Paper Citation Record · LEDGER

Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids

As of 7 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 0 inbound Pith citation observations for arXiv:2606.25599.

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

pith.paper-citation-record.v1
2606.25599 v1

Coverage vector

measured 38 of 38 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-06-25T19:36:51.509710Z

measured 38 of 38 standing notices

One-hop event checks from named stored sources.

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Pith citing papers itemized under the disclosed page cap.

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Source: cited_works

Reference resolution

38 of 38 outbound references displayed

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External citation measurements

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Outbound references

Observation 7e33cb38-5e72-47cc-b2ca-c0660341f67d · outbound

This paper cites Toward sustainable and low-carbon industrial transformation: Insights from industrial green microgrids.

Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids Toward sustainable and low-carbon industrial transformation: Insights from industrial green microgrids

Reference 1

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Observation 69c37497-7f6c-4b00-a7f4-ec77d636616f · outbound

This paper cites A review of industrial load ﬂexibility enhancement for demand-response interaction.

Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids A review of industrial load ﬂexibility enhancement for demand-response interaction

Reference 2

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Observation 55c83a7f-f1e1-4768-9b53-43f01770c1d1 · outbound

This paper cites Multi-objective scheduling of a steelmaking plant integrated with renewable energy sources and energy storage systems: Balancing costs, emissions and make-span.

Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids Multi-objective scheduling of a steelmaking plant integrated with renewable energy sources and energy storage systems: Balancing costs, emissions and make-span

Reference 3

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Observation 22731a68-a410-4935-9a68-5271483c5832 · outbound

This paper cites Hybrid lithium-ion battery and hydrogen energy storage systems for a wind-supplied microgrid.

Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids Hybrid lithium-ion battery and hydrogen energy storage systems for a wind-supplied microgrid

Reference 4

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Observation 02facbc9-8eef-4e13-9f49-4e0c3f5e36d7 · outbound

This paper cites A demand response energy management scheme for industrial facilities in smart grid.

Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids A demand response energy management scheme for industrial facilities in smart grid

Reference 5

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Observation 81919fce-6cfc-452e-95b0-29edfc4596ec · outbound

This paper cites Optimal industrial load control in smart grid.

Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids Optimal industrial load control in smart grid

Reference 6

Resolution: unresolved; no resolver link, observed 2026-06-25T19:36:51.509710Z

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Observation 477f444a-dcb6-48bc-b31c-41a05480bc67 · outbound

This paper cites A real-time decision model for industrial load management in a smart grid.

Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids A real-time decision model for industrial load management in a smart grid

Reference 7

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Observation fa670e42-91f7-43f6-b835-ec47f5f0e0ab · outbound

This paper cites Demand response of ancillary service from industrial loads coordinated with energy storage.

Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids Demand response of ancillary service from industrial loads coordinated with energy storage

Reference 8

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Observation f8384408-ac9a-4e8c-858e-a043ed3880c5 · outbound

This paper cites Demand-side management via optimal production scheduling in power-intensive industries: The case of metal casting process.

Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids Demand-side management via optimal production scheduling in power-intensive industries: The case of metal casting process

Reference 9

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Observation d484b827-4aab-40b8-bbb2-14780baa9ebd · outbound

This paper cites Robust self-scheduling of operational processes for industrial demand response aggregators.

Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids Robust self-scheduling of operational processes for industrial demand response aggregators

Reference 10

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Observation 57a1b87b-831d-4f85-bb1c-9ae7320bab32 · outbound

This paper cites Cost-eﬀective scheduling of steel plants with ﬂexible eafs.

Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids Cost-eﬀective scheduling of steel plants with ﬂexible eafs

Reference 11

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Observation 13958372-4b81-44f9-94c6-eb7bb082fc49 · outbound

This paper cites Quantifying ﬂexibility provisions of the ladle furnace reﬁning process as cuttable loads in the iron and steel industry.

Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids Quantifying ﬂexibility provisions of the ladle furnace reﬁning process as cuttable loads in the iron and steel industry

Reference 12

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Observation 52a83e24-da57-40b0-b9a5-087349efca00 · outbound

This paper cites Cost-eﬀective scheduling of a hydrogen-based iron and steel plant powered by a grid-assisted renewable energy system.

Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids Cost-eﬀective scheduling of a hydrogen-based iron and steel plant powered by a grid-assisted renewable energy system

Reference 13

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Observation 79a27f74-fa88-42d5-b6aa-89b48d06e19c · outbound

This paper cites Distributionally robust chance-constrained energy management of steel industrial microgrid with energy storage in distribution market.

Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids Distributionally robust chance-constrained energy management of steel industrial microgrid with energy storage in distribution market

Reference 14

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Observation 6731ee10-c793-412a-b61c-c203c6aea5a8 · outbound

This paper cites Eﬃcient scheduling of discrete industrial processes through continuous modeling.

Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids Eﬃcient scheduling of discrete industrial processes through continuous modeling

Reference 15

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Observation bf832129-ce3a-4054-90b5-ea54959d5052 · outbound

This paper cites Smoothing tie-line power ﬂuctuations for industrial microgrids by demand side control: An output regulation approach.

Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids Smoothing tie-line power ﬂuctuations for industrial microgrids by demand side control: An output regulation approach

Reference 16

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Observation 69be4b9e-db03-46ac-9311-8343211c764a · outbound

This paper cites Suppressing active power ﬂuctuations at pcc in grid-connection microgrids via multiple besss: A collaborative multi-agent reinforcement learning approach.

Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids Suppressing active power ﬂuctuations at pcc in grid-connection microgrids via multiple besss: A collaborative multi-agent reinforcement learning approach

Reference 17

Resolution: unresolved; no resolver link, observed 2026-06-25T19:36:51.509710Z

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Observation c82039dd-4a22-4a4f-87ac-5f6d63667f64 · outbound

This paper cites Multi-time-scale energy management of renewable microgrids considering grid-friendly interaction.

Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids Multi-time-scale energy management of renewable microgrids considering grid-friendly interaction

Reference 18

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Observation e5a94b0e-877f-444d-96b1-824559590bb0 · outbound

This paper cites Flat tie-line power scheduling control of grid-connected hybrid microgrids.

Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids Flat tie-line power scheduling control of grid-connected hybrid microgrids

Reference 19

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Observation 9862a939-5432-4ad1-a6ed-42b0ec7c7684 · outbound

This paper cites A demand response and battery storage coordination algorithm for providing microgrid tie-line smoothing services.

Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids A demand response and battery storage coordination algorithm for providing microgrid tie-line smoothing services

Reference 20

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Observation 87cfd4a8-acd2-4dc8-ad66-926a4b9707f4 · outbound

This paper cites Data center holistic demand response algorithm to smooth microgrid tie-line power ﬂuctuation.

Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids Data center holistic demand response algorithm to smooth microgrid tie-line power ﬂuctuation

Reference 21

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Observation d5794086-2e0b-401a-9a4c-a93f3448c6ff · outbound

This paper cites Multi-data center tie-line power smoothing method based on demand response.

Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids Multi-data center tie-line power smoothing method based on demand response

Reference 22

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Observation f7e867f8-261b-45ce-9d29-d1be8330ad5c · outbound

This paper cites A novel rolling optimization strategy considering grid-connected power ﬂuctuations smoothing for renewable energy microgrids.

Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids A novel rolling optimization strategy considering grid-connected power ﬂuctuations smoothing for renewable energy microgrids

Reference 23

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Observation 6c57abae-8bae-41f8-ae0f-9140251a516b · outbound

This paper cites A coordinated multitimescale model predictive control for output power smoothing in hybrid microgrid incorporating hydrogen energy storage.

Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids A coordinated multitimescale model predictive control for output power smoothing in hybrid microgrid incorporating hydrogen energy storage

Reference 24

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Observation 2e186190-0ae4-4441-8f95-3c80cd670e04 · outbound

This paper cites Multi-agent deep reinforcement learning based demand response for discrete manufacturing systems energy management.

Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids Multi-agent deep reinforcement learning based demand response for discrete manufacturing systems energy management

Reference 25

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This paper cites Multi-agent deep reinforcement learning based demand response and energy management for heavy industries with discrete manufacturing systems.

Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids Multi-agent deep reinforcement learning based demand response and energy management for heavy industries with discrete manufacturing systems

Reference 26

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Observation 1d365505-2497-4bac-bad2-acfe53044fdc · outbound

This paper cites Energy management based on multi-agent deep reinforcement learning for a multi-energy industrial park.

Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids Energy management based on multi-agent deep reinforcement learning for a multi-energy industrial park

Reference 27

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Observation b2b8f6b9-f6cd-49b5-ba1f-b3c6a6dfcdad · outbound

This paper cites Coordination for multienergy microgrids using multiagent reinforcement learning.

Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids Coordination for multienergy microgrids using multiagent reinforcement learning

Reference 28

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Observation 4829e3ec-87a3-4848-82bc-e2f9af4bf668 · outbound

This paper cites Physics-model-free heat-electricity energy management of multiple microgrids based on surrogate model-enabled multi-agent deep reinforcement learning.

Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids Physics-model-free heat-electricity energy management of multiple microgrids based on surrogate model-enabled multi-agent deep reinforcement learning

Reference 29

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This paper cites Renewable energy integration and microgrid energy trading using multi-agent deep reinforcement learning.

Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids Renewable energy integration and microgrid energy trading using multi-agent deep reinforcement learning

Reference 30

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This paper cites Multi-agent reinforcement learning for energy management in microgrids with shared hydrogen storage.

Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids Multi-agent reinforcement learning for energy management in microgrids with shared hydrogen storage

Reference 31

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This paper cites Multi-agent hierarchical reinforcement learning for energy management.

Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids Multi-agent hierarchical reinforcement learning for energy management

Reference 32

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This paper cites Collaborative optimization of multi-energy multi-microgrid system: A hierarchical trust-region multi-agent reinforcement learning approach.

Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids Collaborative optimization of multi-energy multi-microgrid system: A hierarchical trust-region multi-agent reinforcement learning approach

Reference 33

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This paper cites Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning.

Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning

Reference 34

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Observation 6fb4bc0f-9e52-47f2-a422-9be46313878c · outbound

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Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids Heterogeneous-agent reinforcement learning

Reference 35

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This paper cites Guidelines for the Construction and Application of Industrial Green Microgrids (2026–2030).

Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids Guidelines for the Construction and Application of Industrial Green Microgrids (2026–2030)

Reference 36

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This paper cites Long-term energy management for microgrid with hybrid hydrogen-battery energy storage: A prediction-free coordinated optimization framework.

Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids Long-term energy management for microgrid with hybrid hydrogen-battery energy storage: A prediction-free coordinated optimization framework

Reference 37

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Observation b39750fe-c7e2-4062-8c00-f75a3b71a682 · outbound

This paper cites Data Miner 2: Real-Time Five Minute LMPs.

Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids Data Miner 2: Real-Time Five Minute LMPs

Reference 38

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