Paper Citation Record · LEDGER

The Economic Dispatch of Power-to-Gas Systems with Deep Reinforcement Learning:Tackling the Challenge of Delayed Rewards with Long-Term Energy Storage

As of 7 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2506.06484.

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

pith.paper-citation-record.v1
2506.06484 v1

Coverage vector

measured 32 of 32 reference resolution

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measured 32 of 32 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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A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

32 of 32 outbound references displayed

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External citation measurements

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Outbound references

Observation 0e624b34-e94d-4e70-ae75-77166058e838 · outbound

This paper cites Enhancing Battery Storage Energy Arbitrage With Deep Reinforcement Learning and Time-Series Forecasting.

The Economic Dispatch of Power-to-Gas Systems with Deep Reinforcement Learning:Tackling the Challenge of Delayed Rewards with Long-Term Energy Storage Enhancing Battery Storage Energy Arbitrage With Deep Reinforcement Learning and Time-Series Forecasting

Reference 1

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Observation 4c64a3ef-0963-4d7f-9a4e-754d752f4324 · outbound

This paper cites Deep reinforce- ment learning for economic battery dispatch: A compre- hensive comparison of algorithms and experiment design choices.

The Economic Dispatch of Power-to-Gas Systems with Deep Reinforcement Learning:Tackling the Challenge of Delayed Rewards with Long-Term Energy Storage Deep reinforce- ment learning for economic battery dispatch: A compre- hensive comparison of algorithms and experiment design choices

Reference 2

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Observation 9dc97af9-2c9e-41ef-9307-20cf0b99fb10 · outbound

This paper cites Deep-Reinforcement- Learning-Based Capacity Scheduling for PV-Battery Stor- age System.

The Economic Dispatch of Power-to-Gas Systems with Deep Reinforcement Learning:Tackling the Challenge of Delayed Rewards with Long-Term Energy Storage Deep-Reinforcement- Learning-Based Capacity Scheduling for PV-Battery Stor- age System

Reference 3

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Observation 547d1e26-fcb3-4f37-b4b3-6719e9176733 · outbound

This paper cites Deep Reinforcement Learning-Based Energy Storage Arbitrage With Accurate Lithium-Ion Battery Degradation Model.

The Economic Dispatch of Power-to-Gas Systems with Deep Reinforcement Learning:Tackling the Challenge of Delayed Rewards with Long-Term Energy Storage Deep Reinforcement Learning-Based Energy Storage Arbitrage With Accurate Lithium-Ion Battery Degradation Model

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This paper cites Data- driven battery operation for energy arbitrage using rainbow deep reinforcement learning.

The Economic Dispatch of Power-to-Gas Systems with Deep Reinforcement Learning:Tackling the Challenge of Delayed Rewards with Long-Term Energy Storage Data- driven battery operation for energy arbitrage using rainbow deep reinforcement learning

Reference 5

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Observation 501e2b0d-9eae-4446-88a8-d70d07fd04ba · outbound

This paper cites Dynamicen- ergy conversion and management strategy for an integrated electricity and natural gas system with renewable energy: Deep reinforcement learning approach.

The Economic Dispatch of Power-to-Gas Systems with Deep Reinforcement Learning:Tackling the Challenge of Delayed Rewards with Long-Term Energy Storage Dynamicen- ergy conversion and management strategy for an integrated electricity and natural gas system with renewable energy: Deep reinforcement learning approach

Reference 6

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Observation be40b532-3eb5-4de4-97cd-97ee57ba881e · outbound

This paper cites Inte- grated Electricity-Gas System Optimal Dispatch Based on Deep Reinforcement Learning.

The Economic Dispatch of Power-to-Gas Systems with Deep Reinforcement Learning:Tackling the Challenge of Delayed Rewards with Long-Term Energy Storage Inte- grated Electricity-Gas System Optimal Dispatch Based on Deep Reinforcement Learning

Reference 7

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This paper cites Dynamicoptimizationofanintegrateden- ergysystemwithcarboncaptureandpower-to-gasintercon- nection: A deep reinforcement learning-based scheduling strategy.

The Economic Dispatch of Power-to-Gas Systems with Deep Reinforcement Learning:Tackling the Challenge of Delayed Rewards with Long-Term Energy Storage Dynamicoptimizationofanintegrateden- ergysystemwithcarboncaptureandpower-to-gasintercon- nection: A deep reinforcement learning-based scheduling strategy

Reference 8

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Observation 5c67bdb3-df0e-4848-85e1-4f34fadae142 · outbound

This paper cites Stochastic coordinated operation of wind and battery energy storage system considering bat- tery degradation.

The Economic Dispatch of Power-to-Gas Systems with Deep Reinforcement Learning:Tackling the Challenge of Delayed Rewards with Long-Term Energy Storage Stochastic coordinated operation of wind and battery energy storage system considering bat- tery degradation

Reference 9

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Observation 10cd732e-d107-4ecc-ae41-6535c5b2dd17 · outbound

This paper cites OptimalBiddingStrategyofBattery Storage in Power Markets Considering Performance-Based Regulation and Battery Cycle Life.

The Economic Dispatch of Power-to-Gas Systems with Deep Reinforcement Learning:Tackling the Challenge of Delayed Rewards with Long-Term Energy Storage OptimalBiddingStrategyofBattery Storage in Power Markets Considering Performance-Based Regulation and Battery Cycle Life

Reference 10

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Observation 816dfa36-ab19-47da-a08b-20112a3c9971 · outbound

This paper cites Optimal Economic Gas Turbine Dis- patch with Deep Reinforcement Learning.

The Economic Dispatch of Power-to-Gas Systems with Deep Reinforcement Learning:Tackling the Challenge of Delayed Rewards with Long-Term Energy Storage Optimal Economic Gas Turbine Dis- patch with Deep Reinforcement Learning

Reference 11

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Observation 62bb98b7-4f73-4ac1-a955-4fb485f81cfe · outbound

This paper cites DeepReinforcementLearningforJointDispatchofBattery Energy Storage Systems and Gas Turbines in Microgrids withRenewableEnergy.

The Economic Dispatch of Power-to-Gas Systems with Deep Reinforcement Learning:Tackling the Challenge of Delayed Rewards with Long-Term Energy Storage DeepReinforcementLearningforJointDispatchofBattery Energy Storage Systems and Gas Turbines in Microgrids withRenewableEnergy

Reference 12

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This paper cites MIT press (2018).

The Economic Dispatch of Power-to-Gas Systems with Deep Reinforcement Learning:Tackling the Challenge of Delayed Rewards with Long-Term Energy Storage MIT press (2018)

Reference 13

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Observation b1bd297c-7835-46d2-93ee-ffe351095506 · outbound

This paper cites Human-level control through deep reinforce- ment learning.

The Economic Dispatch of Power-to-Gas Systems with Deep Reinforcement Learning:Tackling the Challenge of Delayed Rewards with Long-Term Energy Storage Human-level control through deep reinforce- ment learning

Reference 14

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This paper cites Proximal Policy Optimization Algorithms.

The Economic Dispatch of Power-to-Gas Systems with Deep Reinforcement Learning:Tackling the Challenge of Delayed Rewards with Long-Term Energy Storage Proximal Policy Optimization Algorithms

Reference 15

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Observation b52ca242-5bd7-48d3-b8b5-73ed44e60d65 · outbound

This paper cites Market and sys- tem reporting.

The Economic Dispatch of Power-to-Gas Systems with Deep Reinforcement Learning:Tackling the Challenge of Delayed Rewards with Long-Term Energy Storage Market and sys- tem reporting

Reference 16

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This paper cites Using bias-corrected reanalysis to simulate current and future wind power out- put.

The Economic Dispatch of Power-to-Gas Systems with Deep Reinforcement Learning:Tackling the Challenge of Delayed Rewards with Long-Term Energy Storage Using bias-corrected reanalysis to simulate current and future wind power out- put

Reference 17

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This paper cites Hydrogen and Power-to-X solutions - Elyzer P-300 - Technical Data.

The Economic Dispatch of Power-to-Gas Systems with Deep Reinforcement Learning:Tackling the Challenge of Delayed Rewards with Long-Term Energy Storage Hydrogen and Power-to-X solutions - Elyzer P-300 - Technical Data

Reference 18

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Observation 1a33ad73-3c4f-4c79-ba27-675388c3c7b5 · outbound

This paper cites Power-to- Methane: A state-of-the-art review.

The Economic Dispatch of Power-to-Gas Systems with Deep Reinforcement Learning:Tackling the Challenge of Delayed Rewards with Long-Term Energy Storage Power-to- Methane: A state-of-the-art review

Reference 19

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This paper cites Techno- economic evaluation of a power-to-methane plant : Lev- elized cost of methane, financial performance met- rics, and sensitivity analysis.

The Economic Dispatch of Power-to-Gas Systems with Deep Reinforcement Learning:Tackling the Challenge of Delayed Rewards with Long-Term Energy Storage Techno- economic evaluation of a power-to-methane plant : Lev- elized cost of methane, financial performance met- rics, and sensitivity analysis

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This paper cites Putting CO2 to Use.

The Economic Dispatch of Power-to-Gas Systems with Deep Reinforcement Learning:Tackling the Challenge of Delayed Rewards with Long-Term Energy Storage Putting CO2 to Use

Reference 21

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Observation f03176e2-ed88-4b1c-8b2d-272d109d4eed · outbound

This paper cites Gymnasium: A Standard Interface for Reinforcement Learning Environments.

The Economic Dispatch of Power-to-Gas Systems with Deep Reinforcement Learning:Tackling the Challenge of Delayed Rewards with Long-Term Energy Storage Gymnasium: A Standard Interface for Reinforcement Learning Environments

Reference 22

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This paper cites Stable- Baselines3: Reliable Reinforcement Learning Implemen- tations.

The Economic Dispatch of Power-to-Gas Systems with Deep Reinforcement Learning:Tackling the Challenge of Delayed Rewards with Long-Term Energy Storage Stable- Baselines3: Reliable Reinforcement Learning Implemen- tations

Reference 23

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The Economic Dispatch of Power-to-Gas Systems with Deep Reinforcement Learning:Tackling the Challenge of Delayed Rewards with Long-Term Energy Storage Optuna: Anext-generation hyperparameter optimization framework

Reference 24

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This paper cites Learning Tetris Us- ing the Noisy Cross-Entropy Method.

The Economic Dispatch of Power-to-Gas Systems with Deep Reinforcement Learning:Tackling the Challenge of Delayed Rewards with Long-Term Energy Storage Learning Tetris Us- ing the Noisy Cross-Entropy Method

Reference 25

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The Economic Dispatch of Power-to-Gas Systems with Deep Reinforcement Learning:Tackling the Challenge of Delayed Rewards with Long-Term Energy Storage Gurobi Optimizer Reference Manual

Reference 26

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This paper cites EnergyMan- agement for Lifetime Extension of Energy Storage Sys- tem in Micro-Grid Applications.

The Economic Dispatch of Power-to-Gas Systems with Deep Reinforcement Learning:Tackling the Challenge of Delayed Rewards with Long-Term Energy Storage EnergyMan- agement for Lifetime Extension of Energy Storage Sys- tem in Micro-Grid Applications

Reference 27

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This paper cites A PSO- Optimized Fuzzy Logic Control-Based Charging Method for Individual Household Battery Storage Systems within a Community.

The Economic Dispatch of Power-to-Gas Systems with Deep Reinforcement Learning:Tackling the Challenge of Delayed Rewards with Long-Term Energy Storage A PSO- Optimized Fuzzy Logic Control-Based Charging Method for Individual Household Battery Storage Systems within a Community

Reference 28

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This paper cites Cost Projections for Utility-Scale Battery Storage: 2023 Update.

The Economic Dispatch of Power-to-Gas Systems with Deep Reinforcement Learning:Tackling the Challenge of Delayed Rewards with Long-Term Energy Storage Cost Projections for Utility-Scale Battery Storage: 2023 Update

Reference 29

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The Economic Dispatch of Power-to-Gas Systems with Deep Reinforcement Learning:Tackling the Challenge of Delayed Rewards with Long-Term Energy Storage Aeroderivative gas turbines

Reference 30

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This paper cites Capital Cost and Performance Characteristic Estimates for Utility Scale Electric Power Generating Technolo- gies.

The Economic Dispatch of Power-to-Gas Systems with Deep Reinforcement Learning:Tackling the Challenge of Delayed Rewards with Long-Term Energy Storage Capital Cost and Performance Characteristic Estimates for Utility Scale Electric Power Generating Technolo- gies

Reference 31

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The Economic Dispatch of Power-to-Gas Systems with Deep Reinforcement Learning:Tackling the Challenge of Delayed Rewards with Long-Term Energy Storage Unresolved cited work

Reference 2021

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