Forecasting OPEC Electricity Generation Based on Elman Network Trained by Cuckoo Search Algorithm

2019 ◽  
pp. 59-76
Author(s):  
Abdullah Khan ◽  
Rahmat Shah ◽  
Nasreen Akhter ◽  
Awais Qureshi ◽  
Kamran Ullah ◽  
...  
Keyword(s):  
Electricity Generation ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Elman Network

scholarly journals Minimize electricity generation cost for large scale wind- thermal systems considering prohibited operating zone and power reserve constraints

2021 ◽  
Vol 11 (3) ◽  
pp. 1905
Author(s):  
Phan Nguyen Vinh ◽  
Bach Hoang Dinh ◽  
Van-Duc Phan ◽  
Hung Duc Nguyen ◽  
Thang Trung Nguyen
Keyword(s):  
Wind Power ◽  
Electricity Generation ◽  
Power Plants ◽  
Large Scale ◽  
Search Algorithm ◽  
Thermal Power ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Working Zone ◽  
Generation Cost

Wind power plants (WPs) play a very important role in the power systems because thermal power plants (TPs) suffers from shortcomings of expensive cost and limited fossil fuels. As compared to other renewable energies, WPs are more effective because it can produce electricity all a day from the morning to the evening. Consequently, this paper integrates the optimal power generation of TPs and WPs to absolutely exploit the energy from WPs and reduce the total electricity generation cost of TPs. The target can be reached by employing a proposed method, called one evaluation-based cuckoo search algorithm (OEB-CSA), which is developed from cuckoo search algorithm (CSA). In addition, conventional particle swarm optimization (PSO) is also implemented for comparison. Two test systems with thirty TPs considering prohibited working zone and power reserve constraints are employed. The first system has one wind power plant (WP) while the second one has two WPs. The result comparisons indicate that OEB-CSA can be the best method for the combined systems with WPs and TPs.

scholarly journals Optimal Load Dispatch in Competitive Electricity Market by Using Different Models of Hopfield Lagrange Network

Energies ◽  
2019 ◽  
Vol 12 (15) ◽  
pp. 2932 ◽  
Author(s):  
Duong ◽  
Nguyen ◽  
Phan ◽  
Vo ◽  
Nguyen
Keyword(s):  
Electricity Market ◽  
Electricity Generation ◽  
Search Algorithm ◽  
Error Function ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Hyperbolic Tangent ◽  
Electric Market ◽  
Optimal Load ◽  
Revenue Models

In this paper, a Hopfield Lagrange network (HLN) method is applied to solve the optimal load dispatch (OLD) problem under the concern of the competitive electric market. The duty of the HLN is to determine optimal active power output of thermal generating units in the aim of maximizing the benefit of electricity generation from all available units. In addition, the performance of the HLN is also tested by using five different functions consisting of the logistic, hyperbolic tangent, Gompertz, error, and Gudermanian functions for updating outputs of continuous neurons. The five functions are tested on two systems with three units and 10 units considering two revenue models in which the first model considers payment for power delivered and the second model concerns payment for reserve allocated. In order to evaluate the real effectiveness and robustness of the HLN, comparisons with other methods such as particle swarm optimization (PSO), the cuckoo search algorithm (CSA) and differential evolution (DE) are also implemented on the same systems. High benefits and fast execution time from the HLN lead to a conclusion that the HLN should be applied for solving the OLD problem in a competitive electric market. Among the five applied functions, error function is considered to be the most effective one because it can support the HLN to find the highest benefit and reach the fastest convergence with the smallest number of iterations. Thus, it is suggested that error function should be used for updating outputs for continuous neurons of the HLN.

Optimal broadcast scheduling method for VANETs: An adaptive discrete firefly approach

2020 ◽  
Vol 39 (6) ◽  
pp. 8125-8137
Author(s):  
Jackson J Christy ◽  
D Rekha ◽  
V Vijayakumar ◽  
Glaucio H.S. Carvalho
Keyword(s):  
Intelligent Transportation System ◽  
Search Algorithm ◽  
Mac Protocol ◽  
High Mobility ◽  
Optimal Solution ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Traffic Density ◽  
Scheduling Problem ◽  
Broadcast Scheduling

Vehicular Adhoc Networks (VANET) are thought-about as a mainstay in Intelligent Transportation System (ITS). For an efficient vehicular Adhoc network, broadcasting i.e. sharing a safety related message across all vehicles and infrastructure throughout the network is pivotal. Hence an efficient TDMA based MAC protocol for VANETs would serve the purpose of broadcast scheduling. At the same time, high mobility, influential traffic density, and an altering network topology makes it strenuous to form an efficient broadcast schedule. In this paper an evolutionary approach has been chosen to solve the broadcast scheduling problem in VANETs. The paper focusses on identifying an optimal solution with minimal TDMA frames and increased transmissions. These two parameters are the converging factor for the evolutionary algorithms employed. The proposed approach uses an Adaptive Discrete Firefly Algorithm (ADFA) for solving the Broadcast Scheduling Problem (BSP). The results are compared with traditional evolutionary approaches such as Genetic Algorithm and Cuckoo search algorithm. A mathematical analysis to find the probability of achieving a time slot is done using Markov Chain analysis.

scholarly journals Optimization strategy of wireless charger node deployment based on improved cuckoo search algorithm

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Yang Wang ◽  
Feifan Wang ◽  
Yujun Zhu ◽  
Yiyang Liu ◽  
Chuanxin Zhao
Keyword(s):  
Sensor Network ◽  
Sensor Node ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Sensor Nodes ◽  
Optimization Strategy ◽  
Multi Objective ◽  
Received Power

AbstractIn wireless rechargeable sensor network, the deployment of charger node directly affects the overall charging utility of sensor network. Aiming at this problem, this paper abstracts the charger deployment problem as a multi-objective optimization problem that maximizes the received power of sensor nodes and minimizes the number of charger nodes. First, a network model that maximizes the sensor node received power and minimizes the number of charger nodes is constructed. Second, an improved cuckoo search (ICS) algorithm is proposed. This algorithm is based on the traditional cuckoo search algorithm (CS) to redefine its step factor, and then use the mutation factor to change the nesting position of the host bird to update the bird’s nest position, and then use ICS to find the ones that maximize the received power of the sensor node and minimize the number of charger nodes optimal solution. Compared with the traditional cuckoo search algorithm and multi-objective particle swarm optimization algorithm, the simulation results show that the algorithm can effectively increase the receiving power of sensor nodes, reduce the number of charger nodes and find the optimal solution to meet the conditions, so as to maximize the network charging utility.

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