MXenes in solid-state batteries: Current status and outlook

Serajian, Sahand; Shamsabadi, Ahmad A.; Gnani Peer Mohamed, Syed Ibrahim; Nejati, Siamak; Bavarian, Mona

doi:10.1016/j.jpowsour.2024.234721

Cited by 4 publications

(1 citation statement)

References 239 publications

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“…These techniques can be categorized into four main methods: Monte Carlo Simulation approach (MCS), and Analytical and Approximate methods. In this paper, the MCS method is chosen for modeling due to its ability to effectively handle complex uncertain variables [31]- [37].…”

Section: Uncertainty Modelingmentioning

confidence: 99%

Economic energy scheduling of electrical microgrid considering optimal participation of the electric vehicles

Du,

Yang,

Guo

2024

STET

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This research presents a strategy for managing energy scheduling within an electrical microgrid, with a specific focus on enhancing the integration of electric vehicles (EVs). By incorporating Monte Carlo simulation to address uncertainties related to EV charging power and demand-side variables, the study aims to ensure precise outcomes. The economic energy scheduling is conducted on a day-ahead basis, taking these uncertainties into consideration to assess the efficiency of the recommended approach. The primary objective is to reduce the overall system costs, encompassing operational expenditures and EV charging power. To tackle the intricacies of the operational framework, the study utilizes the modified sunflower optimization (MSFO) algorithm to resolve the outlined issue. The simulation findings highlight the superior performance of the proposed optimization algorithms compared to others. The proposed approach leads to minimize cost of microgrid by 4.31%, 3.82% and 1.87% than genetic algorithm (GA), Particle swarm optimization (PSO) algorithm and Teaching learning-based optimization (TLBO) algorithm, respectively.

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Section: Uncertainty Modelingmentioning

confidence: 99%

Economic energy scheduling of electrical microgrid considering optimal participation of the electric vehicles

Du,

Yang,

Guo

2024

STET

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Optimal Day-Ahead Energy Scheduling of the Smart Distribution Electrical Grid Considering Hybrid Demand Management

Candra,

Pradhan,

Shukhratovna

et al. 2024

Smart Grids and Energy

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Optimal demand management of smart energy hybrid system based on multi-objective optimization problem

Askar,

Sadikova,

Mohammed

et al. 2024

Sci. Tech. Energ. Transition

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This paper presents a multi-objective problem for the energy planning of an energy hybrid system. The problem considers three main objectives: minimizing emission pollution and operation cost on the generation side, addressing consumers' dissatisfaction with the electrical demand, and reducing deviation from optimal levels in the 24 hours ahead to flatten demand profiles. To achieve this, a demand flexibility strategy is implemented, involving the optimal shifting of electrical demand using deferrable loads. The proposed approach utilizes the augmented epsilon-constraint method to identify Pareto solutions for the objectives. Additionally, the TOPSIS decision-making technique is employed to select the optimal solution from the set of Pareto solutions. The effectiveness and robustness of the proposed approach are validated through two case studies. Overall, the paper highlights the importance of considering multiple objectives in the energy scheduling of hybrid systems and demonstrates the effectiveness of the proposed approach in achieving a balance between environmental, economic, and consumer satisfaction goals. The use of demand flexibility strategies and multi-objective optimization techniques can significantly improve the operation energy systems, paving the way for more efficient energy management practices. The implementation of demand side management has resulted in a significant reduction of 2.8% and 64.9% in the first and second objectives, respectively, compared to the absence of such management.

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MXenes in solid-state batteries: Current status and outlook

Cited by 4 publications

References 239 publications

Economic energy scheduling of electrical microgrid considering optimal participation of the electric vehicles

Economic energy scheduling of electrical microgrid considering optimal participation of the electric vehicles

Optimal Day-Ahead Energy Scheduling of the Smart Distribution Electrical Grid Considering Hybrid Demand Management

Optimal demand management of smart energy hybrid system based on multi-objective optimization problem

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