Fuel cost minimization with reserve capacity and inter-area flow limit for reliable and cost effective operation of multi microgrids

2016 ◽  
Author(s):  
Chandrasekhar Yammani ◽  
Vamsi Krishna Macha
Keyword(s):  
Cost Minimization ◽  
Cost Effective ◽  
Reserve Capacity ◽  
Fuel Cost ◽  
Flow Limit ◽  
Effective Operation ◽  
Fuel Cost Minimization

Optimal Power Flow and Optimal Reactive Power Dispatch Using Different Evolutionary Optimization Techniques

2018 ◽  
pp. 117-157
Keyword(s):  
Power Flow ◽  
Optimal Power Flow ◽  
Reactive Power ◽  
Cost Minimization ◽  
Voltage Profile ◽  
Fuel Cost ◽  
Loss Minimization ◽  
Optimal Reactive Power Dispatch ◽  
Reactive Power Dispatch ◽  
Fuel Cost Minimization

This chapter describes grey wolf optimization (GWO), teaching-learning-based optimization (TLBO), biogeography-based optimization (BBO), krill herd algorithm (KHA), chemical reaction optimization (CRO), and hybrid CRO (HCRO) algorithms to solve both single and multi-objective optimal power flow (MOOPF) and optimal reactive power dispatch (ORPD) problems while satisfying various operational constraints. The proposed HCRO approach along with GWO, TLBO, BBO, KHA, and CRO algorithms are implemented on IEEE 30-bus system to solve four different single objectives: fuel cost minimization, system power loss minimization, voltage stability index minimization, and voltage deviation minimization; two bi-objectives optimization, namely minimization of fuel cost and transmission loss; minimization of fuel cost and voltage profile; and one tri-objective optimization, namely minimization of fuel cost, minimization of transmission losses, and improvement of voltage profile simultaneously. The simulation results clearly suggest that the proposed is able to provide a better solution than other approaches.

scholarly journals Optimal Economic Load Dispatch using Multiobjective Cuckoo Search Algorithm

2018 ◽  
Vol 12 (1) ◽  
pp. 168 ◽  
Author(s):  
Z.M. Yasin ◽  
N.F.A. Aziz ◽  
N.A. Salim ◽  
N.A. Wahab ◽  
N.A. Rahmat
Keyword(s):  
Carbon Emission ◽  
Search Algorithm ◽  
Cost Minimization ◽  
Operating Cost ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Economic Load Dispatch ◽  
Fuel Cost ◽  
Multiobjective Particle Swarm Optimization ◽  
Fuel Cost Minimization

In this paper, Multiobjective Cuckoo Search Algorithm (MOCSA) is developed to solve Economic Load Dispatch (ELD) problem. The main goal of the ELD is to meet the load demand at minimum operating cost by determining the output of the committed generating unit while satisfying system equality and inequality constraints. The problem formulation is based on a multiobjective model in which the multiobjective are defined as fuel cost minimization and carbon emission minimization. MOCSA is based on the inspiration from the brooding parasitism of cuckoo species in nature. Three cases are considered to test the effectiveness of the proposed technique which are fuel cost minimization, carbon emission minimization and multiobjective function with fixed weighted sum. The effectiveness of the MOCSA’s performances are illustrated through comparative study with other techniques such as Multiobjective Genetic Algorithm (MOGA) and Multiobjective Particle Swarm Optimization (MOPSO) in terms of fitness functions. The proposed study was conducted on three generating unit system at various loading condition. The result proved that MOCSA provide better solution in minimizing fuel cost and carbon emission usage as compared to other techniques.

scholarly journals Model relaxations for the fuel cost minimization of steady-state gas pipeline networks

2000 ◽  
Vol 31 (2-3) ◽  
pp. 197-220 ◽  
Author(s):  
Suming Wu ◽  
R.Z. Ríos-Mercado ◽  
E.A. Boyd ◽  
L.R. Scott
Keyword(s):  
Steady State ◽  
Cost Minimization ◽  
Gas Pipeline ◽  
Fuel Cost ◽  
Pipeline Networks ◽  
Fuel Cost Minimization

scholarly journals A Modified Rao-2 Algorithm for Optimal Power Flow Incorporating Renewable Energy Sources

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1532
Author(s):  
Mohamed H. Hassan ◽  
Salah Kamel ◽  
Ali Selim ◽  
Tahir Khurshaid ◽  
José Luis Domínguez-García
Keyword(s):  
Renewable Energy ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Cost Minimization ◽  
Renewable Energy Sources ◽  
Energy Sources ◽  
Fuel Cost ◽  
Optimal Power ◽  
Fuel Cost Minimization ◽  
Bus System

In this paper, a modified Rao-2 (MRao-2) algorithm is proposed to solve the problem of optimal power flow (OPF) in a power system incorporating renewable energy sources (RES). Quasi-oppositional and Levy flight methods are used to improve the performance of the Rao algorithm. To demonstrate effectiveness of the MRao-2 technique, it is tested on two standard test systems: an IEEE 30-bus system and an IEEE 118-bus system. The objective function of the OPF is the minimization of fuel cost in five scenarios. The IEEE 30-bus system reflects fuel cost minimization in three scenarios (without RES, with RES, and with RES under contingency state), while the IEEE 118-bus system reflects fuel cost minimization in two scenarios (without RES and with RES). The achieved results of various scenarios using the suggested MRao-2 technique are compared with those obtained using five recent techniques: Atom Search Optimization (ASO), Turbulent Flow of Water-based Optimization (TFWO), Marine Predators Algorithm (MPA), Rao-1, Rao-3 algorithms, as well as the conventional Rao-2 algorithm. Those comparisons confirm the superiority of the MRao-2 technique over those other algorithms in solving the OPF problem.

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