Machine Learning Based Hydrogen Electrolyzer Control Strategy for Solar Power Output and Battery State of Charge Regulation

Syed, Miswar Akhtar; Khalid, Muhammad

doi:10.1109/isgteurope52324.2021.9640182

Cited by 4 publications

(8 citation statements)

References 16 publications

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“…The sum of the P B and the wind-PV power, P SW , is the amount of power that is sent to the grid. The power output characteristics of the electrolyzer and fuel cell system could be approximated using the first order transfer functions [11,12]. Thus, the HE and FC can be stated numerically as follows:…”

Section: Microgrid With Hydrogen Elctrolyzer Fuel Cell Systemmentioning

confidence: 99%

“…Among the numerous ESS are Superconducting Magnetic Energy Storage, Flywheel Energy Storage [8], Compressed Air, and Battery Energy Storage System (BESS) [9]. However, because of their cost or capacity [10], many of these devices are not appropriate for wind-solar generation [11,12]. Some of the issues connected with deploying ESS in wind and solar energy systems include ramp rates [13], transmission losses, and limiting the power output of wind-solar farms [4].…”

Section: Introductionmentioning

confidence: 99%

“…Some of the issues connected with deploying ESS in wind and solar energy systems include ramp rates [13], transmission losses, and limiting the power output of wind-solar farms [4]. The Battery Energy Storage System [11,12] is the energy storage system that works best with wind-solar power generation as it has many advantages, particularly its ease of implementation and modest needed installation space, the BESS is currently the most frequently utilized ESS to alleviate power fluctuation [14]. Nonetheless, because the BESS should have a large capacity to sustain the instability, the adoption of BESS in wind-solar power systems has certain obstacles.…”

Section: Introductionmentioning

confidence: 99%

“…Because larger batteries are costly, incorporating BESS into the framework will lead to an increase in capital costs as well as maintenance overhead [8,15]. As a result, a control strategy is essential for power smoothing, optimizing BESS use, and eventually reducing BESS capacity and investment [11,12,16]. Because of its ease of implementation and suitability for real-time operation, output power firming is a popular approach of managing BESS [17].…”

Section: Introductionmentioning

confidence: 99%

“…Due to its adaptability, Model Predictive Control (MPC) is also utilized to regulate the BESS charging levels, authors in [11,12] created a control system that uses MPC in conjunction with a battery energy storage system to stabilize the solar power output and assure optimum battery performance. Closely, [43] presented an MPC technique to smooth and distribute wind power with BESS.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

An Intelligent Model Predictive Control Strategy for Stable Solar-Wind Renewable Power Dispatch Coupled with Hydrogen Electrolyzer and Battery Energy Storage

Syed

Khalid

2023

International Journal of Energy Research

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Renewable energy sources such as photovoltaic (PV) and wind power are widely used; however, their intermittent nature impairs power supply quality by creating frequency distortions and irregularities in voltage. Battery energy storage systems (BESS) are utilized to flatten out and relieve fluctuation issues. To prevent the need for larger storage systems and to prolong their operational life through controlled charging and discharging, a method of control for BESS charging level regulation is necessary. This study presents a solar-wind power and battery state of charge (SoC) control technique using a hydrogen electrolyzer (HE) fuel cell unit. An Intelligent Model Predictive Controller (IMPC) has been developed that utilizes a neural network (NN) plant model for predictive minimization instead of using a mathematical plant model for dynamic management of the HE output, which allows for flattened PV-wind power supply with regulated BESS charging and discharging. The IMPC accepts as inputs the intermittent renewable power and different battery attributes and intelligently manages the HE production while staying within the imposed restrictions. The NN, as opposed to a mathematical plant model, captures the dynamics of the plant with exceptionally high accuracy. Moreover, the NN plant model performance increases as the data gathered from the actual system increases. The neural network model resolves concerns with the MPC model’s mathematical intricacy that occurs as the plant becomes more complicated. According to simulation results, the IMPC greatly reduces PV-wind power fluctuations, for solar power, the IMPC reduces the peak battery SoC by 26.7% and compared to FLC, the peak SoC is reduced by 11.2%. Similarly, for wind power, the peak SoC is reduced by 7.3% and is decreased 3.2% more than the FLC. To demonstrate the power smoothing effectiveness of the IMPC, the peak solar power ramp rate is reduced by 30.3% and the peak wind power ramp rate is reduced by 91.3%. The presented method encourages green hydrogen usage in the electrical sector for supplying firmed solar and wind power.

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Section: Microgrid With Hydrogen Elctrolyzer Fuel Cell Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

An Intelligent Model Predictive Control Strategy for Stable Solar-Wind Renewable Power Dispatch Coupled with Hydrogen Electrolyzer and Battery Energy Storage

Syed

Khalid

2023

International Journal of Energy Research

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Accurate parameter identification of proton exchange membrane fuel cell models using different metaheuristic optimization algorithms

Sultan,

Menesy,

Alqahtani

et al. 2023

Energy Reports

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Review and outlook on reinforcement learning: Its application in agricultural energy internet

Fu,

Zhang,

Bai

et al. 2024

IET Renewable Power Gen

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Agricultural Energy Internet (AEI), representing a key evolutionary direction in the integrated energy landscape of rural regions, holds a vital position in advancing the electrification of agricultural sectors. However, the disjointed control between agricultural loads and grid operations hinders the collaborative development of agriculture and energy. Addressing these issues, this paper investigates the current applications of artificial intelligence in the fields of agriculture and energy. The authors examine the evolutionary path of AEI, particularly emphasizing the critical technologies emerging from the intersection of agriculture, energy, and digital networks. Furthermore, the authors examine the critical technologies of reinforcement learning in the context of smart grid applications. In response to the challenges posed by low energy efficiency in rural areas, a reinforcement learning framework is proposed for coordinating fisheries, agriculture, livestock farming, and rural distribution networks. This framework provides a clear pathway for the application of reinforcement learning in AEI. This research acts as a conduit, merging agricultural and energy domains to promote a cohesive progression that markedly aids in the enhancement of rural electrification and the adoption of sustainable energy methodologies through reinforcement learning.

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Machine Learning Based Hydrogen Electrolyzer Control Strategy for Solar Power Output and Battery State of Charge Regulation

Cited by 4 publications

References 16 publications

An Intelligent Model Predictive Control Strategy for Stable Solar-Wind Renewable Power Dispatch Coupled with Hydrogen Electrolyzer and Battery Energy Storage

An Intelligent Model Predictive Control Strategy for Stable Solar-Wind Renewable Power Dispatch Coupled with Hydrogen Electrolyzer and Battery Energy Storage

Accurate parameter identification of proton exchange membrane fuel cell models using different metaheuristic optimization algorithms

Review and outlook on reinforcement learning: Its application in agricultural energy internet

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