Optimal Operation Control of PV-Biomass Gasifier-Diesel-Hybrid Systems Using Reinforcement Learning Techniques

Козлов, А. Г.; Tomin, Nikita; Sidorov, Denis; Lora, Electo Eduardo Silva; Kurbatsky, Victor

doi:10.3390/en13102632

Cited by 17 publications

(17 citation statements)

References 68 publications

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“…RL aims to learn how to maximize the rewards for the agent based on the rewards or penalties of its actions in predefined environment states [129]. Different studies implement RL to optimize the schedule of DGs or DSs based on their predicted product, energy price, and load demand [128], [130], [129], [131]- [133]. FLC is another intelligent control technique which emulates human decision making [134].…”

Section: A Power-sharingmentioning

confidence: 99%

Recent Contributions, Future Prospects and Limitations of Interlinking Converter Control in Hybrid AC/DC Microgrids

et al. 2021

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This work analyzes interlinking converter control in hybrid AC/DC microgrids. The paper addresses the state-of-the-art general hybrid microgrid structure. The key power electronics topologies are used as bidirectional interface converters in the AC and DC parts. Different control structures of hybrid microgrids are categorized, followed by the classification of the main control functions, their control strategies, and the control techniques and a summary of their positive and negative aspects and applications. Control functions, their strategies and techniques are classified in the interlinking-converter based. Finally, overall control objectives, time-scaled control structures, and their strategies are outlined. The prospects, main challenges, research gaps, and the trend of the hybrid microgrid structure and control are reviewed and summarized in the conclusions.

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Section: A Power-sharingmentioning

confidence: 99%

Recent Contributions, Future Prospects and Limitations of Interlinking Converter Control in Hybrid AC/DC Microgrids

et al. 2021

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“…Learning the policy could even improve controlling the efficiency in the short-term [12]. Existing works focus on non-islanded settings [13] where no state causes a failure. Since our building is partially islanded, this approach would yield to a failure where the islanded portion is not powered anymore.…”

Section: Minimizing Carbon Impactmentioning

confidence: 99%

“…In this section, we reformulate our problem (13) to simplify its resolution. We consider in particular the reduction of the action space to reduce the complexity and improve the convergence of learning.…”

Section: Problem Reformulationsmentioning

confidence: 99%

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Deep Reinforcement Learning for Hybrid Energy Storage Systems: Balancing Lead and Hydrogen Storage

2021

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We address the control of a hybrid energy storage system composed of a lead battery and hydrogen storage. Powered by photovoltaic panels, it feeds a partially islanded building. We aim to minimize building carbon emissions over a long-term period while ensuring that 35% of the building consumption is powered using energy produced on site. To achieve this long-term goal, we propose to learn a control policy as a function of the building and of the storage state using a Deep Reinforcement Learning approach. We reformulate the problem to reduce the action space dimension to one. This highly improves the proposed approach performance. Given the reformulation, we propose a new algorithm, DDPGαrep, using a Deep Deterministic Policy Gradient (DDPG) to learn the policy. Once learned, the storage control is performed using this policy. Simulations show that the higher the hydrogen storage efficiency, the more effective the learning.

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“…Several previous works show simulations and experimental results about the control of other similar processes such as combustion [2] [3] [4] [5]. On the other hand, some publications show the results based on simulation about the control of the biomass gasification process, mainly applied to a downdraft gasifier [6] [7] [8] [9] [10]. The novelty of this paper resides on the design of a control system for a BFB gasification plant to improve the gasification process, which has been validated in field tests that have demonstrated an increment in efficiency and safety.…”

Section: Introductionmentioning

confidence: 99%

Experimental analysis of a bubbling fluidized bed gasification plant fed by biomass: Design, implementation and validation of the control system

2021

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Despite being a renewable source, biomass as fuel for power generation is still not completely exploited. In biomass gasification plants, control operations are crucial for the proper management of the plant. This paper describes the results of a regulation control applied to an experimental biomass bubbling fluidized bed (BFB) gasification plant. The aim of implementing the system is to improve the biomass gasification process, increasing the efficiency and ensuring the safety in the plant operation. The equivalence ratio (ER) is one of the main parameters in a gasification process. To improve the ER, the airflow input is controlled, measuring the air velocity through an anemometer. On the other hand, the biomass flow is controlled modifying the speed of the screw conveyor using an inverter for regulating the frequency of its electric motor. A PLC is used for programming the instructions to implement control functions and to store the data given by the measurement devices. Once implemented the control system, the biomass gasification plant could work either; manually o automatically, allowing to adjust ER, increasing efficiency of the process. Finally, some tests are done to validate the control system, using the acquired data to improve the process.

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Optimal Operation Control of PV-Biomass Gasifier-Diesel-Hybrid Systems Using Reinforcement Learning Techniques

Cited by 17 publications

References 68 publications

Recent Contributions, Future Prospects and Limitations of Interlinking Converter Control in Hybrid AC/DC Microgrids

Recent Contributions, Future Prospects and Limitations of Interlinking Converter Control in Hybrid AC/DC Microgrids

Deep Reinforcement Learning for Hybrid Energy Storage Systems: Balancing Lead and Hydrogen Storage

Experimental analysis of a bubbling fluidized bed gasification plant fed by biomass: Design, implementation and validation of the control system

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