An Integrated Generation-Compensation optimization Strategy for Enhanced Short-Term Voltage Security of Large-Scale Power Systems Using Multi-Objective Reinforcement Learning Method

Deng, Zhuoming; Liu, Mingbo

doi:10.1109/powercon.2018.8601814

Cited by 4 publications

(5 citation statements)

References 13 publications

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“…In addition to the motivating examples discussed above, recent years have seen multiobjective learning and planning methods applied across a wide range of problem domains including: distributed computing [27,124], drug and molecule design [62,214], cybersecurity [162], simulation [132], job shop scheduling [98], cognitive radio networks [100,129], satellite communications [45,63], recommender systems [78], power systems [34,35,97,193], building management [213], traffic management [70], manufacturing [36,54,80], bidding and pricing [76,207], education [151], and robotics [64]. The scope and variety of these applications supports our assertion that many important problems involve multiple objectives, and are best addressed using explicitly multi-objective methods.…”

Section: Other Topicsmentioning

confidence: 99%

A practical guide to multi-objective reinforcement learning and planning

Hayes

Rădulescu

Bargiacchi

et al. 2022

Auton Agent Multi-Agent Syst

126

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Real-world sequential decision-making tasks are generally complex, requiring trade-offs between multiple, often conflicting, objectives. Despite this, the majority of research in reinforcement learning and decision-theoretic planning either assumes only a single objective, or that multiple objectives can be adequately handled via a simple linear combination. Such approaches may oversimplify the underlying problem and hence produce suboptimal results. This paper serves as a guide to the application of multi-objective methods to difficult problems, and is aimed at researchers who are already familiar with single-objective reinforcement learning and planning methods who wish to adopt a multi-objective perspective on their research, as well as practitioners who encounter multi-objective decision problems in practice. It identifies the factors that may influence the nature of the desired solution, and illustrates by example how these influence the design of multi-objective decision-making systems for complex problems.

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Section: Other Topicsmentioning

confidence: 99%

A practical guide to multi-objective reinforcement learning and planning

Hayes

Rădulescu

Bargiacchi

et al. 2022

Auton Agent Multi-Agent Syst

126

View full text Add to dashboard Cite

show abstract

“…In addition to the motivating examples discussed above, recent years have seen multi-objective learning and planning methods applied across a wide range of problem domains including: distributed computing [Qin et al, 2020, da Silva Veith et al, 2019, drug and molecule design [Zhou et al, 2019, Horwood andNoutahi, 2020], cybersecurity [Sun et al, 2018], simulation [Ravichandran et al, 2018], job shop scheduling [Méndez-Hernández et al, 2019], cognitive radio networks [Messikh andZarour, 2018, Raj et al, 2020], satellite communications [Hu et al, 2020, Ferreira et al, 2019, recommender systems [Lacerda, 2017], power systems [Deng and Liu, 2018, Deng et al, 2020, Wang et al, 2019, Mello et al, 2020, building management , traffic management [Jin and Ma, 2019], manufacturing [Govindaiah and Petty, 2019, Lepenioti et al, 2020, Dornheim and Link, 2018, bidding and pricing [Yang et al, 2020, Krasheninnikova et al, 2019, education [Rowe et al, 2018], and robotics [Huang et al, 2019]. The scope and variety of these applications supports our assertion that many important problems involve multiple objectives, and are best addressed using explicitly multi-objective methods.…”

Section: Other Topicsmentioning

confidence: 99%

A Practical Guide to Multi-Objective Reinforcement Learning and Planning

Hayes,

Rădulescu,

Bargiacchi

et al. 2021

Preprint

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Real-world decision-making tasks are generally complex, requiring trade-offs between multiple, often conflicting, objectives. Despite this, the majority of research in reinforcement learning and decision-theoretic planning either assumes only a single objective, or that multiple objectives can be adequately handled via a simple linear combination. Such approaches may oversimplify the underlying problem and hence produce suboptimal results. This paper serves as a guide to the application of multi-objective methods to difficult problems, and is aimed at researchers who are already familiar with single-objective reinforcement learning and planning methods who wish to adopt a multi-objective perspective on their research, as well as practitioners who encounter multiobjective decision problems in practice. It identifies the factors that may influence the nature of the desired solution, and illustrates by example how these influence the design of multi-objective decision-making systems for complex problems.

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“…The first approach consists in using machine learning to predict the preventive control scheme. For instance, in [114], the authors propose to use multi-objective reinforcement learning for short-term voltage stability, in order to minimise both voltage deviation and control action cost.…”

Section: Learning a Modelmentioning

confidence: 99%

Recent Developments in Machine Learning for Energy Systems Reliability Management

2020

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This paper reviews recent works applying machine learning techniques in the context of energy systems reliability assessment and control. We showcase both the progress achieved to date as well as the important future directions for further research, while providing an adequate background in the fields of reliability management and of machine learning. The objective is to foster the synergy between these two fields and speed up the practical adoption of machine learning techniques for energy systems reliability management. We focus on bulk electric power systems and use them as an example, but we argue that the methods, tools, etc. can be extended to other similar systems, such as distribution systems, micro-grids, and multi-energy systems.

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An Integrated Generation-Compensation optimization Strategy for Enhanced Short-Term Voltage Security of Large-Scale Power Systems Using Multi-Objective Reinforcement Learning Method

Cited by 4 publications

References 13 publications

A practical guide to multi-objective reinforcement learning and planning

A practical guide to multi-objective reinforcement learning and planning

A Practical Guide to Multi-Objective Reinforcement Learning and Planning

Recent Developments in Machine Learning for Energy Systems Reliability Management

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