A Genetic Algorithm Approach to Optimize Dispatching for A Microgrid Energy System with Renewable Energy Sources

Sen, Sajib; Gupta, Kishor Datta; Poudyal, Subash; Ahsan, Md Manjurul

doi:10.5121/csit.2019.91401

Cited by 5 publications

(4 citation statements)

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“…where x i represents the optimization variables in the scheduling model, x i, min and x i, max represent the minimum and maximum values of the optimization variables, respectively, G represents the equality constraints to be satisfied in the scheduling model, and H represents the inequality constraints to be satisfied in the scheduling model. On the basis of ensuring the safe functioning of the generalized power distribution network, give the generalized power supply and users some autonomy, and study the coordinated optimum scheduling of the generalized power supply, users, and distribution network [17]. New stakeholders, such as generic power providers and consumers, are actively specifying power generating and use plans, participating in power grid dispatching, and reaping economic benefits as a consequence.…”

Section: Construction Of Multiobjective Coordinated Optimization Game Model For Generalized Power Active Distribution Networkmentioning

confidence: 99%

Multiobjective Optimal Dispatching Method of Generalized Power Active Distribution Network Based on Game Theory

Xiao-xiang

2021

Security and Communication Networks

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To improve the operating dependability of a generalized power active distribution network, a multiobjective optimal scheduling approach based on game theory is proposed. The active distribution network's multistakeholder coordinated and optimal dispatching mode is then established, and the game interaction between various stakeholders in the generalized power active distribution network is evaluated. Using the multistakeholder game coordinated and optimal dispatching strategy, manage and regulate the distribution firms, distributed generation operators, and users engaging in demand response in the active distribution network, to guarantee that the distribution system runs smoothly and fairly. The rate of renewable energy usage, network loss, and user satisfaction are all factored into the multiobjective optimum scheduling objective function of a generalized power active distribution network. Using the multistakeholder game coordinated and optimal dispatching strategy, manage and regulate the distribution firms, distributed generation operators, and demand response participants in the active distribution network, to make sure the distribution system runs smoothly and equitably. Taking into account the rate of renewable energy usage, network loss, and user satisfaction, a multiobjective optimum scheduling objective function for a generalized power active distribution network is thus created.

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Section: Construction Of Multiobjective Coordinated Optimization Game Model For Generalized Power Active Distribution Networkmentioning

confidence: 99%

Multiobjective Optimal Dispatching Method of Generalized Power Active Distribution Network Based on Game Theory

Xiao-xiang

2021

Security and Communication Networks

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“…Conti et al [18] Image processing IA Immune Ink-on-paper fingerprints identification Sen et al [152] Power distribution VR GA Optimize dispatching for micro-grid energy Yeom et al [153], Kitchens et al [154] Face& Keystroke analysis IA NN Person authentication using NN Ku [155] Keystroke analysis IA NN Password authentication for multiserver architecture Gai et al [40] Adaptive access ACS GA Allocate multimedia data in diverse memory Ye et al [41] Adaptive access ACS GA QoS-aware service Hartman et al [42] Scalable storage ACS Immune Cost-effective storage systems Lin et al [46] Trust based ACS Immune A mutual trust based ACS Tzeng [50] Hierarchical key ACS Immune Time-bound cryptographic scheme Sen et al [156] DDoS attack detection PNS NN Identify incoming DDoS type traffic attack…”

Section: Discussionmentioning

confidence: 99%

Applications and Evaluations ofBio-InspiredApproaches in Cloud Security: A Review

et al. 2020

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Cloud computing gained much popularity in the recent past due to its many internet-based services related to data, application, operating system, and eliminating the need for central hardware access. Many of the challenges associated with cloud computing can be specified as network load, security intrusion, authentication, biometric identification, and information leakage. Numerous algorithms have been proposed and evaluated to solve those challenges. Among those, bio-inspired algorithms such as Evolutionary, Swarm, Immune, and Neural algorithms are the most prominent ones which are developed based on nature's ecosystems. Bio-inspired algorithms' adaptability allows many researchers and practitioners to utilize them to solve many security-related cloud computing issues. This paper aims to explore previous research, recent studies, challenges, and scope for further analysis of cloud security. Therefore, this study provides an overview of bio-inspired algorithms application and evaluations, taking into account cloud security challenges,

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“…Combining data classification techniques with nature-inspired algorithms such as genetic programming [17] and the swarm algorithm [18] enables the differentiation of different bacteria from viral meningitis [19]. As a result, artificial intelligence has gained popularity in recent years as a beneficial tool for optimization and decision support systems.…”

Section: Introductionmentioning

confidence: 99%

Effect of Data Scaling Methods on Machine Learning Algorithms and Model Performance

et al. 2021

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Heart disease, one of the main reasons behind the high mortality rate around the world, requires a sophisticated and expensive diagnosis process. In the recent past, much literature has demonstrated machine learning approaches as an opportunity to efficiently diagnose heart disease patients. However, challenges associated with datasets such as missing data, inconsistent data, and mixed data (containing inconsistent missing data both as numerical and categorical) are often obstacles in medical diagnosis. This inconsistency led to a higher probability of misprediction and a misled result. Data preprocessing steps like feature reduction, data conversion, and data scaling are employed to form a standard dataset—such measures play a crucial role in reducing inaccuracy in final prediction. This paper aims to evaluate eleven machine learning (ML) algorithms—Logistic Regression (LR), Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), Classification and Regression Trees (CART), Naive Bayes (NB), Support Vector Machine (SVM), XGBoost (XGB), Random Forest Classifier (RF), Gradient Boost (GB), AdaBoost (AB), Extra Tree Classifier (ET)—and six different data scaling methods—Normalization (NR), Standscale (SS), MinMax (MM), MaxAbs (MA), Robust Scaler (RS), and Quantile Transformer (QT) on a dataset comprising of information of patients with heart disease. The result shows that CART, along with RS or QT, outperforms all other ML algorithms with 100% accuracy, 100% precision, 99% recall, and 100% F1 score. The study outcomes demonstrate that the model’s performance varies depending on the data scaling method.

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A Genetic Algorithm Approach to Optimize Dispatching for A Microgrid Energy System with Renewable Energy Sources

Cited by 5 publications

References 0 publications

Multiobjective Optimal Dispatching Method of Generalized Power Active Distribution Network Based on Game Theory

Multiobjective Optimal Dispatching Method of Generalized Power Active Distribution Network Based on Game Theory

Applications and Evaluations ofBio-InspiredApproaches in Cloud Security: A Review

Effect of Data Scaling Methods on Machine Learning Algorithms and Model Performance

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