A comprehensive review on sustainable energy management systems for optimal operation of future-generation of solar microgrids

Tajjour, Salwan; Chandel, S.S.

doi:10.1016/j.seta.2023.103377

Cited by 40 publications

(13 citation statements)

References 141 publications

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“…A plot pk versus k is a sample correlogram. For large sample sizes, pk is normally distributed with mean p k , and variance is approximated by Bartlett's approximation for processes in which p k = 0 for k > m. The sample partial autocorrelation function can be represented as Equation (3).…”

Section: Arima and Sarima Modelsmentioning

confidence: 99%

“…One of its essential aspects lies in mitigating the adverse effects of traditional energy sources, primarily fossil fuels, which have long been fundamental to global energy production but pose significant environmental hazards, including air and water pollution, greenhouse gas emissions, and climate change. By transitioning towards renewable energy sources such as solar, wind, hydroelectric, and geothermal power, sustainable energy management endeavors to curtail these deleterious impacts, building a cleaner, healthier environment for the present and future inhabitants of our planet [1][2][3].…”

Section: Introduction 1backgroundmentioning

confidence: 99%

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Empowering Sustainability: A Consumer-Centric Analysis Based on Advanced Electricity Consumption Predictions

Durmus Senyapar,

Aksoz

2024

Sustainability

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This study addresses the critical challenge of accurately forecasting electricity consumption by utilizing Exponential Smoothing and Seasonal Autoregressive Integrated Moving Average (SARIMA) models. The research aims to enhance the precision of forecasting in the dynamic energy landscape and reveals promising outcomes by employing a robust methodology involving model application to a large amount of consumption data. Exponential Smoothing demonstrates accurate predictions, as evidenced by a low Sum of Squared Errors (SSE) of 0.469. SARIMA, with its seasonal ARIMA structure, outperforms Exponential Smoothing, achieving lower Mean Absolute Percentage Error (MAPE) values on both training (2.21%) and test (2.44%) datasets. This study recommends the adoption of SARIMA models, supported by lower MAPE values, to influence technology adoption and future-proof decision-making. This study highlights the societal implications of informed energy planning, including enhanced sustainability, cost savings, and improved resource allocation for communities and industries. The synthesis of model analysis, technological integration, and consumer-centric approaches marks a significant stride toward a resilient and efficient energy ecosystem. Decision-makers, stakeholders, and researchers may leverage findings for sustainable, adaptive, and consumer-centric energy planning, positioning the sector to address evolving challenges effectively and empowering consumers while maintaining energy efficiency.

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Section: Arima and Sarima Modelsmentioning

confidence: 99%

Section: Introduction 1backgroundmentioning

confidence: 99%

Empowering Sustainability: A Consumer-Centric Analysis Based on Advanced Electricity Consumption Predictions

Durmus Senyapar,

Aksoz

2024

Sustainability

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“…Establishing solar microgrid systems can benefit the shared use of solar energy among community residential consumers. Solar microgrids have become increasingly popular due to the widespread availability of solar energy resources (Tajjour and Singh Chandel, 2023). A microgrid is a bounded section of the electricity transmission and distribution network that combines locally distributed generation sources, energy storage devices, and regulated electrical loads to produce a self-sufficient energy system (Cagnano et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Modelling and optimizing microgrid systems with the utilization of real-time residential data: a case study for Palapye, Botswana

Seane,

Samikannu,

Oladiran

et al. 2024

Front. Energy Res.

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Microgrids are becoming a realistic choice for residential buildings due to the increasing need for affordable and sustainable energy solutions in developing nations. Through modeling and simulation, the main goal is to evaluate the viability and performance of a solar microgrid system. Residential load modeling is used, which is vital to developing an effective Energy Management System (EMS) for the microgrid. A residential household’s load metering data is examined using statistical methods, including time series and regression analysis. For the residential community load in this research, Proportional-Integral-Derivative (PID) controllers and Fuzzy Logic Controllers (FLC) are used to generate the necessary Direct Current (DC) microgrid voltage. The simulation research shows that FLC have benefits over PID controllers. The FLC technique performs better at reducing total harmonic distortion, which improves the microgrid system’s overall power quality. The Seasonal Autoregressive Integrated Moving Average (SARIMA) model was found to be the most appropriate and reliable model for the dataset after the performance of the models was evaluated using the metrics. The optimization results also showed that FLC optimization improves the microgrid system’s stability. The exponential Gaussian process regression (GPR) produced the highest R-squared measure of 0.49 and RSME measure of 7.9646, making it the best goodness fit for modeling the total daily energy usage and the peak daily usage.

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“…Energy optimization not only ensures the reliable and stable operation of MGs but also contributes to cost reduction, environmental sustainability, and grid resilience. In traditional power systems, centralized energy management and control strategies are typically sufficient to meet the re-quirements for their efficient operation (Tajjour et al, 2023;Dey et al, 2023). However, in MGs, due to the constraints imposed by diverse energy resources and constantly changing power demands, traditional energy optimization manage-ment methods are no longer applicable (Alamir et al, 2023;Zhang et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Energy optimization management of microgrid using improved soft actor-critic algorithm

Yu,

Zheng,

Zeng

et al. 2024

Int. J. Renew. Energy Dev.

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To tackle the challenges associated with variability and uncertainty in distributed power generation, as well as the complexities of solving high-dimensional energy management mathematical models in mi-crogrid energy optimization, a microgrid energy optimization management method is proposed based on an improved soft actor-critic algorithm. In the proposed method, the improved soft actor-critic algorithm employs an entropy-based objective function to encourage target exploration without assigning signifi-cantly higher probabilities to any part of the action space, which can simplify the analysis process of distributed power generation variability and uncertainty while effectively mitigating the convergence fragility issues in solving the high-dimensional mathematical model of microgrid energy management. The effectiveness of the proposed method is validated through a case study analysis of microgrid energy op-timization management. The results revealed an increase of 51.20%, 52.38%, 13.43%, 16.50%, 58.26%, and 36.33% in the total profits of a microgrid compared with the Deep Q-network algorithm, the state-action-reward-state-action algorithm, the proximal policy optimization algorithm, the ant-colony based algorithm, a microgrid energy optimization management strategy based on the genetic algorithm and the fuzzy inference system, and the theoretical retailer stragety, respectively. Additionally, com-pared with other methods and strategies, the proposed method can learn more optimal microgrid energy management behaviors and anticipate fluctuations in electricity prices and demand.

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A comprehensive review on sustainable energy management systems for optimal operation of future-generation of solar microgrids

Cited by 40 publications

References 141 publications

Empowering Sustainability: A Consumer-Centric Analysis Based on Advanced Electricity Consumption Predictions

Empowering Sustainability: A Consumer-Centric Analysis Based on Advanced Electricity Consumption Predictions

Modelling and optimizing microgrid systems with the utilization of real-time residential data: a case study for Palapye, Botswana

Energy optimization management of microgrid using improved soft actor-critic algorithm

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