DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-13, 15, and 16 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Sortomme et al. “Optimal Scheduling of Vehicle-to-Grid Energy and Ancillary Services,” IEEE Transactions on Smart Grid, Vol. 3, No. 1, March 2012 (“Sortomme”).
Sortomme discloses:
1. A computer implemented method for managing a distributed energy storage (DES) arrangement to participate in grid balancing, wherein the DES arrangement comprises a pool of nodes (As detailed from the 5th paragraph to the end of Section II, Sortomme discloses ancillary services provided by a plurality of electric vehicles, i.e. distributed energy storage systems; their charging and discharging is managed by an aggregator, which use them to provide ancillary services to the grid), the method comprising:
monitoring energy levels of battery systems of the nodes of the DES arrangement (The state of charge of each battery is monitored, as indicated by constraints (15)-(20) in the optimization problem formulated in Section III);
identifying, based on monitoring the energy levels, one or more target nodes for optimization (The optimization results comprise the variables MnAPi, MxAPi and RsRPi, which translate the participation of each vehicle to the ancillary services); and
enabling node specific up or down regulation actions for the target nodes irrespective of grid balancing actions of the whole DES arrangement, wherein the node specific up regulation action comprises discharging battery systems of the nodes and node specific down regulation action comprises charging battery systems of the nodes based on conditions of the individual battery systems (The regulation commands are up or down regulation, and correspond to a discharge or a charge of the battery; see Fig. 3; the constraint (17) on the final charge level of each electric vehicle assures that a vehicle might be charged even when the total regulation provided by the aggregator to the grid is a down regulation; another example of charge in case of down regulation is given in the paragraph before last on page 354).
2. The method of claim 1, wherein the target nodes are risk nodes that are at risk of being at least partially disabled from participating in grid balancing (the constraints (15)-(17) will increase the probability of a charged battery to be discharged, as well as the probability of a discharged battery to be charged).
3. The method of claim 1, further comprising activating node specific up or down regulation actions at least for one of the target nodes (the commands resulting from the optimization are implemented in Sections V and VI).
4. The method of claim 1, further comprising enabling node specific up regulation actions at least for one of the target nodes, while the whole DES arrangement is performing down regulation actions (the optimization enables the charge during up regulation, and the discharge during down regulation; the sum of the individual regulations has to mee the regulation required by the grid).
5. The method of claim 1, further comprising enabling node specific down regulation actions at least for one of the target nodes, while the whole DES arrangement is performing up regulation actions (the optimization enables the charge during up regulation, and the discharge during down regulation; the sum of the individual regulations has to mee the regulation required by the grid).
6. The method of claim 1, further comprising enabling the node specific up or down regulation actions so that overall effect of those on grid balancing of the whole DES arrangement is zero during periods of no regulation actions by the whole DES arrangement (the optimization enables the charge during up regulation, and the discharge during down regulation; the sum of the individual regulations has to mee the regulation required by the grid).
7. The method of claim 1, wherein said identifying is further based on grid balancing commitments and/or predefined operating objectives of the whole DES arrangement (the optimization enables the charge during up regulation, and the discharge during down regulation; the sum of the individual regulations has to mee the regulation required by the grid).
8. The method claim 1, wherein said identifying is further based on at least one of the following: predicted grid balancing activation needs, energy levels of other battery systems of the DES arrangement, predefined energy balancing plans, predefined minimum/maximum energy levels, predefined minimum/maximum power levels, availability of different energy sources (the command optimized for each battery depends on the states of the other batteries, as well as expected regulation needs).
9. The method claim 1, further comprising identifying a node as a target node, if the battery system of the node is close to a minimum energy level, when up regulation need is expected; and/or identifying a node as a target node, if the battery system of the node is close to a maximum energy level, when down regulation need is expected (the constraints (15)-(17) will increase the probability of a charged battery to be discharged, as well as the probability of a discharged battery to be charged).
10. The method of claim 1, further comprising adjusting grid balancing actions of the whole DES arrangement to compensate the node specific up or down regulation actions (the optimization enables the charge during up regulation, and the discharge during down regulation; the sum of the individual regulations has to mee the regulation required by the grid).
11. The method claim 1, further comprising forecasting, whether the whole DES arrangement is at risk of being unable to fulfil grid balancing commitments due to operation of the identified target nodes; and performing the enabling step responsive to forecasting that the whole DES arrangement is at risk of being unable to fulfil grid balancing commitments (Sortomme uses forecasts for the regulation needs (Equations (2)-(5)).
12. The method of claim 1, wherein the forecasting is based on assuming continuous regulation needs, based on simulation of regulation needs and/or based on observed historical regulation needs (Sortomme uses forecasts for the regulation needs (Equations (2)-(5)).
13. The method of claim 1, further comprising:
detecting that the battery systems of a plurality of nodes are close to a maximum energy level; and responsively discharging the battery systems of the plurality of nodes by local consumption to set a new base energy level for the plurality of nodes; and/or
detecting that the battery systems of a plurality of nodes are close to a minimum energy level; and responsively charging the battery systems of the plurality of nodes to set a new base energy level for the plurality of nodes (the constraints (15)-(17) will increase the probability of a charged battery to be discharged, as well as the probability of a discharged battery to be charged).
15. An apparatus comprising a processor and a memory including computer program code, and wherein the memory and the computer program code are configured to, with the processor, cause the performance of the method of claim 1 (e.g., Figs. 1-2).
16. A non-transitory computer readable medium having the computer program comprising computer executable program code which when executed in an apparatus causes the apparatus to perform the method claim 1 (e.g., Figs. 1-2).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Guerrero Hernandez et al. US 2024/0055863 discloses a system for managing distributed energy storage systems like a Virtual Power Plant (VPP). The system is adapted to process grid services requests from grid operators and/or market agents, and to dynamically set clusters of energy storage devices based on properties of the energy storage devices, and selecting those energy storage devices that are suitable to fulfill grid service operation requirements of at least one grid service. The system is further adapted to allow injection or absorption of energy to or from the grid during a time period, through at least one cluster of selected energy storage devices, to provide at least one grid service demanded by grid operators and/or market agents. The system of the invention enables the participation of a battery energy storage system in a plurality of grid services at the same time, so as to optimize the use of prosumers battery storage capacity, and enlarge prosumers participation in the electricity market.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RYAN A JARRETT whose telephone number is (571)272-3742. The examiner can normally be reached M-F 9:00-5:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kenneth Lo can be reached at 571-272-9774. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RYAN A JARRETT/Primary Examiner, Art Unit 2116
07/29/26
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