Paper Citation Record · LEDGER

Hierarchical Constrained Reinforcement Learning with Dynamic Boundary for Spatio-Temporal Vehicle-to-Grid Scheduling

As of 16 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2608.03409.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2608.03409 v1

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T14:54:45.769653Z

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

34 of 34 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 256d0020-e4b0-4efe-9bf1-8c2bbe146aba · outbound

This paper cites A large-scale high- resolution geographic analysis of impacts of electric vehicle charging on low-voltage grids,.

Hierarchical Constrained Reinforcement Learning with Dynamic Boundary for Spatio-Temporal Vehicle-to-Grid Scheduling A large-scale high- resolution geographic analysis of impacts of electric vehicle charging on low-voltage grids,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:54:46.517596Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation fc693326-db97-425f-a924-956325ee4332 · outbound

This paper cites An investigation of plug-in electric vehicle charging impact on power systems scheduling and energy costs,.

Hierarchical Constrained Reinforcement Learning with Dynamic Boundary for Spatio-Temporal Vehicle-to-Grid Scheduling An investigation of plug-in electric vehicle charging impact on power systems scheduling and energy costs,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:54:46.495939Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 21232e31-cccd-440b-8360-9ae286a32679 · outbound

This paper cites A non-iterative de- coupled solution of the coordinated robust opf in transmission and distribution networks with variable generating units,.

Hierarchical Constrained Reinforcement Learning with Dynamic Boundary for Spatio-Temporal Vehicle-to-Grid Scheduling A non-iterative de- coupled solution of the coordinated robust opf in transmission and distribution networks with variable generating units,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:54:46.476132Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3f1e2ad6-b3e6-44cc-a30f-4d55b950dd38 · outbound

This paper cites Two-stage optimization for efficient v2g coordination in distribution power system,.

Hierarchical Constrained Reinforcement Learning with Dynamic Boundary for Spatio-Temporal Vehicle-to-Grid Scheduling Two-stage optimization for efficient v2g coordination in distribution power system,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:54:46.457122Z

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Observation b5f97872-9c8a-4f6f-a0e5-abe39017e2d2 · outbound

This paper cites Complexity of branch- and-bound and cutting planes in mixed-integer optimization—ii,.

Hierarchical Constrained Reinforcement Learning with Dynamic Boundary for Spatio-Temporal Vehicle-to-Grid Scheduling Complexity of branch- and-bound and cutting planes in mixed-integer optimization—ii,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:54:46.437833Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ad49ae71-7ea5-4521-b690-9da4ca2182f0 · outbound

This paper cites Route planning for a fleet of electric vehicles with waiting times at charging stations,.

Hierarchical Constrained Reinforcement Learning with Dynamic Boundary for Spatio-Temporal Vehicle-to-Grid Scheduling Route planning for a fleet of electric vehicles with waiting times at charging stations,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:54:46.419621Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 8d4fea9c-29f2-4c11-89ca-fd5e2ce4f25a · outbound

This paper cites Optimized in- tegration of electric vehicles in low voltage distribution grids,.

Hierarchical Constrained Reinforcement Learning with Dynamic Boundary for Spatio-Temporal Vehicle-to-Grid Scheduling Optimized in- tegration of electric vehicles in low voltage distribution grids,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:54:46.396777Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b49149ec-302f-4fb0-adfc-da68757a5001 · outbound

This paper cites Model predic- tive control for smart grids with multiple electric-vehicle charging stations,.

Hierarchical Constrained Reinforcement Learning with Dynamic Boundary for Spatio-Temporal Vehicle-to-Grid Scheduling Model predic- tive control for smart grids with multiple electric-vehicle charging stations,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:54:46.372447Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f5ca329b-5963-4b51-977a-7ac47f0a7f7c · outbound

This paper cites Deepopf: deep neural networks for optimal power flow,.

Hierarchical Constrained Reinforcement Learning with Dynamic Boundary for Spatio-Temporal Vehicle-to-Grid Scheduling Deepopf: deep neural networks for optimal power flow,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:54:46.353819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 53fe6fb2-f5c0-4f58-a3a6-a49a9b0d500c · outbound

This paper cites Physics-informed reinforcement learning for real-time optimal power flow with renewable energy resources,.

Hierarchical Constrained Reinforcement Learning with Dynamic Boundary for Spatio-Temporal Vehicle-to-Grid Scheduling Physics-informed reinforcement learning for real-time optimal power flow with renewable energy resources,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:54:46.335399Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1480f790-8882-4ae3-addb-1f7c744b35af · outbound

This paper cites Physics-informed graphical representation-enabled deep reinforcement learning for robust distribu- tion system voltage control,.

Hierarchical Constrained Reinforcement Learning with Dynamic Boundary for Spatio-Temporal Vehicle-to-Grid Scheduling Physics-informed graphical representation-enabled deep reinforcement learning for robust distribu- tion system voltage control,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:54:46.316165Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 01146db5-108e-40c8-a04e-611c26047580 · outbound

This paper cites Learning to operate an electric vehicle charging station considering vehicle-grid integration,.

Hierarchical Constrained Reinforcement Learning with Dynamic Boundary for Spatio-Temporal Vehicle-to-Grid Scheduling Learning to operate an electric vehicle charging station considering vehicle-grid integration,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:54:46.297969Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation eddcd866-e5c5-4465-8401-7e6c9579f1c9 · outbound

This paper cites Definition and evaluation of model-free coordination of electrical vehicle charging with reinforcement learning,.

Hierarchical Constrained Reinforcement Learning with Dynamic Boundary for Spatio-Temporal Vehicle-to-Grid Scheduling Definition and evaluation of model-free coordination of electrical vehicle charging with reinforcement learning,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:54:46.273846Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b7c218d5-1dde-4189-9c23-76de3870a405 · outbound

This paper cites Safe off-policy deep reinforcement learning algorithm for volt-var control in power distribution systems,.

Hierarchical Constrained Reinforcement Learning with Dynamic Boundary for Spatio-Temporal Vehicle-to-Grid Scheduling Safe off-policy deep reinforcement learning algorithm for volt-var control in power distribution systems,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:54:46.254168Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 75ee2575-63f0-4371-8b6a-6594c532f81f · outbound

This paper cites A hybrid data-driven method for fast solution of security- constrained optimal power flow,.

Hierarchical Constrained Reinforcement Learning with Dynamic Boundary for Spatio-Temporal Vehicle-to-Grid Scheduling A hybrid data-driven method for fast solution of security- constrained optimal power flow,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:54:46.232979Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1b99eee8-e3cc-462d-a766-1ff0ffe22247 · outbound

This paper cites Safe deep reinforcement learning- based constrained optimal control scheme for active distribution networks,.

Hierarchical Constrained Reinforcement Learning with Dynamic Boundary for Spatio-Temporal Vehicle-to-Grid Scheduling Safe deep reinforcement learning- based constrained optimal control scheme for active distribution networks,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:54:46.208457Z

Source-reported events for the cited work

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Observation b968d31d-0d61-43f6-9cf3-efd6820ede78 · outbound

This paper cites Model-augmented safe reinforcement learning for volt-var control in power distribution networks,.

Hierarchical Constrained Reinforcement Learning with Dynamic Boundary for Spatio-Temporal Vehicle-to-Grid Scheduling Model-augmented safe reinforcement learning for volt-var control in power distribution networks,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:54:46.187898Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1bff50c7-950b-49c3-b006-5778510eef5c · outbound

This paper cites A safe policy learning-based method for decentralized and economic frequency control in isolated networked-microgrid systems,.

Hierarchical Constrained Reinforcement Learning with Dynamic Boundary for Spatio-Temporal Vehicle-to-Grid Scheduling A safe policy learning-based method for decentralized and economic frequency control in isolated networked-microgrid systems,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:54:46.168222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation fde614d8-e947-4889-ba77-774e0b1adc48 · outbound

This paper cites Deepopf: A deep neural network ap- proach for security-constrained dc optimal power flow,.

Hierarchical Constrained Reinforcement Learning with Dynamic Boundary for Spatio-Temporal Vehicle-to-Grid Scheduling Deepopf: A deep neural network ap- proach for security-constrained dc optimal power flow,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:54:46.149259Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1b8d7160-e3f9-46f3-b424-c17daf34199a · outbound

This paper cites Dc3: A learning method for optimization with hard constraints,.

Hierarchical Constrained Reinforcement Learning with Dynamic Boundary for Spatio-Temporal Vehicle-to-Grid Scheduling Dc3: A learning method for optimization with hard constraints,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:54:46.131270Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a7fece29-1d1a-4c24-a6d2-6e4aa6af8efa · outbound

This paper cites Electric vehicle charging management based on deep reinforcement learning,.

Hierarchical Constrained Reinforcement Learning with Dynamic Boundary for Spatio-Temporal Vehicle-to-Grid Scheduling Electric vehicle charging management based on deep reinforcement learning,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:54:46.113630Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 973562ae-c36e-45a9-abb8-e747f2898cda · outbound

This paper cites A Deep Reinforcement Learning-Based Charging Scheduling Approach with Augmented Lagrangian for Electric Vehicle.

Hierarchical Constrained Reinforcement Learning with Dynamic Boundary for Spatio-Temporal Vehicle-to-Grid Scheduling A Deep Reinforcement Learning-Based Charging Scheduling Approach with Augmented Lagrangian for Electric Vehicle

Reference 22

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Observation f4a645fd-4f46-4717-b84c-d3b223d64939 · outbound

This paper cites Constrained ev charging scheduling based on safe deep reinforcement learning,.

Hierarchical Constrained Reinforcement Learning with Dynamic Boundary for Spatio-Temporal Vehicle-to-Grid Scheduling Constrained ev charging scheduling based on safe deep reinforcement learning,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:54:46.095483Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 72d089c4-c074-4016-9a59-4e909aba2d5c · outbound

This paper cites Network-constrained reinforcement learning for optimal ev charging control,.

Hierarchical Constrained Reinforcement Learning with Dynamic Boundary for Spatio-Temporal Vehicle-to-Grid Scheduling Network-constrained reinforcement learning for optimal ev charging control,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:54:46.076196Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 884a572b-8ee3-4f5e-8869-9cb224c42bf7 · outbound

This paper cites Uncertainty-aware critic augmentation for hierarchical multi-agent ev charging control,.

Hierarchical Constrained Reinforcement Learning with Dynamic Boundary for Spatio-Temporal Vehicle-to-Grid Scheduling Uncertainty-aware critic augmentation for hierarchical multi-agent ev charging control,

Reference 25

Resolution: verified exact; raw_fallback, observed 2026-08-15T14:54:45.914786Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b48e1ac6-5bd1-4e67-bbfa-a502000e5c68 · outbound

This paper cites Ev charging command fast allocation approach based on deep reinforcement learning with safety modules,.

Hierarchical Constrained Reinforcement Learning with Dynamic Boundary for Spatio-Temporal Vehicle-to-Grid Scheduling Ev charging command fast allocation approach based on deep reinforcement learning with safety modules,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:54:46.057040Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4614ef42-b78e-4e3c-9ddb-fc4660c51bdb · outbound

This paper cites Distributionally robust optimiza- tion for vehicle-to-grid with uncertain renewable energy,.

Hierarchical Constrained Reinforcement Learning with Dynamic Boundary for Spatio-Temporal Vehicle-to-Grid Scheduling Distributionally robust optimiza- tion for vehicle-to-grid with uncertain renewable energy,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:54:46.039398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 10dec163-3b76-4d10-9c12-eede7f47dda7 · outbound

This paper cites Reduced policy optimization for contin- uous control with hard constraints,.

Hierarchical Constrained Reinforcement Learning with Dynamic Boundary for Spatio-Temporal Vehicle-to-Grid Scheduling Reduced policy optimization for contin- uous control with hard constraints,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:54:46.021151Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ccb80cd5-550b-401d-a5a9-a8a40397b2d2 · outbound

This paper cites Constrained large-scale real-time ev scheduling based on recurrent deep reinforcement learning,.

Hierarchical Constrained Reinforcement Learning with Dynamic Boundary for Spatio-Temporal Vehicle-to-Grid Scheduling Constrained large-scale real-time ev scheduling based on recurrent deep reinforcement learning,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:54:46.005171Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 04b876a1-4f95-45e0-b384-cf8a860fab91 · outbound

This paper cites Discovering Temporal Structure: An Overview of Hierarchical Reinforcement Learning.

Hierarchical Constrained Reinforcement Learning with Dynamic Boundary for Spatio-Temporal Vehicle-to-Grid Scheduling Discovering Temporal Structure: An Overview of Hierarchical Reinforcement Learning

Reference 30

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Source-reported events for the cited work

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Observation 24d43121-4e65-40aa-9d94-a06ec92bc749 · outbound

This paper cites Constrained policy optimization,.

Hierarchical Constrained Reinforcement Learning with Dynamic Boundary for Spatio-Temporal Vehicle-to-Grid Scheduling Constrained policy optimization,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:54:45.982343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b972b589-e656-446d-807d-5067f6c8cf55 · outbound

This paper cites CUP: A Conservative Update Policy Algorithm for Safe Reinforcement Learning.

Hierarchical Constrained Reinforcement Learning with Dynamic Boundary for Spatio-Temporal Vehicle-to-Grid Scheduling CUP: A Conservative Update Policy Algorithm for Safe Reinforcement Learning

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-15T14:54:45.758379Z

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Observation df440459-b6e9-44eb-a47c-ebb5410c7478 · outbound

This paper cites De- terministic policy gradient algorithms,.

Hierarchical Constrained Reinforcement Learning with Dynamic Boundary for Spatio-Temporal Vehicle-to-Grid Scheduling De- terministic policy gradient algorithms,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:54:45.965997Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e5168db4-dc17-4082-821f-398f2ef875e6 · outbound

This paper cites Soft actor-critic: Off-policy maxi- mum entropy deep reinforcement learning with a stochastic actor,.

Hierarchical Constrained Reinforcement Learning with Dynamic Boundary for Spatio-Temporal Vehicle-to-Grid Scheduling Soft actor-critic: Off-policy maxi- mum entropy deep reinforcement learning with a stochastic actor,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:54:45.950473Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Pith citing papers

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