This application claims priority to Chinese Patent Application No. 202210964574.5, filed on Aug. 12, 2022, the contents of which are hereby incorporated by reference.
The present application relates to the technical field of energy systems, specifically to a method and device for low-carbon integrated energy system scheduling.
In the context of global low-carbon development, the virtual power plant may coordinate various types of energy, such as electricity, gas, heat, and hydrogen, thus providing an effective solution to enhance energy efficiency and reduce carbon emissions. However, the randomness and uncertainty of renewable energy output pose great challenges to the low-carbon virtual power plant scheduling. At present, the traditional methods utilize stochastic optimization to achieve low-carbon virtual power plant scheduling. However, the above methods often have the following technical challenges:
The above information disclosed in the background technology section is only used to enhance the understanding of the background of the present application, and therefore, it may include information that does not constitute prior art known to ordinary technical personnel in this field in the country.
The content of the present application is intended to briefly introduce the concepts, which will be described in detail in the specific embodiments section later. The content of the present application is not intended to identify key or necessary features of the technical solution required for protection, nor is it intended to limit the scope of the technical solution required for protection.
The embodiments of the present application propose a method and device for low-carbon virtual power plant scheduling to solve one or more of the technical problems mentioned in the background technical section above.
First, the embodiments of the present application provide a method for low-carbon virtual power plant scheduling, comprising: obtaining the energy device information set for each energy device in the virtual power plant, where each piece of the energy device information in the above energy device information set comprises an energy device name, energy device parameter information and an energy device number; generating the energy acquisition cost information for each energy device based on the energy device name, energy device parameter information and the energy device number in the energy device information set; generating the energy scheduling objective values and the objective energy device parameter information of each energy device based on the preset constraint sets and energy acquisition cost information of each energy device; controlling each energy device in the virtual power plant to execute the energy scheduling tasks based on the objective energy device parameter information.
Second, the embodiments of the present application provide a device for low-carbon virtual power plant scheduling, comprising: acquisition unit, configured to obtain the energy device information set for each energy device in the virtual power plant, wherein the energy device information in the above energy device information set comprises the energy device names, energy device parameter information and energy device number; first generation unit, configured to generate the energy acquisition cost information for each energy device based on the energy device names, energy device parameter information and energy device number in the energy device information set; second generation unit, configured to generate the energy scheduling objective values and the objective energy device parameter information of each energy device based on the preset constraint sets and energy acquisition cost information of each energy device; control unit, configured to control each energy device in the virtual power plant to execute the energy scheduling tasks based on the objective energy device parameter information.
The above embodiments of the present application have the following beneficial effects: first, by using the low-carbon virtual power plant scheduling method in this application, the comprehensiveness of energy scheduling parameters may be improved. For example, the detailed model and parameter information of carbon capture and storage (CCS) are considered. Hence, the scheduling plans generated by the embodiments of the present application may have wider applicability, thus further improving comprehensive energy efficiency and reducing carbon emissions. Second, the low-carbon virtual power plant scheduling method in this application does not require predetermined probability distributions of renewable energy output. Hence, the flexibility and applicability of the virtual power plant scheduling may be improved.
The present application will be further described in detail below with reference to the drawings and embodiments. Although certain embodiments of the present application are shown in the drawings, it should be noted that the present application may be implemented in various forms and should not be limited to the embodiments described here. On the contrary, these embodiments are provided for a more thorough and complete understanding of the present application. It should be noted that the drawings and embodiments disclosed in this application are only for illustrative purposes and are not intended to limit the scope of protection of this application.
Furthermore, it should be noted that for the convenience of description, only the parts related to the present application are shown in the drawings. The embodiments of the present application and the features in the embodiments may be combined with each other without conflict.
It should be noted that the concepts such as “first” and “second” mentioned in the present application are only used to distinguish different devices, modules, or units, and are not intended to limit the order or interdependence of the functions performed by these devices, modules or units.
It should be noted that the “one” and “multiple” mentioned in the present application are indicative rather than restrictive. It should be understood by the technicians of this field that they should be understood as “one or more”, unless otherwise explicitly stated in the context.
The names of the messages or information exchanged between the devices in the embodiments are only for illustrative purposes but are not intended to limit the scope of these messages or information.
The present application will be described in detail below with reference to the drawings and embodiments.
In some embodiments, the IES operator may obtain the energy device information set for each energy device in the IES through a wired connection or wireless connection. The energy device information in the above energy device information set comprises energy device names, energy device parameter information and energy device numbers. The energy devices correspond one-to-one to the energy device information in the energy device information set. The above energy devices may include photovoltaic systems (PV), combined heat and power units (CHP), carbon capture and storage devices (CCS), power-to-gas devices (P2G), and energy storage devices (ES). There is no limit on the number of PVs, CHPs, CCSs, P2Gs, or ESs mentioned above. The energy device name of PVs may be “photovoltaic system”. The energy device parameters of the PV may include its unit operating cost, unit penalty cost for solar power curtailment, solar power curtailment at each scheduling period, actual output power at each scheduling period, and predicted output power at each scheduling period. The above unit operating cost may be the cost per unit of electric power produced by the PV, whose unit may be “$/kW”. The above unit penalty cost for solar power curtailment may be the pre-set penalty cost of abandoning unit solar power generation. The predicted output power may be pre-set based on weather conditions. The above solar power curtailment may be the abandoned power generation of the PV. The energy device name of CHPs may be “combined heat and power unit”. The above CHPs may be the CHPs equipped with CCSs. The energy device parameters of the CHP may include its unit start-up cost, unit shut-down cost, unit operating cost, electric-to-heat ratio, natural gas-to-electric ratio, natural gas-to-heat ratio, ramp-down rate, ramp-up rate, minimum natural gas consumption rate, maximum natural gas consumption rate, unit carbon emission intensity, natural gas consumption rate at each scheduling period, start-up indicator variable at each scheduling period, shut-down indicator variable at each scheduling period, operating status at each scheduling period, electric output power at each scheduling period, and thermal output power at each scheduling period. The above unit start-up cost may be the cost of starting up the CHP once. The above unit shut-down cost may be the cost of shutting up the CHP once. The energy device parameters of the CCS may include its unit operating cost, CO2 capture rate at each scheduling period, and CO2 capture efficiency. The energy device name of P2Gs may be “power-to-gas device”. The P2G may include the power-to-hydrogen (P2H) device and the hydrogen-to-natural gas (H2N) device. The energy device parameters of the P2G may include its unit operating cost, consumed electric power at each scheduling period, and the parameters of the P2H and the H2N. The energy device parameters of the P2H may include its conversion coefficient, ramp-down rate, ramp-up rate, maximum consumed electric power, consumed electric power at each scheduling period, and hydrogen production at each scheduling period. The energy device parameters of the H2N may include its conversion coefficient, maximum natural gas production rate, ramp-down rate, ramp-up rate, natural gas production rate at each scheduling period, hydrogen consumption rate at each scheduling period, and CO2 consumption rate at each scheduling period. The energy device name of ESs may be “energy storage device”. The ES may include the electric storage (BT), thermal storage (TS), natural gas storage (GS), and hydrogen storage (HS). The energy device parameters of the ES may include its unit operating cost, minimum input power, maximum input power, minimum output power, maximum output power, minimum remaining energy state, energy loss rate, charging efficiency, discharging efficiency, input power at each scheduling period, output power at each scheduling period, and remaining energy state at each scheduling period. It should be noted that wireless connection methods may include but are not limited to 3G/4G connection, WiFi connection, Bluetooth connection, WiMAX connection, Zigbee connection, UWB (ultra wideband) connection, and other known or future-developed wireless connection methods.
In some embodiments, based on the energy device names, energy device parameter information and energy device numbers in the energy device information set, the IES operator may generate the energy acquisition cost information for each energy device in the IES. In practice, the IES operator may generate the energy acquisition cost information for each energy device in the IES through the following steps:
where in (1), FPV is the total operating cost of PVs, ωPVcur is unit penalty cost for solar power curtailment of PVs, PPV,i,tcur is the solar power curtailment of the i-th PV at the t-th scheduling period, Ω is the set of the scheduling periods, EPV is the set of PVs, and Δt is the time interval. The above time interval may be 15 minutes or 1 hour. ωPV,i,t is the unit operating cost of the i-th PV, and PPV,i,t is the actual output power of the i-th PV at the t-th scheduling period.
where in (2), FCHPu−d is the total start-up and shutdown costs of CHPs, ECHP is the set of CHPs, ωCHP,ion is the unit start-up cost of the i-th CHP, ωCHP,ioff is the unit shut-down cost of the i-th CHP, ui,ton is the start-up indicator variable of the i-th CHP at the t-th scheduling period, ui,toff is the shut-down indicator variable of the i-th CHP at the t-th scheduling period. In (3), FCHP is the total operating cost of CHPs, ωCHP,i,t is the unit operating cost of the i-th CHP, and NCHP,i,t is the natural gas consumption rate of the i-th CHP at the t-th scheduling period.
where in (4), FCCS is the total operating cost of CCSs, ECCS is the set of CCSs, ωCCS,i is the unit operating cost of the i-th CCS, and CCCS,i,t is the CO2 capture rate of the i-th CCS at the t-th scheduling period.
where in (5), FP2G is the total operating cost of P2Gs, EP2G is the set of P2Gs, ωP2G,i is the unit operating cost of the i-th P2G, and PP2G,i,t is the consumed electric power of the i-th P2G at the t-th scheduling period.
where in (6), FES is the total operating cost of ESs, and Θ is the set of ES types. The above set of ES types may include electric energy storage, thermal energy storage, natural gas storage, and hydrogen storage. Er is the set of the r-th type ESs, ωr,i is the unit operating cost of the r-th type i-th ES, Qr,i,tc is the input power of the r-th type i-th ES at the t-th scheduling period, and Qr,i,td is the output power of the r-th type i-th ES at the t-th scheduling period.
where in (7), Fdev is the total operating cost of the energy devices in the IES.
In some embodiments, the IES operator may generate the IES energy scheduling objective values and objective energy device parameter information based on the preset constraint sets and energy acquisition cost information of each energy device. The above IES energy scheduling objective values may be the minimum total operating cost of the IES. The above objective energy device parameter information may include the objective energy device parameter information of each PV, CHP, CCS, P2G, BT, TS, GS, HS, energy purchases from the energy grid, or adjustable load. The objective energy device parameter information of each PV may include its solar power curtailment and actual output power at each scheduling period. The objective energy device parameter information of each CHP may include its natural gas consumption rate, start-up indicator variable, shut-down indicator variable, operating status, electric output power, and thermal output power at each scheduling period. The objective energy device parameter information of each CCS may include its CO2 capture rate at each scheduling period. The objective energy device parameter information of each P2G may include its consumed electric power at each scheduling period and the objective energy device parameter information of the P2H and H2N. The objective energy device parameter information of each P2H may include its consumed electric power and hydrogen production rate at each scheduling period. The objective energy device parameter information of each H2N may include its natural gas production rate, hydrogen consumption rate, and CO2 consumption rate at each scheduling period. The objective energy device parameter information of each BT, TS, GS, or HS may include its input power, output power, and remaining energy state at each scheduling period. The objective energy device parameter information of the energy purchases from the energy grid may include the purchased electric power, purchased thermal power, and purchased natural gas from the energy grids at each scheduling period. The objective energy device parameter information of the adjustable load may include the transferable and interruptible load. The above energy grids may include the electric grid, thermal grid, and natural gas grid. The above load may include the electric load, thermal load, and hydrogen load. The above constraint set may include the operating constraints of each PV, P2G, CHP, ES, integrated demand response, and energy balance constraints. The operating constraints of each PV may be expressed by the following formulas:
0≤PPV,i,t≤PPV,i,tmax, ∀i∈ EPV, t∈ Ω (8)
PPV,i,tcur=PPV,i,tmax−PPV,i,t, ∀i ∈ EPV, t ∈ Ω (9)
where in (8), PPV,i,t is the actual output power of the i-th PV at the t-th scheduling period, and PPV,i,tmax is the predicted output power of the i-th PV at the t-th scheduling period.
The operating constraints of each P2G may be expressed by the following formulas:
HP2H,i,t=ηP2HPP2H,i,t, ∀i∈ EP2G, t∈ Ω (10)
ΔPP2H,imin≤PP2H,i,t−PP2H,i,t−1≤ΔPP2H,imax, ∀i ∈ EP2G, t ∈ Ω (11)
0≤PP2H,i,t≤PP2H,imax, ∀i∈ EP2G, t∈ Ω (12)
NH2N,i,t=ηH2NHH2N,i,t, ∀i∈ EP2G, t∈ Ω (13)
CH2N,i,t=γHH2N,i,t, ∀i∈ EP2G, t∈ Ω (14)
0≤NH2N,i,t≤NH2N,imax, ∀i∈ EP2G, t∈ Ω (15)
ΔNH2N,imin≤NH2N,i,t−NH2N,i,t−1≤ΔNH2N,imax, ∀i∈ EP2G, t∈ Ω (16)
where in (10), HP2H,i,t is the hydrogen production rate of the power-to-hydrogen (P2H) device in the i-th P2G at the t-th scheduling period, and ηP2H is the conversion coefficient of the P2H device in the i-th P2G. Equation (10) represents the relationship between hydrogen production and electric power consumption. In (11), PP2H,i,t is the consumed electric power of the P2H device in the i-th P2G at the t-th scheduling period, ΔPP2H,imin is the ramp down rate of the P2H device in the i-th P2G, and ΔPP2H,imax is the ramp up rate of the P2H device in the i-th P2G. In (12), PP2H,imax is the maximum consumed electric power of the P2H device in the i-th P2G. Constraints (11) and (12) limit the electric power consumption and its variation of the P2H device in the i-th P2G. In (13), NH2N,i,t is the natural gas production rate of the hydrogen-to-natural gas (H2N) device in the i-th P2G at the t-th scheduling period, HH2N,i,t is the hydrogen consumption rate of the hydrogen-to-natural gas (H2N) device in the i-th P2G at the t-th scheduling period, and ηH2N is the conversion coefficient of the H2N device in the P2G. Constraint (13) represents the relationship between the natural gas production rate and hydrogen consumption rate of the H2N device in the i-th P2G. In (14), CH2N,i,t is the CO2 consumption rate of the H2N device in the i-th P2G and γ is the proportion of hydrogen and CO2 in methanation. Constraint (14) represents the relationship between the CO2 consumption rate and hydrogen consumption rate in the H2N device. In (15), NH2N,imax is the maximum natural gas production rate of the H2N device in the i-th P2G. In (16), ΔNH2N,imin is the ramp-down rate of the H2N device in the i-th P2G and ΔNH2N,imax is the ramp-up rate of the H2N device in the i-th P2G. Constraints (15) and (16) limit the natural gas production rate and its variation of the H2N device in the i-th P2G.
The operating constraints of each CHP may be expressed by the following formulas:
PCHP,i,t=ηCHPE−TTCHP,i,t, ∀i ∈ ECHP, t ∈ Ω (17)
NCHP,i,t=ηCHPN−EPCHP,i,t+ηCHPN−TTCHP,i,t, ∀i ∈ ECHP, t ∈ Ω (18)
ΔNCHP,imin≤NCHP,i,t−NCHP,i,t−1≤ΔNCHP,imax, ∀i ∈ ECHP, t ∈ Ω (19)
zi,tNCHP,imin≤NCHP,i,t≤zi,tNCHP,imax, ∀i ∈ ECHP, t ∈Ω (20)
ui,ton−ui,toff=zi,t+1−zi,t, ∀i ∈ ECHP, t ∈ Ω (21)
ui,ton+ui,toff≤1, ∀i ∈ ECHP, t ∈ Ω (22)
CCCS,i,t=ηCCSeCPCHP,i,t, ∀i∈ ECHP, t∈ Ω (23)
where in (17), ηCHPE−T is the electric-to-heat ratio of the CHP. Constraint (17) represents the relationship between the electric output power and thermal output power of the i-th CHP. In (18), ηCHPN−E is the natural gas-to-electric ratio of the CHP and ηCHPN−T is the natural gas-to-heat ratio of the CHP. Constraint (18) represents the relationship between natural gas consumption, electric power, and thermal power. In (19), ΔNCHP,imin is the ramp-down rate of the i-th CHP and ΔNCHP,imax is the ramp-up rate of the i-th CHP. In (20), NCHP,imin is the minimum natural gas consumption rate of the i-th CHP, NCHP,imax is the maximum natural gas consumption rate of the i-th CHP, and zi,t is the operating status of the i-th CHP at the t-th scheduling period. Constraints (19) and (20) represent the upper and lower bounds of the natural gas consumption rate of the CHP and its rate of change. Constraint (21) represents the relationship between the start-up status, shut-down status and the sign of start-up and shutdown of the i-th CHP. Constraint (22) ensures that CHP cannot start and shut down at the same time. In (23), CCCS,i,t is the CO2 capture rate of the i-th CCS at the t-th scheduling period, ηCCS is the CO2 capture efficiency of the CCS, and eC is the unit carbon emission intensity of the CHP.
The operating constraints of each ES may be expressed by the following formulas:
where in (24), Qr,ic,min in is the minimum input power of the r-th type ES i and Qr,ic,max is the maximum input power of the r-th type ES i. In (25), Qr,id,min is the minimum output power of the r-th type ES i and Qr,id,max is the maximum output power of the r-th type ES i. Constraints (24) and (25) specify the range of input and output power of the ES. In (26), Sr,imin is the minimum remaining energy state of the r-th type ES i, Sr,imax is the maximum remaining energy state of the r-th type ES i, and Sr,i,t is the remaining energy state of the r-th type ES i at the t-th scheduling period. In (27), λr,i is the energy loss rate of the r-th type ES i, ηr,ic is the charging efficiency of the r-th type ES i, and ηr,id is the discharging efficiency of the r-th type ES i.
The constraints for integrated demand response may be expressed by the following formulas:
Σt∈ΩDshift+,v,t=Σt∈ΩDshift−,v,t, ∀v ∈ M (28)
Dshift+,vmin≤Dshift+,v,t≤Dshift+,vmax, ∀v ∈ M, t ∈ Ω (29)
Dshift−,vmin≤Dshift−,v,t≤Dshift−,vmax, ∀v ∈ M, t ∈ Ω (30)
Dcut,vmin≤Dcut,v,t≤Dcut,vmax, ∀v∈ M, t∈ Ω (31)
Dload,v,t=Dload0,v+Dshift+,v,t−Dshift−,v,t−Dcut,v,t, ∀v ∈ M, t ∈ Ω (32)
where in (28), M is the set of load types, Dshift+,v,t is the load transfer-in of the v-th type load at the t-th scheduling period load and Dshift−,v,t is the load transfer-out of the v-th type load at the t-th scheduling period load. In (29), Dshift+,vmin is the minimum load transfer-in of the v-th type load and Dshift+,vmax is the maximum load transfer-in of the v-th type load. In (30), Dshift−,vmin is the minimum load transfer-out of the v-th type load and Dshift−,vmax is the maximum load transfer-out of the v-th type load. In (31), Dcut,v,t is the interruptible load of the v-th type load at the t-th scheduling period load, Dcut,vmin is the minimum interruptible load of the v-th type load, and Dcut,vmax is the maximum interruptible load of the v-th type load. In (32), Dload,v,t is the value of the v-th type load at the t-th scheduling period load after integrated demand response and Dload0,v is the preset basic load of the v-th type load.
The energy balance constraints may be expressed by the following formulas:
where in (33), Pload,t is the total electric load in the IES at the t-th scheduling period, Ppur,t is the purchased electric power from electric grid at the t-th scheduling period, EBT is the set of electric storage (BT), and QBT,i,t is the output electric power of the i-th BT at the t-th scheduling period. In (34), Tload,t is the total thermal load in the IES at the t-th scheduling period, Tpur,t is the purchased thermal power from heat grid at the t-th scheduling period, ETS is the set of thermal storage (TS), and QTS,i,t is the output thermal power of the i-th TS at the t-th scheduling period. In (35), Nload,t is the total natural gas load in the IES at the t-th scheduling period, Npur,t is the purchased natural gas from natural gas grid at the t-th scheduling period, EGS is the set of gas storage (GS), and QGS,i,t is the natural gas generation rate of the i-th GS at the t-th scheduling period. In (36), Hload,t is the total hydrogen load in the IES at the t-th scheduling period, EHS is the set of hydrogen storage (HS), and QHS,i,t is the hydrogen generation rate of the i-th HS at the t-th scheduling period.
In practice, based on the above operating constraints and the acquisition cost information of the energy devices, the IES operator may obtain the following objective function for the low-carbon IES scheduling:
min F=FCHPu−d+Fdev+Fpur+Fidr (38)
where in (38), F is the objective function of the low-carbon IES scheduling model and Fpur is the total energy purchase cost. Fpur may be obtained by the following formula:
where in (39), ωpurE is the electricity price, ωpurT is the thermal price, and ωpurG is the natural gas price.
In (38), Fidr is the subsidy cost for integrated demand response that the IES needs to pay. Fidr may be obtained by the following formula:
where in (40), ωD,shift,v is the subsidy cost for the v-th type transferable load and ωD,cut,v is the subsidy cost for the v-th type interruptible load.
Optionally, before S103, the IES operator may perform the following steps:
In some alternative implementations of some embodiments, the IES operator may generate the IES energy scheduling objective values and objective energy device parameter information based on the preset constraint sets and energy acquisitioncost information of each energy device through the following steps:
where in (41), ps is the actual probability value of the s-th second PV power scenario, Ψ is the feasible region of the probability value of second PV power scenario, and Φ is the set of second PV power scenarios.
where in (42), ps0 is the preset probability value of the s-th second PV power scenario and ρ1 is a preset parameter.
The above norm-inf based constraints may be represented by the following formula:
where in (43), ρ∞ is a preset parameter.
Finally, the norm-1 based constraints and norm-inf based constraints constitute the fourth constraint set.
where δs is the preset first auxiliary variable.
Then, the norm-inf based constraints in the fourth constraint set may be equivalently transformed into the norm-inf based linear constraints in the linear constraint set by introducing the second auxiliary variable.
σs≤ρ∞, σs≥ps−ps0, σs≥ps0−ps, ∀s∈ Φ (44)
where σs is the preset second auxiliary variable.
In some embodiments, various methods may be used to control the energy devices in the IES to execute the energy scheduling tasks based on the objective energy device parameter information. In practice, the IES operator may control each PV in various ways to ensure that its operating parameters at each scheduling period are equal to its objective energy device parameter information. The IES operator may control each CHP in various ways to ensure that its operating parameters at each scheduling period are equal to the same as objective energy device parameter information. The IES operator may control each P2G in various ways to ensure that its operating parameters at each scheduling period are equal to the same as objective energy device parameter information. The IES operator may control each BT in various ways to ensure that its operating parameters at each scheduling period are equal to the same as objective energy device parameter information. The IES operator may control each TS in various ways to ensure that its operating parameters at each scheduling period are equal to the same as objective energy device parameter information. The IES operator may control each GS in various ways to ensure that its operating parameters at each scheduling period are equal to the same as objective energy device parameter information. The IES operator may control each HS in various ways to ensure that its operating parameters at each scheduling period are equal to the same as objective energy device parameter information.
In some alternative implementations of certain embodiments, the IES operator may control the energy devices in the IES to execute energy scheduling tasks based on the objective energy device parameter information through the following steps:
Optionally, the IES operator may perform the following steps:
Optionally, the IES operator may perform the following steps:
Further, referring to
As shown in
It may be understood that the units recorded in device 200 correspond to the various steps in the method described with reference to
The flowchart and block diagram in the attached figure illustrates the possible architecture, functions, and operations of systems, methods, and computer program products according to various embodiments of the present application. At this point, each box in a flowchart or block diagram may represent a module, program segment, or part of code that contains one or more executable instructions for implementing specified logical functions. It should also be noted that in some alternative implementations, the functions indicated in the boxes may also occur in a different order than those indicated in the accompanying drawings. For example, two consecutive boxes may actually be executed in parallel, and sometimes they may also be executed in the opposite order, depending on the function involved. It should also be noted that each box in the block diagram and/or flowchart, as well as the combination of boxes in the block diagram and/or flowchart, may be implemented using dedicated hardware-based systems that perform specified functions or operations, or may be implemented using a combination of dedicated hardware and computer instructions.
The units described in some embodiments of the present application may be implemented through software or hardware. The described unit may also be set in the processor, for example, it may be described as: a processor comprising an acquisition unit, a first generation unit, a second generation unit, and a control unit. In some cases, the names of these units do not constitute a limitation of the unit itself. For example, the acquisition unit may also be described as “the unit that obtains the information set of the energy devices in the virtual power plant”.
The functions described above in this article may be at least partially executed by one or more hardware logic components. For example, non-limiting examples of hardware logic components that may be used include: Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), On Chip Systems (SOC), Complex Programmable Logic Devices (CPLDs), and so on.
The above description is only for some preferred embodiments of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention referred to in the disclosed embodiments is not limited to technical solutions formed by specific combinations of the aforementioned technical features, but should also cover other technical solutions formed by any combination of the aforementioned technical features or equivalent features without departing from the aforementioned invention concept. For example, the technical solution formed by replacing the above features with (but not limited to) technical features with similar functions disclosed in the disclosed embodiments.
Number | Date | Country | Kind |
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202210964574.5 | Aug 2022 | CN | national |
Number | Name | Date | Kind |
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20100235206 | Miller | Sep 2010 | A1 |
20150339762 | Deal | Nov 2015 | A1 |
20160350778 | Levine | Dec 2016 | A1 |
20180284758 | Cella | Oct 2018 | A1 |
20190324444 | Cella | Oct 2019 | A1 |
Number | Date | Country |
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110750758 | Feb 2020 | CN |
111064229 | Apr 2020 | CN |
114498641 | May 2022 | CN |
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20240055869 A1 | Feb 2024 | US |