A Holistic Power Optimization Approach for Microgrid Control Based on Deep Reinforcement Learning Article Swipe
Fulong Yao
,
Wanqing Zhao
,
Matthew Forshaw
,
Yang Song
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2403.01013
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2403.01013
The global energy landscape is undergoing a transformation towards decarbonization, sustainability, and cost-efficiency. In this transition, microgrid systems integrated with renewable energy sources (RES) and energy storage systems (ESS) have emerged as a crucial component. However, optimizing the operational control of such an integrated energy system lacks a holistic view of multiple environmental, infrastructural and economic considerations, not to mention the need to factor in the uncertainties from both the supply and demand. This paper presents a holistic datadriven power optimization approach based on deep reinforcement learning (DRL) for microgrid control considering the multiple needs of decarbonization, sustainability and cost-efficiency. First, two data-driven control schemes, namely the prediction-based (PB) and prediction-free (PF) schemes, are devised to formulate the control problem within a Markov decision process (MDP). Second, a multivariate objective (reward) function is designed to account for the market profits, carbon emissions, peak load, and battery degradation of the microgrid system. Third, we develop a Double Dueling Deep Q Network (D3QN) architecture to optimize the power flows for real-time energy management and determine charging/discharging strategies of ESS. Finally, extensive simulations are conducted to demonstrate the effectiveness and superiority of the proposed approach through a comparative analysis. The results and analysis also suggest the respective circumstances for using the two control schemes in practical implementations with uncertainties.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2403.01013
- https://arxiv.org/pdf/2403.01013
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4392490391
All OpenAlex metadata
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https://openalex.org/W4392490391Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2403.01013Digital Object Identifier
- Title
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A Holistic Power Optimization Approach for Microgrid Control Based on Deep Reinforcement LearningWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2024Year of publication
- Publication date
-
2024-03-01Full publication date if available
- Authors
-
Fulong Yao, Wanqing Zhao, Matthew Forshaw, Yang SongList of authors in order
- Landing page
-
https://arxiv.org/abs/2403.01013Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2403.01013Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2403.01013Direct OA link when available
- Concepts
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Reinforcement learning, Microgrid, Control (management), Computer science, Power (physics), Reinforcement, Artificial intelligence, Psychology, Social psychology, Physics, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.prediction-free | 110 |
| abstract_inverted_index.sustainability, | 10 |
| abstract_inverted_index.cost-efficiency. | 12, 99 |
| abstract_inverted_index.decarbonization, | 9, 96 |
| abstract_inverted_index.prediction-based | 107 |
| abstract_inverted_index.charging/discharging | 173 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile |