Optimizing Generation Capacities Incorporating Renewable Energy with Storage Systems Using Genetic Algorithms

Abbas, Farukh; Habib, Salman; Feng, Donghan; Yan, Zheng

doi:10.3390/electronics7070100

Cited by 19 publications

(12 citation statements)

References 32 publications

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“…Also, due to the noise-immune and fault-tolerant characteristics of ANNs, they can be successfully used for inherently noisy data from energy systems. [29] used genetic algorithms to optimize the generation capacities of renewable energy systems integrated with storage systems. This study evaluated the economic feasibility of the introduction of energy storage systems to the electric grid.…”

Section: Annmentioning

confidence: 99%

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State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

et al. 2019

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Machine learning (ML) models have been widely used in the modeling, design and prediction in energy systems. During the past two decades, there has been a dramatic increase in the advancement and application of various types of ML models for energy systems. This paper presents the state of the art of ML models used in energy systems along with a novel taxonomy of models and applications. Through a novel methodology, ML models are identified and further classified according to the ML modeling technique, energy type, and application area. Furthermore, a comprehensive review of the literature leads to an assessment and performance evaluation of the ML models and their applications, and a discussion of the major challenges and opportunities for prospective research. This paper further concludes that there is an outstanding rise in the accuracy, robustness, precision and generalization ability of the ML models in energy systems using hybrid ML models. Hybridization is reported to be effective in the advancement of prediction models, particularly for renewable energy systems, e.g., solar energy, wind energy, and biofuels. Moreover, the energy demand prediction using hybrid models of ML have highly contributed to the energy efficiency and therefore energy governance and sustainability.

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Section: Annmentioning

confidence: 99%

“…Results for study byAbbas et al (2018). Reproduced from[29], Elsevier: 2018.Anwar et al (2017)[30] presented a novel strategy for generation scheduling and power smoothing for a hybrid system of marine current and wind turbines. In this study, innovative…”

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confidence: 99%

State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

et al. 2019

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“…of runs 30. For GA with a similar number of iterations the selection, crossover and mutation ratios are selected as 0.1, 0.7, and 0.2 respectively, the algorithm description and parameter selection criteria are followed from multiple integer problem as in . Finally, the individual fitness

1 - Y

is calculated using the Monte Carlo Simulation method, the comprehensive index Y is defined in Equation .…”

Section: Problem Formulationmentioning

confidence: 99%

An Improved Optimal Forecasting Algorithm for Comprehensive Electric Vehicle Charging Allocation

et al. 2019

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The anticipation of large‐scale electric vehicles (EVs) charging and discharging load can bring security and reliability challenges to the power system. As a smart load, EV requires an intelligently designed scheduling and pricing algorithm that takes into account the stochastic EVs’ user behavior, grid charging capacity, battery characteristics, and real‐time electricity price variations. Herein, a multiobjective comprehensive stand‐alone solution is proposed considering a dynamic pricing model to intelligently regulate EVs charging/discharging schedule. An improved optimal forecasting approach is utilized to precisely predict the load variations by utilizing historical load and weather data. The proposed alternative heuristic charging strategy optimally configures solution indices and provides a tradeoff between considered evaluation parameters taken from the perspective of both power suppliers and EV users, thus mitigating the effect of uncontrolled charging introduced by stochastic charge–discharge activities. The objective is to shift the peak hours’ load to nonpeak hours with a reduction in average‐to‐peak ratio, minimize charging cost, and maximize the availability of charging capacity for pledging traveling plans determined by EV users. Different EV penetrations are tested to validate the performance of the proposed solution under massive EV integration, with a driving pattern obtained from the Beijing National Travel Survey.

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“…Reliability in the SA environment is regarded and considered via the loss of power supply probability (LPSP) concept in studies [8][9][10][11][12]. The LPSP depicts a value between zero and one.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Evolutionary Sizing Algorithms for Optimal Sizing of a Stand-Alone PV-WT-Battery Hybrid System

et al. 2019

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An increase in the world’s population results in high energy demand, which is mostly fulfilled by consuming fossil fuels (FFs). By nature, FFs are scarce, depleted, and non-eco-friendly. Renewable energy sources (RESs) photovoltaics (PVs) and wind turbines (WTs) are emerging alternatives to the FFs. The integration of an energy storage system with these sources provides promising and economical results to satisfy the user’s load in a stand-alone environment. Due to the intermittent nature of RESs, their optimal sizing is a vital challenge when considering cost and reliability parameters. In this paper, three meta-heuristic algorithms: teaching-learning based optimization (TLBO), enhanced differential evolution (EDE), and the salp swarm algorithm (SSA), along with two hybrid schemes (TLBO + EDE and TLBO + SSA) called enhanced evolutionary sizing algorithms (EESAs) are proposed for solving the unit sizing problem of hybrid RESs in a stand-alone environment. The objective of this work is to minimize the user’s total annual cost (TAC). The reliability is considered via the maximum allowable loss of power supply probability ( L P S P m a x ) concept. The simulation results reveal that EESAs provide better results in terms of TAC minimization as compared to other algorithms at four L P S P m a x values of 0%, 0.5%, 1%, and 3%, respectively, for a PV-WT-battery hybrid system. Further, the PV-WT-battery hybrid system is found as the most economical scenario when it is compared to PV-battery and WT-battery systems.

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Optimizing Generation Capacities Incorporating Renewable Energy with Storage Systems Using Genetic Algorithms

Cited by 19 publications

References 32 publications

State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

An Improved Optimal Forecasting Algorithm for Comprehensive Electric Vehicle Charging Allocation

Enhanced Evolutionary Sizing Algorithms for Optimal Sizing of a Stand-Alone PV-WT-Battery Hybrid System

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