Designed Operating Approach of Economic Dispatch for Java Bali Power Grid Areas Considered Wind Energy and Pollutant Emission Optimized Using Thunderstorm Algorithm Based on Forward Cloud Charge Mechanism

Afandi, Arif Nur; Wibawa, Aji Prasetya; Padmantara, Syaad; Fujita, Goro; Triyana, W.; Sulistyorini, Yunis; Miyauchi, Hajime; Tutkun, Nedim; Mahmoud, Mostafa A. A.; Gao, Xiang

doi:10.15866/iree.v13i1.14687

Cited by 34 publications

(16 citation statements)

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“…In this section, obtained results are presented in several performances as indicated parameters for the economic problem [14], [19], [23]- [25]. According to the execution of the designed programs for 24 hours, the computation's results are summarized in Table 1.…”

Section: Resultsmentioning

confidence: 99%

“…In particular, a modeling is a more popular way than another approach to make a suitable system from the natural processes. In detail, the modeling approach is formed in clarifies assumptions, variables, and parameters [18], [19], [21], [22]. By considering the mathematical engineering, the typical mathematical modeling techniques include computer-aided design, finite element modeling, and analysis.…”

Section: Technical Learning Methodsmentioning

confidence: 99%

“…In ASTA, the exploring step is used to search out a mouth river for guiding the desired possibility selection. The surviving step is used to find out the returning destination to track the desired solution at all various branches [18], [19]. By considering Salmon's behavior, ASTA is constructed based on migrating steps as a computational intelligence referred to the exploring and surviving steps as illustrated in Figure 3.…”

Section: Learning Migrationmentioning

confidence: 99%

“…In detail, downstream and upstream migrations are main keys to being designed paths of the sequences. This phase will be used to define all provided points which are pointed to the best-selected destination for opening the path to get a direction [18], [19]. Moreover, the surviving step is used to find out the returning destination to track the desired solution at all various branches.…”

Section: Learning Migrationmentioning

confidence: 99%

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Natural Behavior Learning for Developing an Innovation of Computational Intelligence Applied to a Technical Quadratic Problem

Afandi¹,

Handayani²,

Lestari³

et al. 2019

Proceedings of the 2nd International Conference on Vocational Education and Training (ICOVET 2018)

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Recently, many algorithms have been presented for new approaches based on natural inspirations as introduced in this paper is explored from the migrating Salmon mechanism. This approach is Artificial Salmon Tracking Algorithm (ASTA) which is tested on a standard system model for carrying out a dynamic economic dispatch. In these studies, ASTA is also used to optimize the system and to get an optimal portion of the balanced combination. The results show that the solution is produced dynamically to make the optimal operation for 24 hours. The system model can be balanced based on the quadratic model while ASTA has been demonstrated clearly to search for optimal solutions.

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Section: Resultsmentioning

confidence: 99%

Section: Technical Learning Methodsmentioning

confidence: 99%

Section: Learning Migrationmentioning

confidence: 99%

Section: Learning Migrationmentioning

confidence: 99%

See 2 more Smart Citations

Natural Behavior Learning for Developing an Innovation of Computational Intelligence Applied to a Technical Quadratic Problem

Afandi¹,

Handayani²,

Lestari³

et al. 2019

Proceedings of the 2nd International Conference on Vocational Education and Training (ICOVET 2018)

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show abstract

“…Therefore, it is also necessary to estimate short-term electrical loads that can predict the need for the electrical usage. The short-term electrical load forecast (SELF) aims to predict electricity requirements in minutes, hours, days, and weekly hours [2], [8], [10], [16]- [18]. The SELF-brings out to forecast load requirements at a load center switchyard (LCS) and play an important role in the real-time control and security functions of the energy management.…”

Section: Introductionmentioning

confidence: 99%

Demand Forecasting Considering Actual Peak Load Periods Using Artificial Neural Network

Yuan¹,

Afandi²,

Putranto³

2018

EECSI

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Presently, electrical energy consumption continues to increase from year to year. Therefore, a short-term load forecasting is required that electricity providers can deliver continuous electrical energy to electricity consumers. By considering the estimation of the electrical load, the scheduling plan for operation and allocation of reserves can be managed well by the supply side. This study is focused on a forecasting of electrical loads using Artificial Neural Network (ANN) method considering a backpropagation algorithm model. The advantage of this method is to forecast the electrical load in accordance with patterns of past loads that have been taught. The data used for the learning is Actual Peak Load Period (APLP) data on the 150 kV system during 2017. Results show that the best network architecture is structured for the APLP Day and Night. Moreover, the momentum setting and understanding rate are 0.85 and 0.1 for the APLP Day. In contrast, 0.9 and 0.15 belong to the APLP Night. Based on the best network architecture, the APLP day testing process generates Mean Squared Error (MSE) around 0.04 and Mean Absolute Percentage Error (MAPE) around 4.66%, while the APLP Night generates MSE in 0.16 and MAPE in 16.83%.

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Mathematical approach of natural mechanism for exploring structures of evolutionary algorithm

Afandi¹,

Prasetya²,

Padmantara³

et al. 2018

AIP Conference Proceedings

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Designed Operating Approach of Economic Dispatch for Java Bali Power Grid Areas Considered Wind Energy and Pollutant Emission Optimized Using Thunderstorm Algorithm Based on Forward Cloud Charge Mechanism

Cited by 34 publications

References 0 publications

Natural Behavior Learning for Developing an Innovation of Computational Intelligence Applied to a Technical Quadratic Problem

Natural Behavior Learning for Developing an Innovation of Computational Intelligence Applied to a Technical Quadratic Problem

Demand Forecasting Considering Actual Peak Load Periods Using Artificial Neural Network

Mathematical approach of natural mechanism for exploring structures of evolutionary algorithm

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