Integration of smart grid with renewable energy sources: Opportunities and challenges – A comprehensive review

Kataray, Tarun; Nitesh, B.; Yarram, Bharath; Sinha, Sanyukta; Cüce, Erdem; Shaik, Saboor; Vigneshwaran, P.; Roy, Abin

doi:10.1016/j.seta.2023.103363

Cited by 48 publications

(17 citation statements)

References 102 publications

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“…Furthermore, predictive algorithms analyze weather forecasts to pre-adjust building temperatures, optimizing energy use for heating or cooling based on anticipated changes in the external environment. Quantitative analysis, grounded in a review of multiple case studies, indicates that such intelligent EMS implementations can lead to a 20-30% reduction in energy consumption [6]. This is supported by a meta-analysis of energy savings reported in various commercial buildings equipped with state-of-the-art EMS, highlighting the significant potential for energy efficiency improvements in building operations.…”

Section: Energy Management Systemsmentioning

confidence: 89%

Sustainable building practices: From construction to maintenance

2024

ACE

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This comprehensive study explores the integration of sustainable practices throughout the various phases of a buildings lifecycle, including construction, operation, and maintenance. It emphasizes the pivotal role of material selection, construction techniques, renewable energy integration, energy management systems, retrofitting, water conservation technologies, predictive maintenance, and efficiency monitoring in minimizing environmental impact while enhancing efficiency. Advanced materials such as high-performance concrete and bio-based insulation, alongside innovative construction methods like modular and prefabricated components, are highlighted for their potential to significantly reduce carbon footprints and energy consumption. The study further investigates the adoption of renewable energy systems, demonstrating their efficacy in achieving energy self-sufficiency. Through the operational phase, it underscores the importance of sophisticated Energy Management Systems (EMS) and retrofitting existing buildings with energy-efficient technologies. Additionally, it delves into the incorporation of water conservation technologies, which substantially decrease water usage and associated energy demands. The maintenance phase is discussed with a focus on predictive maintenance using IoT sensors and AI, alongside the use of sustainable materials for repairs. Quantitative analyses throughout the study illustrate potential reductions in energy consumption, carbon emissions, and water usage, underscoring the critical role of these practices in promoting sustainability in the construction industry. This study aims to provide a holistic view of sustainable building practices, offering valuable insights and methodologies for industry professionals and stakeholders.

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Section: Energy Management Systemsmentioning

confidence: 89%

Sustainable building practices: From construction to maintenance

2024

ACE

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“…FLCs, in the context of smart grids, provide a method to acquire expert knowledge and decision-making heuristics, which makes them highly suitable for optimizing the flow, storage, and distribution of energy. [21][22][23][24][25] The literature extensively examines the adaptive decision-making abilities of fuzzy logic systems. Fuzzy logic facilitates the depiction of inaccurate data by using linguistic variables and rules, so allowing controllers to adapt responsively to fluctuating circumstances.…”

Section: Literature Reviewmentioning

confidence: 99%

Fuzzy Logic-Based Energy Management in Smart Grids for Renewable Integration

Sharma,

Raju,

Dhall

et al. 2024

E3S Web Conf.

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This study presents a novel Fuzzy Logic-Based Energy Management model that aims to optimize the incorporation of renewable energy sources into smart grids. The research used simulated data to evaluate the model’s performance in important metrics, revealing significant improvements in the consumption of renewable energy, stability of the grid, dependability of energy storage, and overall efficiency of the system. The Fuzzy Logic Controller adjusts energy distribution in response to current inputs, leading to a significant 20% improvement in the usage of renewable energy. The capacity to adapt is crucial in dealing with the inherent fluctuation of solar, wind, and biomass sources. The approach greatly improves grid stability, as shown by a 15% decrease in grid frequency variations, highlighting its efficacy in assuring a more regulated and steady electricity supply. Furthermore, the dependability of energy storage systems exhibits a notable 25% enhancement in the state of charge, suggesting optimal cycles of charging and discharging. This increased dependability enhances the stability of the energy supply during times of high demand and variations in the power system. The Fuzzy Logic-Based Energy Management model exhibits a significant 22% improvement in total system efficiency when compared to conventional management systems. This indicator encompasses the model’s combined effect on the usage of renewable energy, the stability of the power grid, and the optimization of energy storage. Comparative analyses conducted against traditional control strategies, such as proportional-integralderivative controllers, consistently demonstrate the superiority of the fuzzy logic approach. This approach results in a 10% decrease in grid frequency deviations, a 15% enhancement in energy storage state of charge, and a 12% boost in overall system efficiency. The resilience of the Fuzzy Logic Controller is highlighted by sensitivity analysis, since it demonstrates consistent performance even when parameters vary significantly. The model’s practical usefulness and adherence to ethical principles are further confirmed by validation using real-world data from operational smart grid installations. This study provides valuable insights, establishing the Fuzzy Logic-Based Energy Management model as an innovative method for addressing the difficulties associated with integrating renewable energy into smart grids. This model promotes a more sustainable and efficient energy environment for the future.

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“…The objective function is designed to minimize the net EC of the building, accounting for energy efficiency, RE utilization, and occupant comfort. This can be expressed as EQU (1).…”

Section: Problem Definitionmentioning

confidence: 99%

Optimizing Building Energy Management with Deep Reinforcement Learning for Smart and Sustainable Infrastructure

Alsharafa,

Krishna

et al. 2024

JMC

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This study develops a new technique for optimising Energy Consumption (EC) and occupant satisfaction in business centres using Building Energy Management Systems (BEMS) that implement Deep Reinforcement Learning (DRL). Energy Management Models (EMM) are growing increasingly advanced and vital for intelligent power systems due to the growing demand for energy efficiency and the adoption of Renewable Energy Sources (RES), which are subject to variability. Flawed energy Consumption (EC) and problems are typical effects of traditional BEMS due to their unpredictability and failure to adapt to new environments. In this intended investigation, a DRL framework is demonstrated that may evolve its decision-making in real-time to control energy savings, electricity, and HVAC through input from the environment in which it operates. A pair of significant metrics, namely the cost of energy and room temperature stability, are employed to assess the effectiveness of the model compared to that provided by conventional rule-driven and predictive control systems. As investigated with different baseline models, the experimental findings proved that the DRL approach significantly reduced the cost of electricity while maintaining stable levels of comfort.

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Integration of smart grid with renewable energy sources: Opportunities and challenges – A comprehensive review

Cited by 48 publications

References 102 publications

Sustainable building practices: From construction to maintenance

Sustainable building practices: From construction to maintenance

Fuzzy Logic-Based Energy Management in Smart Grids for Renewable Integration

Optimizing Building Energy Management with Deep Reinforcement Learning for Smart and Sustainable Infrastructure

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