Case Study of Holistic Energy Management Using Genetic Algorithms in a Sliding Window Approach

Minnerup, Katharina; Herrmann, Thomas; Steinstraeter, Matthias; Lienkamp, Markus

doi:10.3390/wevj10020046

Cited by 7 publications

(3 citation statements)

References 40 publications

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“…It can be downloaded with an open-source license in Danquah et al [47]. Since this model is open source, it is very popular with several independent researchers [48][49][50][51] and has been improved over the last years by including their feedback. Hence, the paper assumes that all programmatic mistakes have been solved and that the simulation model accurately represents the underlying mathematical model.…”

Section: Simulation Model and Verificationmentioning

confidence: 99%

Statistical Validation Framework for Automotive Vehicle Simulations Using Uncertainty Learning

et al. 2021

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The modelling and simulation process in the automotive domain is transforming. Increasing system complexity and variant diversity, especially in new electric powertrain systems, lead to complex, modular simulations that depend on virtual vehicle development, testing and approval. Consequently, the emerging key requirements for automotive validation involve a precise reliability quantification across a large application domain. Validation is unable to meet these requirements because its results provide little information, uncertainties are neglected, the model reliability cannot be easily extrapolated and the resulting application domain is small. In order to address these insufficiencies, this paper develops a statistical validation framework for dynamic systems with changing parameter configurations, thus enabling a flexible validation of complex total vehicle simulations including powertrain modelling. It uses non-deterministic models to consider input uncertainties, applies uncertainty learning to predict inherent model uncertainties and enables precise reliability quantification of arbitrary system parameter configurations to form a large application domain. The paper explains the framework with real-world data from a prototype electric vehicle on a dynamometer, validates it with additional tests and compares it to conventional validation methods. It is published as an open-source document. With the validation information from the framework and the knowledge deduced from the real-world problem, the paper solves its key requirements and offers recommendations on how to efficiently revise models with the framework’s validation results.

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Section: Simulation Model and Verificationmentioning

confidence: 99%

Statistical Validation Framework for Automotive Vehicle Simulations Using Uncertainty Learning

et al. 2021

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“…An interesting manuscript "The effect of perceived risk on the purchase intention of electric vehicles: an extension to the technology acceptance model" (Thilina, 2019) seeks to analyze significant market penetration for the sale of electric vehicles accompanied by the analysis (Mo, 2018) of life-cycle cost of ownership including congestion and environmental impacts (Tu, 2019), (Rajeev, et al, 2019), (Hao, 2017), (Philipsen, et al, 2019), (Lopez-Arboleda, et al, 2019), (Almeida, et al, 2019). Considerable emphasis have been placed on improvements in vehicle charging (Wolbertus, et al, 2019) amongst many other underlying technological areas seeking to improve the value proposition to potential buyers (Jager, et al, 2019), , , (Minnerup, et al, 2019), (Muller, 2019) and also make recommendations in both technology (Zha, et al, 2019), , (Zha, et al, 2019), , (Jiyan, et al, 2019), (Pier, et al, 2019), (Agaton, et al, 2019), , (Watanabe, et al, 2019), (Kusaka, et al, 2019), (Zhang, W., et al, 2019), , (Mayer, et al, 2019), (Ricciardi, et al, 2019), , (Yu, Z., et al, 2019), (Senda, et al, 2019), (Marquez-Fernandez, et al, 2019), (Wu, D., et al, 2019), (Wang, H., et al, 2019), (Obayashi, et al, 2019), (Gong, et al, 2019), (Vermeulen, et al, 2019), (Jia, J., et al, 2019), (Li, Q., et al, 2019) and policy incentives (Zhang, X., et al, 2019), (Ortar, et al, 2019), culminating in charging strategies to influence the obvious trade-off between gasoline prices and charging availability , (Wolbertus, et al, 2019),…”

Section: Materials (Literature Review)mentioning

confidence: 99%

Electric Vehicle Sales Catastrophe Averted (?)

Sands¹

2020

MAS

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The literature indicates catastrophic potential for electric vehicles around the year 2019. Amidst the predicted time, this research revisits the prequel analysis with state-of-the-art deterministic artificial intelligence methodologies to posit the potential for catastrophe in the upcoming year. The methodology has proven effective with motion mechanics, electrodynamics, and even financial analysis of sales date in the prequels, since the model commences with simple regression for mathematical model formulation asserting the certainty equivalence principle, followed by derivative modeling and eventually catastrophe analysis of the derivative models. The prequel analysis paradigms are retained in this sequel utilizing both monthly and cumulative sales data in simple least squares algorithms for predictive curve fitting to establish context and help correctly model the mathematical degree of the data. Extrapolation by forward time-propagation established predictions for models of various mathematical degrees (again merely for context). Next, catastrophe analysis (of the derivative form) revealed stable and unstable equilibrium points and then parametric variation was induced to evaluate the resulting behavior of the derivative models, highlighting the importance of the coefficient of the second order term (the acceleration or change of rate of sales as a forcing function). While the forcing function typically embodies both gasoline prices and vehicle charging proliferation, the relative stability of gas prices together with factors such as vehicle-to-grid elevate charging-station proliferation as the primary forcing function of slow-dynamics in catastrophe analysis. This brief manuscript revisits the prequel research to test the validity of those conclusions and with the benefit of the passage of time, reveal how well the mathematical modeling predicted real behavior. The main finding is the predicted potential catastrophe is less likely to occur and recommendations are made to insure catastrophe is averted.

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“…Application of the hybrid algorithm results in parameter optimization of the neural network, which increases fuel efficiency while reducing carbon emissions [10]. The research proposes an optimization approach for intelligent energy management in microgrids using a genetic algorithm [20]- [22]. It creates an adaptive energy management system for microgrids, capable of varying energy demands over time.…”

Section: Introductionmentioning

confidence: 99%

Optimal Energy Management System for Grid-Tied Microgrid: An Improved Adaptive Genetic Algorithm

Majeed,

Phichaisawat,

Asghar

et al. 2023

IEEE Access

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Grid-tied microgrids play a crucial role by connecting renewable energy sources to the main power grid, contributing to sustainability and resilience in a balanced and effective manner. However, the dynamic interplay between the intermittent nature of renewable energy sources and the volatility of load fluctuations presents a multifaceted array of intricate energy management complexities. This study aims to formulate optimization techniques for energy management systems based on renewable energy resources and standalone diesel systems. The proposed system consists of a wind turbine, a photovoltaic system, a standalone diesel generator, and a battery energy storage system, along with flexible and non-flexible loads tied to the local grid. Battery energy storage acts as a primary backup system, while diesel generators act as a standalone secondary backup system. The performance of the proposed optimization technique is validated using Matlab/Simulink, substantiating its performance and robustness, thus affirming its pragmatic suitability for real-world implementation. A comparison has been made with other optimization techniques and found that the proposed technique gives enhanced efficiency, improved resource allocation, load scheduling, and greater adaptability to varying demand and supply dynamics. Moreover, the proposed system exhibits a superior ability to achieve optimal energy utilization and realize noteworthy cost savings in comparison to the alternatives that underwent evaluation.INDEX TERMS Adaptive Genetic Algorithm, grid-tied microgrid, bidirectional converter, cost optimization, renewable energy resources, peak average ratio, load scheduling, resource allocation.

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Case Study of Holistic Energy Management Using Genetic Algorithms in a Sliding Window Approach

Cited by 7 publications

References 40 publications

Statistical Validation Framework for Automotive Vehicle Simulations Using Uncertainty Learning

Statistical Validation Framework for Automotive Vehicle Simulations Using Uncertainty Learning

Electric Vehicle Sales Catastrophe Averted (?)

Optimal Energy Management System for Grid-Tied Microgrid: An Improved Adaptive Genetic Algorithm

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