Optimized Base Station Sleeping and Smart Grid Energy Procurement Scheme to Improve Energy Efficiency

2022 ◽  
pp. 465-486
Author(s):  
Qiang Wang ◽  
Hai-Lin Liu
Keyword(s):  
Carbon Emission ◽  
Optimal Algorithm ◽  
Base Station ◽  
Mixed Integer ◽  
Integer Programming Problem ◽  
Swarm Optimization ◽  
Improve Energy Efficiency ◽  
Dual Domain ◽  
Mixed Integer Programming Problem ◽  
Subcarrier Assignment

In this chapter, the authors propose a joint BS sleeping strategy, resource allocation, and energy procurement scheme to maximize the profit of the network operators and minimize the carbon emission. Then, a joint optimization problem is formulated, which is a mixed-integer programming problem. To solve it, they adopt the bi-velocity discrete particle swarm optimization (BVDPSO) algorithm to optimize the BS sleeping strategy. When the BS sleeping strategy is fixed, the authors propose an optimal algorithm based on Lagrange dual domain method to optimize the power allocation, subcarrier assignment, and energy procurement. Numerical results illustrate the effectiveness of the proposed scheme and algorithm.

Optimized Base Station Sleeping and Smart Grid Energy Procurement Scheme to Improve Energy Efficiency

2018 ◽  
pp. 357-378
Author(s):  
Qiang Wang ◽  
Hai-Lin Liu
Keyword(s):  
Carbon Emission ◽  
Optimal Algorithm ◽  
Base Station ◽  
Mixed Integer ◽  
Integer Programming Problem ◽  
Swarm Optimization ◽  
Improve Energy Efficiency ◽  
Dual Domain ◽  
Mixed Integer Programming Problem ◽  
Subcarrier Assignment

In this chapter, the authors propose a joint BS sleeping strategy, resource allocation, and energy procurement scheme to maximize the profit of the network operators and minimize the carbon emission. Then, a joint optimization problem is formulated, which is a mixed-integer programming problem. To solve it, they adopt the bi-velocity discrete particle swarm optimization (BVDPSO) algorithm to optimize the BS sleeping strategy. When the BS sleeping strategy is fixed, the authors propose an optimal algorithm based on Lagrange dual domain method to optimize the power allocation, subcarrier assignment, and energy procurement. Numerical results illustrate the effectiveness of the proposed scheme and algorithm.

Optimized Base Station Sleeping and Renewable Energy Procurement Scheme Using PSO

2017 ◽  
Vol 8 (1) ◽  
pp. 54-73
Author(s):  
Qiang Wang ◽  
Hai-Lin Liu
Keyword(s):  
Resource Allocation ◽  
Energy Efficiency ◽  
Wireless Networks ◽  
Renewable Energy ◽  
Carbon Emission ◽  
Optimal Algorithm ◽  
Base Station ◽  
Mixed Integer ◽  
Integer Programming Problem ◽  
Green Communication

Energy efficiency of the wireless networks has drawn more and more attentions due to the requirement of the green communication. The base station sleeping strategy and resource allocation can effectively improve the energy efficiency for the wireless networks. Meanwhile, renewable energy is important to decrease the carbon emission. In this paper, the authors propose a joint BS sleeping strategy, resource allocation and renewable energy procurement scheme to maximize the profit of the network operators and minimize the carbon emission. Then, a joint optimization problem is formulated, which is a mixed integer programming problem. To solve it, they adopt the bi-velocity discrete particle swarm optimization (BVDPSO) algorithm to optimize the BS sleeping strategy. When the BS sleeping strategy is fixed, the authors propose an optimal algorithm based on Lagrange dual domain method to optimize the power allocation, subcarrier assignment and energy procurement. Numerical results illustrate the effectiveness of their proposed scheme and algorithm.

Optimized Base Station Sleeping and Renewable Energy Procurement Scheme Using PSO

2020 ◽  
pp. 65-85
Author(s):  
Qiang Wang ◽  
Hai-Lin Liu
Keyword(s):  
Resource Allocation ◽  
Energy Efficiency ◽  
Wireless Networks ◽  
Renewable Energy ◽  
Carbon Emission ◽  
Optimal Algorithm ◽  
Base Station ◽  
Mixed Integer ◽  
Green Communication ◽  
Dual Domain

Energy efficiency of the wireless networks has drawn more and more attentions due to the requirement of the green communication. The base station sleeping strategy and resource allocation can effectively improve the energy efficiency for the wireless networks. Meanwhile, renewable energy is important to decrease the carbon emission. In this paper, the authors propose a joint BS sleeping strategy, resource allocation and renewable energy procurement scheme to maximize the profit of the network operators and minimize the carbon emission. Then, a joint optimization problem is formulated, which is a mixed integer programming problem. To solve it, they adopt the bi-velocity discrete particle swarm optimization (BVDPSO) algorithm to optimize the BS sleeping strategy. When the BS sleeping strategy is fixed, the authors propose an optimal algorithm based on Lagrange dual domain method to optimize the power allocation, subcarrier assignment and energy procurement. Numerical results illustrate the effectiveness of their proposed scheme and algorithm.

scholarly journals Optimizing the Spectrum and Power Allocation for D2D-Enabled Communication Systems Using DC Programming

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Guomei Gan ◽  
Yanhu Huang ◽  
Qiang Wang
Keyword(s):  
Resource Allocation ◽  
Power Allocation ◽  
Communication Systems ◽  
Dc Programming ◽  
Base Station ◽  
Allocation Problem ◽  
Mixed Integer ◽  
Integer Programming Problem ◽  
Resource Allocation Problem ◽  
Subcarrier Assignment

Device to device (D2D) communication has recently attracted a lot of attentions since it can significantly improve the system throughput and reduce the energy consumption. Indeed, the devices can communicate with each other in a D2D system, and the base station (BS) can share the spectrum with D2D users, which can efficiently improve the spectrum and energy efficiency. Nevertheless, spectrum sharing also raises the difficulty of resource allocation owing to the serious cochannel interference. To reduce the interference, the transmit power of the D2D pairs and BS to cellular users should be further optimized. In this paper, we consider the resource allocation problem of D2D networks involving the power allocation and subcarrier assignment. The resource allocation problem is formulated as a mixed integer programming problem which is difficult to solve. To reduce the computational complexity, the original problem is decomposed as two subproblems in terms of the subcarrier assignment and power allocation. For the subcarrier assignment problem, the particle swarm optimization (PSO) is adopted to solve it since the subcarrier assignment is an integer optimization problem, and it is difficult to be tackled using the traditional optimization approach. When the subcarrier assignment is fixed, there are only the power allocation variables in the original resource allocation problem. The difference of convex functions (DC) programming is adopted to solve the power allocation problem. Simulation results demonstrate the effectiveness of the proposed resource allocation scheme of D2D networks.

scholarly journals A Mixed Integer Programming Poultry Feed Ration Optimisation Problem Using the Bat Algorithm

2016 ◽  
Vol 2016 ◽  
pp. 1-6 ◽  
Author(s):  
Godfrey Chagwiza ◽  
Chipo Chivuraise ◽  
Christopher T. Gadzirayi
Keyword(s):  
Integer Programming ◽  
Mixed Integer Programming ◽  
Moringa Oleifera ◽  
Bat Algorithm ◽  
Mixed Integer ◽  
Poultry Feed ◽  
Integer Programming Problem ◽  
Optimum Solution ◽  
Mixed Integer Programming Problem ◽  
Number Of Iterations

In this paper, a feed ration problem is presented as a mixed integer programming problem. An attempt to find the optimal quantities of Moringa oleifera inclusion into the poultry feed ration was done and the problem was solved using the Bat algorithm and the Cplex solver. The study used findings of previous research to investigate the effects of Moringa oleifera inclusion in poultry feed ration. The results show that the farmer is likely to gain US$0.89 more if Moringa oleifera is included in the feed ration. Results also show superiority of the Bat algorithm in terms of execution time and number of iterations required to find the optimum solution as compared with the results obtained by the Cplex solver. Results revealed that there is a significant economic benefit of Moringa oleifera inclusion into the poultry feed ration.

Scheduling of Yard Truck Considering Loading and Unloading Simultaneously in an Underground Container Logistics System

2021 ◽  
pp. 036119812110478
Author(s):  
Yinping Gao ◽  
Daofang Chang ◽  
Jun Yuan ◽  
Chengji Liang
Keyword(s):  
Genetic Algorithm ◽  
Container Terminals ◽  
Mixed Integer ◽  
Operating Efficiency ◽  
Adaptive Genetic Algorithm ◽  
Integer Programming Problem ◽  
Logistics System ◽  
Container Logistics ◽  
Loading And Unloading ◽  
Mixed Integer Programming Problem

With the rapid growth of containers and scarce of land, the underground container logistics system (UCLS) presents a logical alternative for container terminals to better protect the environment and relieve traffic pressure. The operating efficiency of container terminals is one of the competitive edges over other terminals, which requires UCLS to be well integrated with the handling process of the storage yard. In UCLS, yard trucks (YTs) serve different handling points dynamically instead of one fixed handling point, and yard cranes (YCs) perform loading and unloading simultaneously. To minimize the total time of handling all containers in UCLS, the mixed integer programming problem is described and solved using an adaptive genetic algorithm (AGA). The convergence speed and accuracy of AGA are demonstrated by comparison with conventional genetic algorithm (GA). Additionally, AGA and CPLEX are compared with different scale cases. Experimental results show that the proposed algorithm is superior to CPLEX in resulted solutions and calculation time. A sensitivity analysis is provided to obtain reasonable numbers of YTs for scheduling between handling points and the storage yard in UCLS.

The Optimal Controlled Partitioning Strategy for Power System Based on Master-Slave Problem

2013 ◽  
Vol 385-386 ◽  
pp. 999-1006
Author(s):  
Wei Wang ◽  
Ting Yu ◽  
Tian Jiao Pu ◽  
Ai Zhong Tian ◽  
Ji Keng Lin
Keyword(s):  
Large Scale ◽  
Optimization Method ◽  
Mixed Integer ◽  
Master Problem ◽  
Decomposition Algorithms ◽  
Integer Programming Problem ◽  
Complete Model ◽  
New Approach ◽  
Mixed Integer Programming Problem ◽  
Partitioning Strategy

Controlled partitioning strategy is one of the effective measures taken for the situation when system out-of-step occurs. The complete splitting model, mostly solved by approximate decomposition algorithms, is a large-scale nonlinear mixed integer programming problem. A new alternate optimization method based on master-slave problem to search for optimal splitting strategy is proposed hereby. The complete model was converted into master-slave problems based on CGKP (Connected Graph Constrained Knapsack Problem). The coupling between master problem and slave problem is achieved through load adjustment. A better splitting strategy can be obtained through the alternating iteration between the master problem and the salve problem. The results of the examples show that the method can obtain better splitting strategy with less shed load than other approximate algorithms, which verifies the feasibility and effectiveness of the new approach presented.

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