OPTIMAL UTILIZATION OF ELECTRICAL ENERGY FROM POWER PLANTS BASED ON FINAL ENERGY CONSUMPTION USING GRAVITATIONAL SEARCH ALGORITHM

Smart grid architecture is one of the difficult constructions in electrical power systems. The main feature is divided into three layers; the first layer is the power system level and operation, the second layer is the sensor and the communication devices, which collect the data, and the third layer is the microprocessor or the machine, which controls the whole operation. This hierarchy is working from the third layer towards first layer and vice versa. This paper introduces an eco unit commitment study, that scheduling both conventional power plants (three IEEE) thermal plants) as a dispatchable distributed generators, with renewable energy resources (wind, solar) as a stochastic distributed generating units; and plug-in electric vehicles (PEVs), which can be contributed either loads or generators relied on the charging timetable in a trustworthy unit commitment. The target of unit commitment study is to minimize the combined eco costs by integrating more augmented clean and renewable energy resource with the help of field programming gate array (FPGA) layer installation. A meta-heuristic algorithm, such as the Gravitational Search Algorithm (GSA), proves its accuracy and efficiency in reducing the incorporated cost function implicating costs of CO2 emission by optimally integrating and scheduling stochastic resources and charging and discharging processes of PEVs with conventional resources power plants. The results obtained from GSA are compared with a conventional numerical technique, such as the Dynamic Programming (DP) algorithm. The feasibility to implement GSA on an appropriate hardware platform, such as FPGA, is also discussed.

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Optimal Selection of the Cluster Head in Wireless Sensor Networks by Combining the Multiobjective Genetic Algorithm and the Gravitational Search Algorithm

Journal of Sensors ◽

10.1155/2021/2292580 ◽

2021 ◽

Vol 2021 ◽

pp. 1-16

Author(s):

Seyed Reza Nabavi ◽

Vahid Ostovari Moghadam ◽

Mohammad Yahyaei Feriz Hendi ◽

Amirhossein Ghasemi

Keyword(s):

Genetic Algorithm ◽

Wireless Sensor Networks ◽

Energy Consumption ◽

Sensor Networks ◽

Search Algorithm ◽

Gravitational Search Algorithm ◽

Cluster Head ◽

Wireless Sensor ◽

Multiobjective Genetic Algorithm ◽

Gravitational Search

With the development of various applications of wireless sensor networks, they have been widely used in different areas. These networks are established autonomously and easily in most environments without any infrastructure and collect information of environment phenomenon for proper performance and analysis of events and transmit them to the base stations. The wireless sensor networks are comprised of various sensor nodes that play the role of the sensor node and the relay node in relationship with each other. On the other hand, the lack of infrastructure in these networks constrains the sources such that the nodes are supplied by a battery of limited energy. Considering the establishment of the network in impassable areas, it is not possible to recharge or change the batteries. Thus, energy saving in these networks is an essential challenge. Considering that the energy consumption rate while sensing information and receiving information packets from another node is constant, the sensor nodes consume maximum energy while performing data transmission. Therefore, the routing methods try to reduce energy consumption based on organized approaches. One of the promising solutions for reducing energy consumption in wireless sensor networks is to cluster the nodes and select the cluster head based on the information transmission parameters such that the average energy consumption of the nodes is reduced and the network lifetime is increased. Thus, in this study, a novel optimization approach has been presented for clustering the wireless sensor networks using the multiobjective genetic algorithm and the gravitational search algorithm. The multiobjective genetic algorithm based on reducing the intracluster distances and reducing the energy consumption of the cluster nodes is used to select the cluster head, and the nearly optimal routing based on the gravitational search algorithm is used to transfer information between the cluster head nodes and the sink node. The implementation results show that considering the capabilities of the multiobjective genetic algorithm and the gravitational search algorithm, the proposed method has improved energy consumption, efficiency, data delivery rate, and information packet transmission rate compared to the previous methods.

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Multi-objective optimal load distribution based on improved gravitational search algorithm in power plants

2018 Chinese Control And Decision Conference (CCDC) ◽

10.1109/ccdc.2018.8407802 ◽

2018 ◽

Cited By ~ 1

Author(s):

Le Wei ◽

Qilin Tian

Keyword(s):

Load Distribution ◽

Power Plants ◽

Search Algorithm ◽

Gravitational Search Algorithm ◽

Multi Objective ◽

Optimal Load ◽

Gravitational Search ◽

Optimal Load Distribution

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Selective maintenance process optimization based on an improved gravitational search algorithm, from the perspective of energy consumption

Engineering Optimization ◽

10.1080/0305215x.2019.1657111 ◽

2019 ◽

Vol 52 (8) ◽

pp. 1401-1420

Author(s):

Lele Zhang ◽

Libin Zhang ◽

Hongying Shan ◽

Hongmei Shan

Keyword(s):

Energy Consumption ◽

Process Optimization ◽

Search Algorithm ◽

Gravitational Search Algorithm ◽

Selective Maintenance ◽

Gravitational Search ◽

Maintenance Process

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Analysis of Energy Consumption and Energy Efficiency in Government Office Buildings of Bengkulu, Indonesia

Journal of Physics Conference Series ◽

10.1088/1742-6596/2111/1/012052 ◽

2021 ◽

Vol 2111 (1) ◽

pp. 012052

Author(s):

I A Rahardjo ◽

M Djaohar ◽

M Subekti ◽

Parjiman ◽

I Zakir ◽

...

Keyword(s):

Energy Efficiency ◽

Energy Consumption ◽

Energy Use ◽

Initial Conditions ◽

Electrical Energy ◽

Office Buildings ◽

Air Conditioner ◽

Final Energy ◽

Final Energy Consumption ◽

Consumption Intensity

Abstract This article aimed to analyze the energy consumption and energy efficiency in government office buildings of Bengkulu, Indonesia. This research was conducted using a quantitative descriptive method. The research step begins by observing the initial conditions of Energy Consumption Intensity (ECI) to determine the category of buildings that are efficient or not in terms of energy use, then collecting data on the profile of the use of the building and its rooms, recapitulation of the use of energy sources (both electrical energy and other chemical energy), a list of equipment that consumes electrical energy (lighting, air conditioning, and others). Furthermore, measuring the performance of systems and equipment that consumes energy and conducting efficiency analysis to then make recommendations with operating settings, to replace inefficient equipment with more efficient equipment. The final step is to draw conclusions about the final Energy Consumption Intensity (ECI) obtained based on the results of the analysis and recommendations obtained on the previous Energy Consumption Intensity (ECI) value. It can be concluded that the final Energy Consumption Intensity (ECI) of government office buildings of Bengkulu based on the results of the analysis and recommendations such as performing preventive maintenance on air conditioners, the temperature of the air conditioner is set to always be at 24°C, replacing the type of lamp in the artificial lighting system that still uses TLD lamps with LED lamps, and efforts in organizing energy management systems will decrease around 13 percent from the previous Energy Consumption Intensity (ECI) or down from 40.9 kWh/m2/year to be 35.6 kWh/m2/year.

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