CN114336739B - Cloud-edge cooperation-based method and system for configuring energy storage power of optical storage station - Google Patents
Cloud-edge cooperation-based method and system for configuring energy storage power of optical storage station Download PDFInfo
- Publication number
- CN114336739B CN114336739B CN202111532202.7A CN202111532202A CN114336739B CN 114336739 B CN114336739 B CN 114336739B CN 202111532202 A CN202111532202 A CN 202111532202A CN 114336739 B CN114336739 B CN 114336739B
- Authority
- CN
- China
- Prior art keywords
- power
- energy storage
- station
- optical storage
- state
- 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 287
- 238000003860 storage Methods 0.000 title claims abstract description 236
- 230000003287 optical effect Effects 0.000 title claims abstract description 220
- 238000000034 method Methods 0.000 title claims abstract description 62
- 238000009826 distribution Methods 0.000 claims description 13
- 230000006870 function Effects 0.000 claims description 8
- 238000005070 sampling Methods 0.000 claims description 8
- 238000004590 computer program Methods 0.000 claims description 6
- 230000008685 targeting Effects 0.000 claims description 6
- 230000000087 stabilizing effect Effects 0.000 abstract description 6
- 230000008859 change Effects 0.000 abstract description 5
- 238000005457 optimization Methods 0.000 abstract description 2
- 238000005516 engineering process Methods 0.000 description 8
- 238000010248 power generation Methods 0.000 description 8
- 230000009286 beneficial effect Effects 0.000 description 6
- 230000005622 photoelectricity Effects 0.000 description 5
- 230000008569 process Effects 0.000 description 4
- 238000005096 rolling process Methods 0.000 description 4
- 230000009471 action Effects 0.000 description 3
- 230000004044 response Effects 0.000 description 3
- 230000000694 effects Effects 0.000 description 2
- 230000007613 environmental effect Effects 0.000 description 2
- 239000002028 Biomass Substances 0.000 description 1
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 1
- 238000009825 accumulation Methods 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 229910052799 carbon Inorganic materials 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000009977 dual effect Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Landscapes
- Supply And Distribution Of Alternating Current (AREA)
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
Abstract
The invention provides an energy storage power configuration method and system for an optical storage station based on cloud edge cooperation, a storage medium and electronic equipment, and relates to the field of energy storage optimization. The method comprises the steps of collecting state data of an optical storage station; according to the state data of the optical storage power station, a first state prediction model of the optical storage power station is constructed, and a power balance configuration scheme of the energy storage system is obtained at intervals of a first prediction step length; and constructing a second state prediction model of the optical storage station according to the state data of the optical storage station and the power balance configuration scheme of the energy storage system, and acquiring the power balance configuration scheme of each energy storage device at intervals of a second prediction step length, wherein the second prediction step length is smaller than the first prediction step length. And the cloud edge cooperation is utilized to correct the state data of the cloud predicted optical storage station, so that the energy storage power configuration can be adaptively adjusted along with the state and environment change of the optical storage station, the accuracy of the power configuration decision of the energy storage equipment is improved, and a better grid-connected power fluctuation stabilizing effect is achieved.
Description
Technical Field
The invention relates to the technical field of energy storage optimization, in particular to an energy storage power configuration method, an energy storage power configuration system, a storage medium and electronic equipment of an optical storage station based on cloud edge cooperation.
Background
Under the promotion of the 'double carbon' target, the development and utilization scale of renewable energy sources is gradually increased, the equipment level of related technologies is also gradually increased, and the proportion occupied by the renewable energy sources in an energy structure is gradually increased. At present, renewable energy power generation mainly comprises photovoltaic power generation, wind power generation, biomass energy power generation, ocean energy power generation and the like, wherein the photovoltaic power generation and the wind power generation are widely applied as relatively mature technologies. Because photovoltaic and wind power generation have strong randomness, volatility and uncertainty, large-scale grid connection brings challenges to safe and stable operation of a power grid.
In order to improve the utilization rate of photoelectricity and the scheduling flexibility, the photovoltaic power station can reduce photoelectricity fluctuation by adding an energy storage system, improve photoelectricity access level, peak clipping and valley filling and the like, and further access the safe and stable operation level of the power system greatly, thereby providing an effective method for large-scale fluctuation photoelectricity access. The light storage station can cut peaks and fill valleys in the processes of wind discarding and light discarding, and flexibility and reliability of the power system are improved. The power and the capacity of the energy storage device can directly influence the effect of balancing grid-connected power fluctuation of the energy storage device and the economical efficiency of device configuration, and particularly, the larger the power and the capacity of the energy storage, the better the effect of stabilizing the power fluctuation of renewable energy sources, but the higher the corresponding cost. Meanwhile, if the configured energy storage capacity is smaller, the redundant electric quantity can not be stored, so that the power is wasted; if the energy storage capacity is too large, the economic cost is increased, and the energy storage device can be in insufficient charging for a long time, so that the service life of the energy storage device is directly influenced. Therefore, when the energy storage device participates in the optical energy storage station, the reasonable configuration of the output power of the energy storage device is also very important.
At present, in order to stabilize the renewable energy grid-connected power fluctuation, a traditional model predictive control (Model Predictive Control, MPC) method is generally selected, and energy storage equipment regulation and control are performed only aiming at the condition that a single group of energy storage equipment is charged and discharged frequently with minimum energy storage capacity as a target, so that the renewable energy grid-connected power fluctuation is stabilized. However, in the method, unknown factors such as model mismatch, environmental interference and the like exist in practice, and the adopted physical model deviates from an actual value, so that the stability of grid-connected power of the optical storage power station is low.
Disclosure of Invention
(one) solving the technical problems
Aiming at the defects of the prior art, the invention provides a cloud-edge-synergy-based energy storage power configuration method, a cloud-edge-synergy-based energy storage power configuration system, a storage medium and electronic equipment for an optical storage power station, and solves the technical problem of lower stability of grid-connected power of the optical storage power station.
(II) technical scheme
In order to achieve the above purpose, the invention is realized by the following technical scheme:
an optical storage station energy storage power configuration method based on cloud edge cooperation comprises the following steps:
s1, collecting state data of an optical storage station;
s2, constructing a first state prediction model of the optical storage station according to the state data of the optical storage station, and acquiring a power balance configuration scheme of the energy storage system at intervals of a first prediction step length;
s3, constructing a second state prediction model of the optical storage station according to the state data of the optical storage station and the power balance configuration scheme of the energy storage system, and acquiring the power balance configuration scheme of each energy storage device at intervals of a second prediction step length, wherein the second prediction step length is smaller than the first prediction step length.
Preferably, in the step S1, the state data of the optical storage station is collected according to a preset sampling frequency;
the optical storage power station state data at least comprises grid-connected power of the optical storage power station, original output power of the optical storage power station, charge state of an energy storage system, output power of the energy storage system, charge state of each energy storage device and output power of each energy storage device.
Preferably, the S2 specifically includes:
s21, constructing a first state prediction model according to the grid-connected power of the optical storage power station, the original output power of the optical storage power station, the state of charge of the energy storage system and the output power of the energy storage system at the current moment, and acquiring a grid-connected power prediction sequence of the optical storage power station and a state of charge prediction sequence of the energy storage system in the next first control period at intervals of a first prediction step length;
s22, correcting the optical storage station grid-connected power prediction sequence and the state of charge prediction sequence of the energy storage system in the next first prediction step according to the error corresponding to the optical storage station grid-connected power prediction sequence and the state of charge prediction sequence of the energy storage system, which are acquired every first prediction step in the previous first control period;
s23, acquiring a power balance configuration scheme of the energy storage system by adopting a first quadratic programming method according to the corrected grid-connected power prediction sequence of the optical storage station in the next first prediction step length and the charge state prediction sequence of the energy storage system.
Preferably, the first quadratic programming method in S23 includes
A first objective function targeting a minimum energy storage system output:
wherein,Δt represents a first prediction step; t represents a first control period comprising a number of first prediction steps; p (P) es (k+iDeltaT) represents the output power of the energy storage system after the k moment passes through i first prediction step sizes DeltaT;
the output power limit, the charge state limit and the peak-valley difference limit of the grid-connected power of the optical storage station of the energy storage system are used as first constraint conditions:
P es，min ≤P es，ns (k+iΔT)≤P es，max
0≤SOC es (k+iΔT)≤1
-P wn ≤P gs (k+iΔT)-P gs (k+(i-1)ΔT)≤P wn
wherein P is es，min And P es，max Respectively representing the minimum output power and the maximum output power of the energy storage system; SOC (State of Charge) es (k+iDeltaT) represents the state of charge of the energy storage system after the time k passes through i first prediction step sizes DeltaT; p (P) wn And the limit value of the peak-valley difference of the grid-connected power of the optical storage station is represented.
Preferably, the S3 includes:
s31, distributing power to each energy storage device according to the state of charge of each energy storage device at the current moment by using a power balance configuration scheme of the energy storage system;
s32, constructing a second state prediction model according to the grid-connected power of the optical storage station, the original output power of the optical storage station, the charge state of each energy storage device and the output power of each energy storage device at the current moment, and acquiring a grid-connected power prediction sequence of the optical storage station and a charge state prediction sequence of an energy storage system in the next second control period at intervals of a second prediction step length;
and S33, acquiring a power balance configuration scheme of each energy storage device by adopting a second quadratic programming method according to the grid-connected power prediction sequence of the optical storage station and the charge state prediction sequence of the energy storage system, which are acquired every second prediction step length in the next second control period, and combining the power distribution result in the S31.
Preferably, the second quadratic programming method in S33 includes
A second objective function targeting the minimum energy storage system output:
wherein,Δt represents the second prediction step; t represents a second control period comprising a number of second prediction steps, and t=Δt; m represents the number of energy storage devices in the energy storage system; />Representing the output power of the xth energy storage device after the k moment passes through i second prediction step sizes delta t;
the output power limit of each energy storage device, the charge state limit of each energy storage device and the fluctuation rate limit of the grid-connected power of the optical storage power station are used as second constraint conditions:
wherein,representing the power distribution result of the xth energy storage device in the S31; />Representing a maximum output power of the xth energy storage device; />Representing the charge state of the xth energy storage device after the k moment passes through i second prediction step sizes delta t; maxP g (t) represents the maximum grid-connected power of the optical storage station after the k moment passes through i second prediction step sizes delta t, and minP g And (t) represents the minimum grid-connected power of the optical storage station after the k moment passes through i second prediction step sizes delta t, and gamma is a limit value of the grid-connected fluctuation rate of the optical storage station.
Preferably, the first state prediction model in S21 adopts a state space model; the grid-connected power of the optical storage power station and the state of charge of the energy storage system are used as state variables, the output power of the energy storage system is used as a control variable, and the original output power of the optical storage power station is used as a disturbance input quantity.
Preferably, the second state prediction model in S32 adopts a state space model; the grid-connected power of the optical storage station and the charge state of each energy storage device are used as state variables, the output power of each energy storage device is used as a control variable, and the original output power of the optical storage station is used as a disturbance input quantity.
An optical storage station energy storage power configuration system based on cloud edge cooperation, comprising:
the acquisition module is used for acquiring state data of the optical storage station;
the first prediction module is used for constructing a first state prediction model of the optical storage station according to the state data of the optical storage station, and acquiring a power balance configuration scheme of the energy storage system at intervals of a first prediction step length;
and the second prediction module is used for constructing a second state prediction model of the optical storage station according to the state data of the optical storage station and the power balance configuration scheme of the energy storage system, and acquiring the power balance configuration scheme of each energy storage device at intervals of a second prediction step length, wherein the second prediction step length is smaller than the first prediction step length.
A storage medium storing a computer program for cloud-edge synergy-based storage power configuration of an optical storage station, wherein the computer program causes a computer to execute the storage power configuration method of the optical storage station as described above.
An electronic device, comprising:
one or more processors;
a memory; and
one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs comprising instructions for performing the optical storage plant stored energy power configuration method as described above.
(III) beneficial effects
The invention provides an energy storage power configuration method, an energy storage power configuration system, a storage medium and electronic equipment of an optical storage station based on cloud edge cooperation. Compared with the prior art, the method has the following beneficial effects:
the method comprises the steps of collecting state data of an optical storage station; according to the state data of the optical storage power station, a first state prediction model of the optical storage power station is constructed, and a power balance configuration scheme of the energy storage system is obtained at intervals of a first prediction step length; and constructing a second state prediction model of the optical storage station according to the state data of the optical storage station and the power balance configuration scheme of the energy storage system, and acquiring the power balance configuration scheme of each energy storage device at intervals of a second prediction step length, wherein the second prediction step length is smaller than the first prediction step length. And the cloud edge cooperative technology is utilized to correct the state data of the cloud predicted optical storage power station, so that the energy storage power configuration can be adaptively adjusted along with the state and environment change of the optical storage power station, the accuracy of the power configuration decision of the energy storage equipment is improved, and a better grid-connected power fluctuation stabilizing effect can be achieved.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of an energy storage power configuration method of an optical storage station based on cloud edge cooperation according to an embodiment of the present invention;
fig. 2 is a structural block diagram of an energy storage power configuration system of an optical storage station based on cloud edge cooperation according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention are clearly and completely described, and it is obvious that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
According to the cloud-edge-collaboration-based energy storage power configuration method, system, storage medium and electronic equipment for the optical storage station, the technical problem that the stability of grid-connected power of the optical storage station is low is solved.
The technical scheme in the embodiment of the application aims to solve the technical problems, and the overall thought is as follows:
the embodiment of the invention comprises the steps of collecting state data of an optical storage station; according to the state data of the optical storage power station, a first state prediction model of the optical storage power station is constructed, and a power balance configuration scheme of the energy storage system is obtained at intervals of a first prediction step length; and constructing a second state prediction model of the optical storage station according to the state data of the optical storage station and the power balance configuration scheme of the energy storage system, and acquiring the power balance configuration scheme of each energy storage device at intervals of a second prediction step length, wherein the second prediction step length is smaller than the first prediction step length. And the cloud edge cooperative technology is utilized to correct the state data of the cloud predicted optical storage power station, so that the energy storage power configuration can be adaptively adjusted along with the state and environment change of the optical storage power station, the accuracy of the power configuration decision of the energy storage equipment is improved, and a better grid-connected power fluctuation stabilizing effect can be achieved.
In order to better understand the above technical solutions, the following detailed description will refer to the accompanying drawings and specific embodiments.
Examples:
in a first aspect, as shown in fig. 1, an embodiment of the present invention provides a method for configuring energy storage power of an optical storage station based on cloud edge collaboration, including:
s1, collecting state data of an optical storage station;
s2, constructing a first state prediction model of the optical storage station according to the state data of the optical storage station, and acquiring a power balance configuration scheme of the energy storage system at intervals of a first prediction step length;
s3, constructing a second state prediction model of the optical storage station according to the state data of the optical storage station and the power balance configuration scheme of the energy storage system, and acquiring the power balance configuration scheme of each energy storage device at intervals of a second prediction step length, wherein the second prediction step length is smaller than the first prediction step length.
According to the embodiment of the invention, cloud-edge cooperative technology is utilized to correct the state data of the cloud-predicted optical storage station, so that the energy storage power configuration can be adaptively adjusted along with the state and environment change of the optical storage station, the accuracy of the power configuration decision of the energy storage equipment is improved, and a better grid-connected power fluctuation stabilizing effect can be achieved.
The following will describe each step of the above technical solution in detail in combination with specific details:
firstly, it should be noted that the method for configuring the energy storage power of the optical storage station provided by the embodiment of the invention is based on a cloud-edge collaborative framework, and particularly relates to a cloud end and an edge end, and the subsequent detailed description of the method is to be expanded and described in combination with the cloud end and the edge end.
S1, collecting state data of the optical storage station.
And collecting the state data of the optical storage station at the edge according to the sampling frequency set by the system.
The optical storage power station state data at least comprises grid-connected power of the optical storage power station, original output power of the optical storage power station, charge state of an energy storage system, output power of the energy storage system, charge state of each energy storage device and output power of each energy storage device.
S2, constructing a first state prediction model of the optical storage station according to the state data of the optical storage station, and acquiring a power balance configuration scheme of the energy storage system at intervals of a first prediction step length.
Configuring the power of the energy storage system at the cloud; and utilizing the grid-connected power of the optical storage power station and the output power of the energy storage system collected by the edge end as initial input states of the first prediction model, carrying out peak-valley difference balance decision of the grid-connected power of the optical storage power station by applying cloud energy storage system balance power decision, determining a power balance configuration scheme of the energy storage system in the current step length, and further reducing the peak-valley difference of the grid-connected power of the optical storage power station.
In the step, the balance power of the energy storage system is configured to enable the energy storage to play a role in reducing peak-valley difference of grid-connected power when participating in grid connection of the optical storage station; the method specifically comprises the following steps:
s21, constructing a first state prediction model according to the grid-connected power of the optical storage power station, the original output power of the optical storage power station, the state of charge of the energy storage system and the output power of the energy storage system at the current moment, and acquiring a grid-connected power prediction sequence of the optical storage power station and a state of charge prediction sequence of the energy storage system in the next first control period at intervals of a first prediction step length.
Setting a cloud control period as T (first control period), and setting a prediction step length as delta T (first prediction step length); assuming that the current moment is k moment, the cloud receives the grid-connected power P of the optical storage station at k moment g (k) Original output power P of optical storage station r (k) Output power P of energy storage system es (k) And state of charge SOC of the energy storage system es (k)。
The first state prediction model in the S21 adopts a state space model; wherein the grid-connected power P of the optical storage station g (k) And state of charge SOC of the energy storage system es (k) As a state variable, the energy storage system output power P es (k) As a control variable, the original output power P of the optical storage station r (k) As disturbance input quantity, P g (k+DeltaT) and SOC es (k+Δt) as an output variable; the state space model is:
and iterating the state space model by adopting a rolling time domain strategy, and predicting a prediction sequence of grid-connected power of the optical storage station and the state of charge of the energy storage system in every delta T time from the k+delta T moment to the k+T moment.
S22, correcting the optical storage station grid-connected power prediction sequence and the energy storage system charge state prediction sequence in the next first prediction step according to the error corresponding to the optical storage station grid-connected power prediction sequence and the energy storage system charge state obtained in the previous first prediction step.
Error between the actual state at time k of the optical storage station and the predicted state at time k-1:
e(k)＝X(k)-X(k|k-1)
wherein X (k) is actual state data of the optical storage station at the time k, and X (k|k-1) is state data of the optical storage station at the time k-1. Obviously, X may specifically represent the grid-connected power of the optical storage station or the state of charge of the energy storage system.
Based on the prediction error e (k), a feedback correction formula is used:
Y′(k)＝Y(k)+e(k)
and correcting the predicted state quantity, wherein Y (k) is a state sequence predicted at the moment k, Y' (k) is an output corrected predicted state sequence, and obviously, Y can specifically represent grid-connected power of the optical storage station or the charge state of the energy storage system.
S23, acquiring a power balance configuration scheme of the energy storage system by adopting a first quadratic programming method according to the corrected grid-connected power prediction sequence of the optical storage station in the next first prediction step length and the charge state prediction sequence of the energy storage system.
The first quadratic programming method comprises the following steps of
A first objective function targeting a minimum energy storage system output:
wherein,Δt represents a first prediction step; t represents a first control period comprising a plurality of first prediction step sizes, namely a cloud control period; p (P) es (k+iDeltaT) represents the output power of the energy storage system after the k moment passes through i first prediction step sizes DeltaT;
the output power limit, the charge state limit and the peak-valley difference limit of the grid-connected power of the optical storage station of the energy storage system are used as first constraint conditions:
P es ， min ≤P es，ns (k+iΔT)≤P es，max
0≤SOC es (k+iΔT)≤1
-P wn ≤P gs (k+iΔT)-P gs (k+(i-1)ΔT)≤P wn
wherein P is es，min And P es，max Respectively representing the minimum output power and the maximum output power of the energy storage system; SOC (State of Charge) es (k+iDeltaT) represents the state of charge (after correction) of the energy storage system after the i first prediction steps DeltaT have elapsed at time k; p (P) wn And the limit value of the peak-valley difference of the grid-connected power of the optical storage station is represented.
S3, constructing a second state prediction model of the optical storage station according to the state data of the optical storage station and the power balance configuration scheme of the energy storage system, and acquiring the power balance configuration scheme of each energy storage device at intervals of a second prediction step length, wherein the second prediction step length is smaller than the first prediction step length.
At the edge end, the output power of each energy storage device takes the balance power distributed to the device as the constraint condition of edge energy storage configuration, the power configuration decision of the edge energy storage device is implemented in a control time domain smaller than a cloud algorithm, and the purpose of reducing the grid-connected power fluctuation rate of the optical storage station is achieved by carrying out power configuration on the energy storage devices with different charge states; the method specifically comprises the following steps:
s31, distributing power to each energy storage device according to the state of charge of each energy storage device at the current moment by using the power balance configuration scheme of the energy storage system.
According to the power configuration decision output by the cloud, namely the energy storage system balances the power P in the future delta T period es And (i.e. a power balance configuration scheme of the energy storage system), carrying out power distribution on the balance power according to the states of charge of different devices, and taking a distribution result as a lower bound of the output power of each energy storage device in an edge energy storage device power configuration decision.
The specific power distribution mode is as follows:
wherein M is the number of the energy storage devices,the state of charge of the x-th energy storage device at time k.
S32, constructing a second state prediction model according to the grid-connected power of the optical storage station, the original output power of the optical storage station, the charge state of each energy storage device and the output power of each energy storage device at the current moment, and acquiring a grid-connected power prediction sequence of the optical storage station and a charge state prediction sequence of the energy storage system in the next second control period at intervals of second prediction step length.
Setting an edge control period as t=deltat (second control period), and setting a prediction step length as deltat (second prediction step length); the edge end receives grid-connected power P of the optical storage station at k moment g (k) Original output power P of optical storage station r (k) Output power of each energy storage deviceAnd the state of charge of the energy storage system of each energy storage device +.>The power configuration method is used for carrying out power configuration on the energy storage equipment with different charge states in the cloud control time domain delta T.
The second state prediction model in the step S32 also adopts a state space model; wherein the grid-connected power P of the optical storage station g (k) And the state of charge of each energy storage deviceAs a state variable, the output of each energy storage device is +>As a control variable, the original output power P of the optical storage station r (k) As disturbance input quantity, P g (k+Δt) and->As an output variable; the state space model is:
and iterating the state space model by adopting a rolling time domain strategy, and predicting a prediction sequence of grid-connected power of the optical storage station and the charge state of each energy storage device in each delta t time from the k+delta t time to the k+t time.
And S33, acquiring a power balance configuration scheme of each energy storage device by adopting a second quadratic programming method according to the grid-connected power prediction sequence of the optical storage station and the charge state prediction sequence of the energy storage system, which are acquired every second prediction step length in the next second control period, and combining the power distribution result in the S31.
The second quadratic programming method in the S33 comprises the following steps of
A second objective function targeting the minimum energy storage system output:
wherein,Δt represents the second prediction step; t represents a second control period including a number of second prediction steps, i.e., an edge-side control period, and t=Δt; m represents the number of energy storage devices in the energy storage system; />Representing the output power of the xth energy storage device after the k moment passes through i second prediction step sizes delta t;
the output power limit of each energy storage device, the charge state limit of each energy storage device and the fluctuation rate limit of the grid-connected power of the optical storage power station are used as second constraint conditions:
wherein,representing the power distribution result of the xth energy storage device in the S31; />Representing a maximum output power of the xth energy storage device; />Representing the charge state of the xth energy storage device after the k moment passes through i second prediction step sizes delta t; maxP g (t) represents the maximum grid-connected power of the optical storage station after the k moment passes through i second prediction step sizes delta t, and minP g And (t) represents the minimum grid-connected power of the optical storage station after the k moment passes through i second prediction step sizes delta t, and gamma is a limit value of the grid-connected fluctuation rate of the optical storage station.
It is easy to understand that when the edge control time does not reach the 2 nd prediction step length of the cloud energy storage system balance power decision, the edge energy storage device power configuration decision is ended; and when the time enters the next control time domain of the balance power decision of the cloud energy storage system, the cloud receives the grid-connected state data of the optical storage station, inputs the grid-connected state data as a prediction initial state into a balance power decision prediction model of the cloud energy storage system, and repeats the steps S2 to S3.
According to the embodiment of the invention, the advantages of cloud computing and edge computing are simultaneously exerted, a cloud energy storage system balance power decision method with a long time scale is arranged on the cloud for carrying out, an edge energy storage device power configuration decision with a short time scale is arranged on the edge for carrying out, the timeliness of the system is improved, and data expansion caused by frequent sampling of a large amount of data by the edge in a short time is avoided.
And completing power configuration on the dual time scales of the power of the energy storage device at the cloud end and the edge end. Particularly, the edge terminal carries out decision response, takes an energy storage system configuration instruction output by the cloud energy storage system for balancing the power decision as a constraint condition of the edge energy storage device power configuration decision, configures the power of the energy storage device according to the charge characteristics of different devices, so that the peak-valley difference of the grid-connected power can be reduced on a long time scale after the energy storage device participates in the optical storage station, and the fluctuation of the grid-connected power is stabilized on a short time scale.
The following examples will describe the embodiments of the present invention in further detail.
Assuming that for the optical storage power stations with the energy storage devices with different charge states, the grid-connected power fluctuation rate of the optical storage power stations is required to be not more than 2% within 1min, the prediction step length of the cloud energy storage system balance power decision is set to be 1min, the control period is 60min, the prediction step length of the edge energy storage device power configuration decision is 5s, the control period is 1min, namely the edge sampling frequency is 12 times/min, and the system state variable is updated to the cloud every 1min for feedback correction.
The method is carried out according to the following scheme:
1. first, the edge end of the system collects the original power P of the optical storage station at k moment r (k) Grid-connected power P g (k) State of charge of each energy storage deviceAnd the output of the energy storage devices +.>And calculate the state of charge SOC of the energy storage system es (k) And the output power P of the energy storage system es (k)。
2. Receiving original power P of optical storage station at k moment with 1min as interval r (k) Grid-connected power P of optical storage station g (k) And state of charge SOC of the energy storage system es (k) And the state prediction of the optical storage station is carried out with a prediction step length of 1min and a control period of 60 min. In particular, it can be based on the current grid-connected power P of the optical storage station g (k) And an energy storage systemState of charge SOC es (k) As a state variable, the output power P of the energy storage system es (k) Renewable energy source original power P as control variable r (k) As a disturbance input; p (P) g (k+1 min) and SOC es (k+1min) as an output variable, a model may be constructed, for example, using a state space expression:
and iterating the above formula by using a rolling time domain strategy to predict the predicted data from the moment k+1min to the moment k+60min at intervals of 1 min.
3. And calculating an error between the actual state at the moment k of the optical storage station and the predicted state at the moment k-1min to obtain corrected predicted data Y' (k).
4. Applying the modified prediction data Y' (k); and optimizing the output power of the energy storage system by adopting a secondary planning method. Under the limitation of grid-connected power peak-valley difference of an optical storage station, the minimum output of an energy storage system is taken as a target, namely, a first objective function is as follows:
the corresponding constraint conditions are:
P es，min ≤P es，ns (k+i*1min))≤P es，max
0≤SOC es (k+i*1min))≤1
-P wn ≤P gs (k+i*1min))-P gs (k+(i-1)*1min))≤P wn
5. after the cloud end completes the power decision of the energy storage system, outputting a power balance strategy of the energy storage system within 1min in the future to the edge end, namely energy storage balance power P es 。
6. And after receiving the balance power decision of the energy storage system, distributing the minimum output power of the energy storage equipment according to the charge states of the energy storage equipment.
And according to the power distribution result of the edge end and the state data of the current optical storage station, taking 1min as a control period and 5s as a prediction step length, carrying out power configuration decision of the edge energy storage equipment, namely carrying out decision configuration on the energy storage equipment in a cloud decision time interval according to the power distribution result so as to complete power configuration of the energy storage equipment with different charge states. With the current photo-electricity storage station grid-connected power P g (k) And the state of charge of each energy storage deviceFor the purpose of being a state variable, the balance power of each energy storage device is +>Building a state space expression for the control variable:
the rolling time domain strategy is also utilized to iterate the above method so as to output grid-connected power P of the optical storage station every 5s from the moment k+5s to the moment k+1min g (k) And the state of charge of each energy storage deviceAnd predicting data.
7. Optimizing by using a quadratic programming method, taking limitation of grid-connected power fluctuation rate of the optical storage station into consideration, and carrying out energy storage power configuration by taking the minimum usage amount of energy storage equipment as a target, wherein a second objective function is as follows:
/>
the corresponding constraint conditions are:
8. the edge end receives data such as grid-connected power of the optical storage power station, original output data of the optical storage power station, charge state of the energy storage equipment, output power of the energy storage equipment and the like acquired by the edge end every 5s, and a state variable at the current moment is input into a prediction model of the edge end, so that the state prediction of a new round is started by taking the data as an initial state, and the optimal configuration is completed.
9. When the edge control time reaches a new prediction step length of the cloud energy storage system balance power decision, ending the edge energy storage device power configuration decision, re-receiving the energy storage balance power decision output by the cloud, and performing power distribution as a constraint condition of the edge energy storage device power configuration decision, so as to repeatedly perform optimal configuration on the power of the energy storage device, thereby achieving the purpose of reducing the grid-connected power peak-valley difference and the fluctuation rate of the optical storage station.
In a second aspect, as shown in fig. 2, an embodiment of the present invention provides a method for configuring energy storage power of an optical storage station based on cloud edge collaboration, including:
the acquisition module is used for acquiring state data of the optical storage station;
the first prediction module is used for constructing a first state prediction model of the optical storage station according to the state data of the optical storage station, and acquiring a power balance configuration scheme of the energy storage system at intervals of a first prediction step length;
and the second prediction module is used for constructing a second state prediction model of the optical storage station according to the state data of the optical storage station and the power balance configuration scheme of the energy storage system, and acquiring the power balance configuration scheme of each energy storage device at intervals of a second prediction step length, wherein the second prediction step length is smaller than the first prediction step length.
In a third aspect, an embodiment of the present invention provides a storage medium storing a computer program for cloud-edge collaboration-based storage power configuration of an optical storage station, where the computer program causes a computer to execute the storage power configuration method of the optical storage station as described above.
In a fourth aspect, an embodiment of the present invention provides an electronic device, including:
one or more processors;
a memory; and
one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs comprising instructions for performing the optical storage plant stored energy power configuration method as described above.
It may be understood that the cloud-edge-collaboration-based energy storage power configuration system, the storage medium and the electronic device for the optical storage station provided by the embodiment of the present invention correspond to the cloud-edge-collaboration-based energy storage power configuration method for the optical storage station provided by the embodiment of the present invention, and the explanation, the examples, the beneficial effects and other parts of the relevant content may refer to corresponding parts in the energy storage power configuration method for the optical storage station, which are not described herein again.
In summary, compared with the prior art, the method has the following beneficial effects:
1. according to the embodiment of the invention, cloud-edge cooperative technology is utilized to correct cloud-predicted state data of the optical storage station, so that energy storage power configuration can be adaptively adjusted along with the state and environment change of the optical storage station, the accuracy of power configuration decision of the energy storage equipment is improved, and a better grid-connected power fluctuation stabilizing effect can be achieved
2. Because the cloud control time domain is longer, the prediction data is easy to cause errors due to environmental interference, and the errors are larger due to accumulation along with time, the embodiment of the invention utilizes the advantages of cloud edge cooperation technology, focuses on reducing peak-valley difference of the grid-connected power of the optical storage power station on a long time scale at the cloud end, focuses on reducing the fluctuation rate of the grid-connected power of the optical storage power station on a short time scale at the edge, exerts the advantages of quick response and no need of changing the state of a generator set when the energy storage equipment participates in grid-connection of the optical storage power station, and is beneficial to improving the integral stability of the grid-connected power of the optical storage power station.
3. According to the energy storage power configuration method provided by the embodiment of the invention, a cloud edge cooperative technology is utilized, a short time scale algorithm requiring high-frequency sampling is arranged at the edge, so that data congestion and transmission pressure caused by inputting a large amount of data into a cloud when the sampling frequency is too high are avoided, and the response efficiency of the energy storage device to a decision scheme is improved.
4. The embodiment of the invention realizes the joint configuration of the hybrid energy storage system with various charge characteristics in the photovoltaic power station, is more beneficial to prolonging the service life of energy storage equipment and reducing the energy storage cost.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (7)
1. The cloud edge cooperation-based energy storage power configuration method for the optical storage station is characterized by comprising the following steps of:
s1, collecting state data of an optical storage station;
s2, constructing a first state prediction model of the optical storage station according to the state data of the optical storage station, and acquiring a power balance configuration scheme of the energy storage system at intervals of a first prediction step length;
s3, constructing a second state prediction model of the optical storage station according to the state data of the optical storage station and the power balance configuration scheme of the energy storage system, and acquiring the power balance configuration scheme of each energy storage device at intervals of a second prediction step length, wherein the second prediction step length is smaller than the first prediction step length;
in the step S1, the state data of the optical storage station are collected according to a preset sampling frequency;
the optical storage power station state data at least comprises grid-connected power of the optical storage power station, original output power of the optical storage power station, charge state of an energy storage system, output power of the energy storage system, charge state of each energy storage device and output power of each energy storage device;
the step S2 specifically comprises the following steps:
s21, constructing a first state prediction model according to the grid-connected power of the optical storage power station, the original output power of the optical storage power station, the state of charge of the energy storage system and the output power of the energy storage system at the current moment, and acquiring a grid-connected power prediction sequence of the optical storage power station and a state of charge prediction sequence of the energy storage system in the next first control period at intervals of a first prediction step length;
s22, correcting the optical storage station grid-connected power prediction sequence in the next first prediction step length and the state of charge prediction sequence of the energy storage system according to the optical storage station grid-connected power at the current moment and the state of charge of the energy storage system, and the errors corresponding to the optical storage station grid-connected power prediction sequence and the state of charge prediction sequence of the energy storage system acquired every first prediction step length in the previous first control period;
s23, acquiring a power balance configuration scheme of the energy storage system by adopting a first quadratic programming method according to the corrected grid-connected power prediction sequence of the optical storage station in the next first prediction step length and the charge state prediction sequence of the energy storage system;
the step S3 comprises the following steps:
s31, distributing power to each energy storage device according to the state of charge of each energy storage device at the current moment by using a power balance configuration scheme of the energy storage system;
s32, constructing a second state prediction model according to the grid-connected power of the optical storage station, the original output power of the optical storage station, the charge state of each energy storage device and the output power of each energy storage device at the current moment, and acquiring a grid-connected power prediction sequence of the optical storage station and a charge state prediction sequence of an energy storage system in the next second control period at intervals of a second prediction step length;
and S33, acquiring a power balance configuration scheme of each energy storage device by adopting a second quadratic programming method according to the grid-connected power prediction sequence of the optical storage station and the charge state prediction sequence of the energy storage system, which are acquired every second prediction step length in the next second control period, and combining the power distribution result in the S31.
2. The method for configuring stored energy power of an optical storage station as defined in claim 1, wherein said first quadratic programming method in S23 comprises
A first objective function targeting a minimum energy storage system output:
wherein,Δt represents a first prediction step; t represents a first control period comprising a number of first prediction steps; p (P) es (k+iDeltaT) represents the output power of the energy storage system after the k moment passes through i first prediction step sizes DeltaT;
the output power limit, the charge state limit and the peak-valley difference limit of the grid-connected power of the optical storage station of the energy storage system are used as first constraint conditions:
P es,min ≤P es (k+iΔT)≤P es,max
0≤SOC es (k+iΔT)≤1
-P wn ≤P g (k+iΔT)-P g (k+(i-1)ΔT)≤P wn
wherein P is es,min And P es,max Respectively representing the minimum output power and the maximum output power of the energy storage system; SOC (State of Charge) es (k+iDeltaT) represents the state of charge of the energy storage system after the time k passes through i first prediction step sizes DeltaT; p (P) wn And the limit value of the peak-valley difference of the grid-connected power of the optical storage station is represented.
3. The method for configuring stored energy power of an optical storage plant according to claim 1,
the second quadratic programming method in the S33 comprises the following steps of
A second objective function targeting the minimum energy storage system output:
wherein,Δt represents the second prediction step; t represents a second control period comprising a number of second prediction steps, and t=Δt; m represents the number of energy storage devices in the energy storage system; />Representing the output power of the xth energy storage device after the k moment passes through i second prediction step sizes delta t;
the output power limit of each energy storage device, the charge state limit of each energy storage device and the fluctuation rate limit of the grid-connected power of the optical storage power station are used as second constraint conditions:
wherein,representing the power distribution result of the xth energy storage device in the S31; />Representing a maximum output power of the xth energy storage device; />Representing the charge state of the xth energy storage device after the k moment passes through i second prediction step sizes delta t; max P g (k+i delta t) represents the maximum grid-connected power of the optical storage station after the k moment passes through i second prediction step sizes delta t, and minP g And (k+iDeltat) represents the minimum grid-connected power of the optical storage station after the k moment passes through i second prediction step sizes Deltat, and gamma is the limit value of the grid-connected fluctuation rate of the optical storage station.
4. The method for configuring stored energy power of an optical storage plant according to claim 1,
the first state prediction model in the S21 adopts a state space model; the grid-connected power of the optical storage power station and the state of charge of the energy storage system are used as state variables, the output power of the energy storage system is used as a control variable, and the original output power of the optical storage power station is used as a disturbance input quantity;
and/or the second state prediction model in S32 adopts a state space model; the grid-connected power of the optical storage station and the charge state of each energy storage device are used as state variables, the output power of each energy storage device is used as a control variable, and the original output power of the optical storage station is used as a disturbance input quantity.
5. An optical storage station energy storage power configuration system based on cloud edge cooperation, which is characterized by comprising:
the acquisition module is used for acquiring state data of the optical storage station;
the first prediction module is used for constructing a first state prediction model of the optical storage station according to the state data of the optical storage station, and acquiring a power balance configuration scheme of the energy storage system at intervals of a first prediction step length;
the second prediction module is used for constructing a second state prediction model of the optical storage station according to the state data of the optical storage station and the power balance configuration scheme of the energy storage system, and acquiring the power balance configuration scheme of each energy storage device at intervals of a second prediction step length, wherein the second prediction step length is smaller than the first prediction step length;
the acquisition module acquires the state data of the optical storage station according to a preset sampling frequency;
the optical storage power station state data at least comprises grid-connected power of the optical storage power station, original output power of the optical storage power station, charge state of an energy storage system, output power of the energy storage system, charge state of each energy storage device and output power of each energy storage device;
the first prediction module is specifically configured to:
s21, constructing a first state prediction model according to the grid-connected power of the optical storage power station, the original output power of the optical storage power station, the state of charge of the energy storage system and the output power of the energy storage system at the current moment, and acquiring a grid-connected power prediction sequence of the optical storage power station and a state of charge prediction sequence of the energy storage system in the next first control period at intervals of a first prediction step length;
s22, correcting the optical storage station grid-connected power prediction sequence in the next first prediction step length and the state of charge prediction sequence of the energy storage system according to the optical storage station grid-connected power at the current moment and the state of charge of the energy storage system, and the errors corresponding to the optical storage station grid-connected power prediction sequence and the state of charge prediction sequence of the energy storage system acquired every first prediction step length in the previous first control period;
s23, acquiring a power balance configuration scheme of the energy storage system by adopting a first quadratic programming method according to the corrected grid-connected power prediction sequence of the optical storage station in the next first prediction step length and the charge state prediction sequence of the energy storage system;
the second prediction module is specifically configured to:
s31, distributing power to each energy storage device according to the state of charge of each energy storage device at the current moment by using a power balance configuration scheme of the energy storage system;
s32, constructing a second state prediction model according to the grid-connected power of the optical storage station, the original output power of the optical storage station, the charge state of each energy storage device and the output power of each energy storage device at the current moment, and acquiring a grid-connected power prediction sequence of the optical storage station and a charge state prediction sequence of an energy storage system in the next second control period at intervals of a second prediction step length;
and S33, acquiring a power balance configuration scheme of each energy storage device by adopting a second quadratic programming method according to the grid-connected power prediction sequence of the optical storage station and the charge state prediction sequence of the energy storage system, which are acquired every second prediction step length in the next second control period, and combining the power distribution result in the S31.
6. A storage medium, characterized in that it stores a computer program for cloud-edge synergy-based storage power configuration of an optical storage station, wherein the computer program causes a computer to execute the storage power configuration method of an optical storage station according to any one of claims 1 to 4.
7. An electronic device, comprising:
one or more processors;
a memory; and
one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs comprising instructions for performing the method of storing energy power in an optical storage plant as claimed in any one of claims 1-4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111532202.7A CN114336739B (en) | 2021-12-15 | 2021-12-15 | Cloud-edge cooperation-based method and system for configuring energy storage power of optical storage station |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111532202.7A CN114336739B (en) | 2021-12-15 | 2021-12-15 | Cloud-edge cooperation-based method and system for configuring energy storage power of optical storage station |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114336739A CN114336739A (en) | 2022-04-12 |
| CN114336739B true CN114336739B (en) | 2023-12-29 |
Family
ID=81051978
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111532202.7A Active CN114336739B (en) | 2021-12-15 | 2021-12-15 | Cloud-edge cooperation-based method and system for configuring energy storage power of optical storage station |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114336739B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114744653B (en) * | 2022-04-24 | 2023-07-18 | 浙大宁波理工学院 | Operation optimization method, system and storable medium of composite energy storage power station |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2013143327A1 (en) * | 2012-03-30 | 2013-10-03 | 中国电力科学研究院 | Method for real-time power distribution of battery energy storage power station used for tracking and planning output power |
| CN105680474A (en) * | 2016-02-22 | 2016-06-15 | 中国电力科学研究院 | Control method for restraining rapid power change of photovoltaic station based on energy storage system |
| CN110165707A (en) * | 2019-02-26 | 2019-08-23 | 国网吉林省电力有限公司 | Light-preserved system optimal control method based on Kalman filtering and Model Predictive Control |
| CN113792911A (en) * | 2021-08-17 | 2021-12-14 | 清华大学 | Double-layer collaborative optimization configuration method and system for energy storage capacity of optical storage system |
-
2021
- 2021-12-15 CN CN202111532202.7A patent/CN114336739B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2013143327A1 (en) * | 2012-03-30 | 2013-10-03 | 中国电力科学研究院 | Method for real-time power distribution of battery energy storage power station used for tracking and planning output power |
| CN105680474A (en) * | 2016-02-22 | 2016-06-15 | 中国电力科学研究院 | Control method for restraining rapid power change of photovoltaic station based on energy storage system |
| CN110165707A (en) * | 2019-02-26 | 2019-08-23 | 国网吉林省电力有限公司 | Light-preserved system optimal control method based on Kalman filtering and Model Predictive Control |
| CN113792911A (en) * | 2021-08-17 | 2021-12-14 | 清华大学 | Double-layer collaborative optimization configuration method and system for energy storage capacity of optical storage system |
Non-Patent Citations (2)
| Title |
|---|
| 张增辉 等.光储系统功率波动平抑策略研究.《电测与仪表》.2020,第1-8页. * |
| 桑丙玉 等.平滑新能源输出波动的储能优化配置方法.《中国电机工程学报》.2014,第34卷(第22期),第3700-3706页. * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114336739A (en) | 2022-04-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11326579B2 (en) | Adaptive dynamic planning control method and system for energy storage station, and storage medium | |
| Shu et al. | Optimal operation strategy of energy storage system for grid-connected wind power plants | |
| CN105375479A (en) | Model predicative control based energy management method of distributed energy resource system | |
| CN110854932A (en) | Multi-time scale optimization scheduling method and system for AC/DC power distribution network | |
| CN112966883B (en) | Comprehensive energy system operation optimization method and terminal equipment | |
| CN112529257B (en) | Energy storage power station joint optimization method and system considering AGC signal influence | |
| CN109787221A (en) | A kind of micro-capacitance sensor electric energy safe economic load dispatching method and system | |
| CN114336739B (en) | Cloud-edge cooperation-based method and system for configuring energy storage power of optical storage station | |
| CN113078643A (en) | Energy storage capacity configuration method for wind power plan optimization and deviation compensation considering power grid economic operation | |
| CN112016186A (en) | Annual wind curtailment and light curtailment rate calculation method based on random scene construction | |
| CN114142470B (en) | Method and system for measuring and calculating reasonable scale of energy storage matched with new energy power station of electric power system | |
| CN116131356A (en) | Wind-solar storage grid-connected micro-grid optimal scheduling method | |
| CN110717694B (en) | Energy storage configuration random decision method and device based on new energy consumption expected value | |
| CN113489066A (en) | Power supply reliability assessment method for energy storage-containing power grid interval considering supply and demand uncertainty | |
| CN108418213B (en) | Source-load interaction-based power system scheduling method | |
| CN113496298A (en) | Optimization method and device of comprehensive energy system and electronic equipment | |
| CN110598925A (en) | Energy storage in-trading market decision optimization method based on double-Q learning algorithm | |
| CN111030088B (en) | Method and device for predicting capacity of power transmission channel for power transmission | |
| CN117728468B (en) | Micro-grid energy storage system real-time optimal control method based on supervised learning | |
| CN116822908B (en) | Multi-time-scale energy storage planning method and equipment capable of being rapidly solved | |
| Zhuo | Control of wind power smoothing with battery energy storage system and thermostatically controlled loads | |
| CN114336589A (en) | Energy storage optimization method and system device based on wind power plant wind curtailment and electricity limitation | |
| CN113937769A (en) | Unit combination determination method and device considering wind energy output uncertainty | |
| Liu et al. | A closed‐loop representative day selection framework for generation and transmission expansion planning with demand response | |
| Ranjan et al. | QOAOA based optimal controller for frequency regulation in smart grid system incorporating renewable sources and hybrid energy storage system |
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 |