Thunderstorm Algorithm for Determining Unit Commitment in Power System Operation

Afandi, Arif Nur; Sulistyorini, Yunis

doi:10.5614/j.eng.technol.sci.2016.48.6.7

Cited by 22 publications

(7 citation statements)

References 14 publications

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“…Currently, many algorithms have also been proposed which are conducted to phenomena or entities in nature [22]- [26]. These versions have been advanced to increase computational performances through hierarchies and procedures for sequencing orders of the algorithm [8], [14], [27]. In line with previous efforts, this paper presents an emphasizing of thunderstorm mechanism and salmon tracking applied to the power system problem considered various operational limitations.…”

Section: Introductionmentioning

confidence: 93%

“…In detail, the Thunderstorm Algorithm (TA) is depicted in Fig. 1 with transforming structures in computation processes are Cloud Phase: Streamer Phase; and Avalanche Phase [8], [11], [34]. This figure illustrated a striking propagation from the sky to earth as a charge moving direction.…”

Section: Algorithms Overviewmentioning

confidence: 99%

“…In addition, the optimal power production is also urgent to cover all possible combination between existing power plants as a unit commitment. The UC is commonly approached using an economic dispatch (ED) problem [1]- [7] while the optimal allocation of power outputs belongs to the various generators available to serve the load [8]- [11] considering also an emission dispatch (EmD) problem for decreasing pollutants [6], [12]- [14].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Combined Computational Intelligence Approach for the Power System Optimization Problem

Afandi¹,

Fadlika²,

Gumilar³

et al. 2018

EECSI

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A power system optimization is one of the major problems in the operating performances of the existing system. This problem takes place on the various technical cases to get optimal conditions of the system. Moreover, many approaches have been implemented to carry out the solution under operational constraints. This paper presents Thunderstorm Algorithm (TA) and Artificial Salmon Tracking Algorithm (ASTA) for defining the optimal strategy of the power system optimization based on the unit commitment. Both algorithms are tested on the IEEE-62 bus system, whereas, results show that ASTA and TA can be combined together to solve the power system problem. These algorithms have been applied to predict the power consumption and it has good performances while searching for the optimal solution. These results also show that the economic dispatch problem is conducted to the power production while the algorithm is performed in good characteristics.

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Section: Introductionmentioning

confidence: 93%

Section: Algorithms Overviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Combined Computational Intelligence Approach for the Power System Optimization Problem

Afandi¹,

Fadlika²,

Gumilar³

et al. 2018

EECSI

Self Cite

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“…In general, electric consumers are divided into 6 sectors, namely household, industry, business, social, government office building, and street public lighting [2], [30]- [33]. Each consumer of electric energy has different load characteristics based on electric energy consumption patterns in each sector [34].…”

Section: Problem Statementmentioning

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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“…For a couple of years, a previous penalty factor approach has been used to define an environmental involvement in the total cost of fuel consumptions at generating units. This factor corresponds to maximum capacities of generating units online the power system while producing power outputs to serve the power demand and it refers to the ascending order technique [6], [14], [28]. In this section, a new penalty factor approach is explored as a dominant penalty factor (DPF) which is assumed that dominations of pollutant discharges come from larger emissions of generating units exceeded the allowed emission [4].…”

Section: Over Rate Emission Coefficientmentioning

confidence: 99%

Over Rate Emission Coefficient and Thunderstorm Algorithm for Dynamic Economic Dispatch

A.N¹,

Hermawan²,

Winarno³

2017

IJET

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Abstract-Since environmental requirements have penetrated the power system, its operation needs to consider the fossil burning together with reducing running charges of the fuel consumption.Technically, atmospheric emissions should be controlled economically at thermal power plants for 24 hours associated with a dynamic economic dispatch (DED). Thunderstorm algorithm (TA) is used to solve the DED problem considered an over rate emission coefficient applied to IEEE-62 bus system as a tested model. Moreover, this work also uses technical and environmental requirements of the operation. Simulation results show that TA can be used to obtain the optimal solution. The computational speed is performed in a smooth characteristic while searching the solution of the DED, which is carried out in hourly results.Keyword-Dynamic Dispatch, Emission Coefficient, Power System, Thunderstorm Algorithm I. INTRODUCTION In general, power system operations are imposed in generation sections; transmission sections; distribution sections; and load sections, for conveying energy from generating sites to usage areas under a certain condition. Technically, these systems are operated using various strategies to exist generating units and to control power delivery processes. In addition, these processes are also operated to produce energy within a suitable power sharing for each power plant. In detail, the operation is conducted to technical and environmental constraints while distributing power outputs to load centres. Focused on these limitations, the environmental requirement is included in the operation especially at thermal power plants for reducing pollutants [1]- [4]. Moreover, pollutants are measured in an emission standard for decreasing and controlling various gaseous materials in air, such as CO; CO 2 ; SO x ; and NO x [4]- [9]. In particular, technical limitations are also used to put desired power outputs in a scheduled permitted portion at generating units and power transfers at the lines in order to control and monitor the operation. In fact, the existed power system spends a certain budget for the energy production and infrastructure maintenance associated with generation sections; transmission sections; distribution sections. Financially, power plants take a role for this contribution spent in the fuel procurement at the all period time operation. Recently, it becomes one of the most important problems in the power system operation to reduce the technical cost through unit commitments. By considering this condition, the cost optimization is a significant case to obtain the best schedule and to meet the load demand under operational limitations belonged to equality and inequality constraints for the 24 hours operationTo treat the whole operation for 24 hours, the power system operation becomes complex problems managed using economical cost strategies to provide electric energy and to match operational limitations. These strategies are used to select a better decision considered the minimum total operating cost as an economic ...

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Thunderstorm Algorithm for Determining Unit Commitment in Power System Operation

Cited by 22 publications

References 14 publications

Combined Computational Intelligence Approach for the Power System Optimization Problem

Combined Computational Intelligence Approach for the Power System Optimization Problem

Demand Forecasting Considering Actual Peak Load Periods Using Artificial Neural Network

Over Rate Emission Coefficient and Thunderstorm Algorithm for Dynamic Economic Dispatch

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