Optimal Algorithms for Energy Storage Systems in Microgrid Applications: An Analytical Evaluation Towards Future Directions

Reza, Md. Selim; Rahman, Navila; Wali, Safat B.; Hannan, M. A.; Ker, Pin Jern; Rahman, S A; Muttaqi, Kashem M.

doi:10.1109/access.2022.3144930

Cited by 25 publications

(3 citation statements)

References 144 publications

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“…Adopting a comprehensive energy storage policy to balance the grid, reduce costs, and improve reliability would be significant for both renewable and traditional network systems. Other factors to consider in HESSs include how they charge and discharge (reaction time), as well as their capacity, efficiency, energy and power density, life span, and level of corrosiveness [47][48][49].…”

Section: Literature Reviewmentioning

confidence: 99%

Integration of Superconducting Magnetic Energy Storage for Fast-Response Storage in a Hybrid Solar PV-Biogas with Pumped-Hydro Energy Storage Power Plant

et al. 2023

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Electric distribution systems face many issues, such as power outages, high power losses, voltage sags, and low voltage stability, which are caused by the intermittent nature of renewable power generation and the large changes in load demand. To deal with these issues, a distribution system has been designed using both short- and long-term energy storage systems such as superconducting magnetic energy storage (SMES) and pumped-hydro energy storage (PHES). The aim of this paper is to propose a metaheuristic-based optimization method to find the optimal size of a hybrid solar PV-biogas generator with SMES-PHES in the distribution system and conduct a financial analysis. This method is based on an efficient algorithm called the “enhanced whale optimization” algorithm (EWOA), along with the proposed objective functions and constraints of the system. The EWOA is employed to reduce the hybrid system’s life cycle cost (LCC) and improve its reliability, both of which serve as performance indicators for the distribution system. The proposed method for sizing a grid-connected hybrid solar PV-biogas generator with SMES-PHES is compared with other metaheuristic optimization techniques, including the African vulture optimization algorithm (AVOA), grey wolf optimization algorithm (GWO), and water cycle algorithm (WCA). The numerical results of the EWOA show that the combination of a hybrid solar PV-biogas generator with SMES-PHES can successfully reduce the LCC and increase reliability, making the distribution system work better.

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Section: Literature Reviewmentioning

confidence: 99%

Integration of Superconducting Magnetic Energy Storage for Fast-Response Storage in a Hybrid Solar PV-Biogas with Pumped-Hydro Energy Storage Power Plant

et al. 2023

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“…At the distribution layer, the distribution system operator first evaluates the operational boundaries of the electric vehicle aggregator to develop distribution layer services. Alireza et al (2024) presented a methodology for obtaining maximum and minimum daily load profiles, introduced new hourly and daily system flexibility metrics, and developed a generalized methodology for quantifying and formulating system flexibility, i.e., the possible increase or decrease in power within operating constraints. All of the above studies consider the optimal allocation of flexibility resources from a single aspect such as power supply and energy storage.…”

Section: Introductionmentioning

confidence: 99%

Multi-timescale available flexibility assessment of integrated energy systems considering different proportions of wind power installations

Zhang,

2024

Front. Energy Res.

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The available flexibility capacity of the integrated energy system can be used as one of the indicators of proportions of system wind power installations, which, in turn, affects the maximum installed capacity of the system wind power, and this paper proposes a method for assessing the available flexibility of the integrated energy system at multiple timescales considering different proportions of system wind power installations. First, the framework of the integrated energy system is constructed, and based on the coupling relationship between the electrical and thermal systems, the mathematical models of the P2G, combined heat and power (CHP), energy storage equipment, and wind power generation equipment within the integrated energy system are established, and the Monte Carlo method is used to predict the wind power output in a typical scenario. Second, an integrated energy system optimization model is constructed to obtain the optimal dispatch operation of the system; the empirical mode decomposition (EMD) algorithm is used to decompose the flexibility demand curve of the system in multiple timescales. The flexibility supply capacity model of different types of flexibility resources in the system at different timescales is established, and through the comparative analysis of flexibility supply and demand at the same timescale, the upward and downward flexibility shortage probability and shortage expectation indexes at each timescale can be intuitively calculated and then weighted to constitute a comprehensive index of system flexibility assessment. Finally, the available flexibility analysis of the integrated energy system under different installed wind power capacities shows that the proposed methodology can more comprehensively assess the available flexibility capacity of the integrated energy system under different timescales, and the maximum installed wind power capacity that the system can withstand can be obtained while guaranteeing sufficient available flexibility capacity.

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“…Microgrid is one of the trending topics in energy due to the increased demand for energy systems that have reliability and sustainability 1 . Microgrid models can efficiently generate energy in small-scale and localized power systems while maintaining the storage and control of the loads 2 .…”

Section: Introductionmentioning

confidence: 99%

Exploring the efficacy of GRU model in classifying the signal to noise ratio of microgrid model

Alsulami,

Abu Al-Haija,

Alturki

et al. 2024

Sci Rep

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Microgrids are small-scale energy system that supplies power to homes, businesses, and industries. Microgrids can be considered as a trending technology in energy fields due to their power to supply reliable and sustainable energy. Microgrids have a mode called the island, in this mode, microgrids are disconnected from the major grid and keep providing energy in the situation of an energy outage. Therefore, they help the main grid during peak energy demand times. The microgrids can be connected to the network, which is called networked microgrids. It is possible to have flexible energy resources by using their enhanced energy management systems. However, connection microgrid systems to the communication network introduces various challenges, including increased in systems complicity and noise interference. Integrating network communication into a microgrid system causes the system to be susceptible to noise, potentially disrupting the critical control signals that ensure smooth operation. Therefore, there is a need for predicting noise caused by communication network to ensure the operation stability of microgrids. In addition, there is a need for a simulation model that includes communication network and can generate noise to simulate real scenarios. This paper proposes a classifying model named Noise Classification Simulation Model (NCSM) that exploits the potential of deep learning to predict noise levels by classifying the values of signal-to-noise ratio (SNR) in real-time network traffic of microgrid system. This is accomplished by initially applying Gaussian white noise into the data that is generated by microgrid model. Then, the data has noise and data without noise is transmitted through serial communication to simulate real world scenario. At the end, a Gated Recurrent Unit (GRU) model is implemented to predict SNR values for the network traffic data. Our findings show that the proposed model produced promising results in predicting noise. In addition, the classification performance of the proposed model is compared with well-known machine learning models and according to the experimental results, our proposed model has noticeable performance, which achieved 99.96% classification accuracy.

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Optimal Algorithms for Energy Storage Systems in Microgrid Applications: An Analytical Evaluation Towards Future Directions

Cited by 25 publications

References 144 publications

Integration of Superconducting Magnetic Energy Storage for Fast-Response Storage in a Hybrid Solar PV-Biogas with Pumped-Hydro Energy Storage Power Plant

Integration of Superconducting Magnetic Energy Storage for Fast-Response Storage in a Hybrid Solar PV-Biogas with Pumped-Hydro Energy Storage Power Plant

Multi-timescale available flexibility assessment of integrated energy systems considering different proportions of wind power installations

Exploring the efficacy of GRU model in classifying the signal to noise ratio of microgrid model

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