | Method and device for determining distributed power supply configuration scheme in micro-grid | |
| CN115347556A (en) | Energy storage power station capacity configuration method for promoting new energy consumption | |
| CN113837449A (en) | Virtual power plant participated power grid system centralized optimization scheduling method | |
| CN112598232A (en) | Energy storage system optimal configuration method in VPP (virtual private Point) of improved quantum particle swarm | |
| CN114285093B (en) | Source network charge storage interactive scheduling method and system | |
| CN116388299B (en) | Wind-solar energy storage station group power tracking optimization control method, system and equipment | |
| CN114744632B (en) | Method, device and storage medium for scheduling low-voltage direct-current interconnected power distribution network with quick charge load |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |