Multi-Objective Heterogeneous Clustering Approach for Efficient-Energy Optimization in WSN

2019 ◽  
Author(s):  
Abhishek Prajapati ◽  
Vinay Kumar ◽  
Prince Rajpoot ◽  
Kumkum Dubey ◽  
Shubham Harsh Singh ◽  
...  
Keyword(s):  
Energy Optimization ◽  
Efficient Energy ◽  
Multi Objective ◽  
Clustering Approach ◽  
Heterogeneous Clustering

scholarly journals Enhanced Multi-Objective Energy Optimization by a Signaling Method

Energies ◽  
2016 ◽  
Vol 9 (10) ◽  
pp. 807 ◽  
Author(s):  
João Soares ◽  
Nuno Borges ◽  
Zita Vale ◽  
P.B. Oliveira
Keyword(s):  
Energy Optimization ◽  
Multi Objective

A New Energy Optimization Strategy for Pumping Operation in Water Distribution Systems

2012 ◽  
Author(s):  
Dhafar Al-Ani ◽  
Saeid Habibi
Keyword(s):  
Distribution System ◽  
Energy Cost ◽  
Distribution Systems ◽  
Water Distribution ◽  
Water Distribution System ◽  
Water Distribution Systems ◽  
Energy Optimization ◽  
Operating Conditions ◽  
Optimization Strategy ◽  
Multi Objective

As time goes on, more and more operating-modes based on changing demand profiles will be compiled to enrich the range of feasible solutions for a water distribution system. This implies the conservation of energy consumed by a water pumping station and improves the ability for energy optimization. Another important goal was improving safety, reliability, and maintenance cost. In this paper, three important goals were addressed: cost-effectives, safety, and self-sustainability operations of water distribution systems. In this work, the objective functions to optimize were total electrical energy cost, maintenance costs, and reservoir water level variation while preserving the service provided to water clients. To accomplish these goals, an effective Energy Optimization Strategy (EOS) that manages trade-off among operational cost, system safety, and reliability was proposed. Moreover, the EOS aims at improving the operating conditions (i.e., pumping schedule) of an existing network system (i.e., with given capacities of tanks) and without physical changes in the infrastructure of the distribution systems. The new strategy consisted of a new Parallel Multi-objective Particle Swarm optimization with Adaptive Search-space Boundaries (P-MOPSO-ASB) and a modified EPANET. This has several advantages: obtaining a Pareto-front with solutions that are quantitatively equally good and providing the decision maker with the opportunity to qualitatively compare the solutions before their implementation into practice. The multi-objective optimization approach developed in this paper follows modern applications that combine an optimization algorithm with a network simulation model by using full hydraulic simulations and distributed demand models. The proposed EOS was successfully applied to a rural water distribution system, namely Saskatoon West. The results showed that a potential for considerable cost reductions in total energy cost was achieved (approximately % 7.5). Furthermore, the safety and the reliability of the system are preserved by using the new optimal pump schedules.

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