Deep reinforcement learning approach for MPPT control of partially shaded PV systems in Smart Grids

Ávila, Luis; Paula, Mariano De; Trimboli, Maximiliano; Carlucho, Ignacio

doi:10.1016/j.asoc.2020.106711

Cited by 61 publications

(21 citation statements)

References 42 publications

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“…A range of benchmark environments implementing the OpenAI interface are available for video games [180]. Similar benchmarks are not available for the energy domain, although a few works in the energy domain implement the OpenAI interface for the following applications: maximum power point tracking of PV installations [181], building energy management [25], [26], [27], microgrid energy management [142], demand response for building cooling [182]. Building on such works, the emergence of a range of open-source benchmark environments for diverse battery applications could greatly speed up the research on RL applications for battery management and improve the possibilities to comparatively assess similar works and identify the superior RL designs.…”

Section: Discussionmentioning

confidence: 99%

Exploiting Battery Storages With Reinforcement Learning: A Review for Energy Professionals

2022

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The transition to renewable production and smart grids is driving a massive investment to battery storages, and reinforcement learning (RL) has recently emerged as a potentially disruptive technology for their control and optimization of battery storage systems. A surge of papers has appeared in the last two years applying reinforcement learning to the optimization of battery storages in buildings, energy communities, energy harvesting Internet of Things networks, renewable generation, microgrids, electric vehicles and plug-in hybrid electric vehicles. This article reviews these applications through 4 different perspectives. Firstly, the type of optimization problem is analyzed; the literature can be divided to approaches that optimize either financial targets or energy efficiency. Secondly, the approaches for handling user comfort are analyzed for applications that may impact a human user. Thirdly, this paper discusses the approach to model and reduce battery degradation. Fourthly, the articles are categorized by application context and applications likely to attract a high amount of research are identified. The paper concludes with a list of unresolved challenges.

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Section: Discussionmentioning

confidence: 99%

Exploiting Battery Storages With Reinforcement Learning: A Review for Energy Professionals

2022

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“…Black horizontal line is the nominal frequency and the battery idle state. The battery simulation model was wrapped in custom Python code that implements an interface similar to the environments in the OpenAI Gym collection [52], which has been used in several recent publications on RL applications in the energy domain [53][54][55][56][57][58]. This interface defines the functions reset(S[0]) and step(a).…”

Section: Implementation 41 Enviromentmentioning

confidence: 99%

A Simulation Environment for Training a Reinforcement Learning Agent Trading a Battery Storage

et al. 2021

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Battery storages are an essential element of the emerging smart grid. Compared to other distributed intelligent energy resources, batteries have the advantage of being able to rapidly react to events such as renewable generation fluctuations or grid disturbances. There is a lack of research on ways to profitably exploit this ability. Any solution needs to consider rapid electrical phenomena as well as the much slower dynamics of relevant electricity markets. Reinforcement learning is a branch of artificial intelligence that has shown promise in optimizing complex problems involving uncertainty. This article applies reinforcement learning to the problem of trading batteries. The problem involves two timescales, both of which are important for profitability. Firstly, trading the battery capacity must occur on the timescale of the chosen electricity markets. Secondly, the real-time operation of the battery must ensure that no financial penalties are incurred from failing to meet the technical specification. The trading-related decisions must be done under uncertainties, such as unknown future market prices and unpredictable power grid disturbances. In this article, a simulation model of a battery system is proposed as the environment to train a reinforcement learning agent to make such decisions. The system is demonstrated with an application of the battery to Finnish primary frequency reserve markets.

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“…The performance of the proposed algorithm is evaluated under various conditions. One may observe from the algorithm results that the RL-based MPPT algorithm follows MPP with a deviation of less than 1% (Avila et al, 2020). Irmak and Güler also propose a new algorithm by combining the MPPT methods P&O and Model Predictive Control (MPC) to increase the dynamic performance of the boost converter used in PV applications.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network based MPPT Algorithm with Boost Converter topology for Stand-Alone PV System

Yılmaz

Corapsiz

2022

Erzincan Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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The increasing energy need in parallel with the technology development and the depletion of the resources have increased the importance of alternative energy resources. Solar energy systems are frequently preferred due to their advantages such as not having moving parts, being reliable and working without noise. Production of electricity from solar energy is obtained by serial or parallel connection of photovoltaic (PV) panels, depending on the desired voltage and current values. DC-DC converters are used to convert the energy obtained from the PV panels to the desired grid values. Maximum power point tracking (MPPT) algorithms are used in order to obtain the highest possible efficiency from the PV panels. MPPT algorithms control the duty period (D) ratio of DC-DC converters and obtain maximum energy. In this study, an Artificial Neural Network (ANN) based MPPT algorithm is proposed. Firstly, the temperature and irradiance data at the PV panel input are trained using the Levenberg-Marquardt algorithm. As a result, a reference voltage is generated and MPPT is realized by comparing it with the voltage produced by the PV panel. In order to evaluate the performance of the proposed algorithm, it is compared with the traditional MPPT methods such as Perturb & Observe (P&O) and Incremental Conductance (INC). As a result of the simulation studies, it has been observed that ANN based MPPT is more successful than P&O and INC algorithms for several irradiance and temperature conditions.

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Deep reinforcement learning approach for MPPT control of partially shaded PV systems in Smart Grids

Cited by 61 publications

References 42 publications

Exploiting Battery Storages With Reinforcement Learning: A Review for Energy Professionals

Exploiting Battery Storages With Reinforcement Learning: A Review for Energy Professionals

A Simulation Environment for Training a Reinforcement Learning Agent Trading a Battery Storage

Artificial Neural Network based MPPT Algorithm with Boost Converter topology for Stand-Alone PV System

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