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Bare Bones Teaching Learning-Based Optimization Technique for Economic Emission Load Dispatch Problem Considering Transmission Losses

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Iranian Journal of Science and Technology, Transactions of Electrical Engineering Aims and scope Submit manuscript

Abstract

Economic emission load dispatch plays a great role in power system cost analysis. The purpose of economic emission load dispatch is to minimize the fuel and emission cost satisfying load demand. Various iterative techniques had been used to solve economic emission load dispatch problem in previous years by different authors. In this article, the new teaching learning-based optimization (TLBO) technique using variant bare bones TLBO has been proposed for solving economic emission load dispatch problem with convex and non-convex constraints by considering transmission losses and valve point loading effects. Conventional TLBO has two phases like teacher phase and learner phase. In teacher phase, learners update their knowledge through sharing knowledge with teacher, and finally, learners improve their knowledge by interactions among learners. Bare bones TLBO employs an interactive learning strategy, which is the hybridization of the learning strategy in the conventional TLBO. The proposed algorithm has been applied on six-, ten- and forty-unit systems, and the results obtained are compared with existing techniques; hence, superiority of the proposed algorithm is proved.

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Abbreviations

\(F_{i} \left( {P_{i} } \right)\) :

Fuel cost function

\(E_{i} \left( {P_{i} } \right)\) :

Emission cost function

\(P_{i}\) :

Output power of generator i

N :

Number of generators

\(a_{i} ,b_{i} ,c_{i} ,d_{i} ,e_{i}\) :

Cost coefficients of unit i

\(\alpha_{i} ,\beta_{i} ,\gamma_{i}\) :

Emission cost coefficient

\(P_{i}^{\hbox{min} }\) :

Minimum operating limits of generator i

\(P_{i}^{\hbox{max} }\) :

Maximum operating limits of generator i

\(P_{\text{D}}\) :

Load demand

\(P_{\text{L}}\) :

Transmission losses

\(B_{ij}\) :

Transmission loss coefficient

\(P_{N}\) :

Output of Nth generator

L :

Number of learners in class, i.e., ‘class size’

D :

Number of courses offered to the learners, i.e., ‘no of designed variables’

MAXIT:

Maximum number of allowable iterations

\(X_{j}^{\hbox{min} }\) :

Minimum value of jth parameter

\(X_{j}^{\hbox{max} }\) :

Maximum value for jth parameter

\(X_{\text{Teacher}}\) :

Optimum power output generator (known as ‘Teacher’)

\(Xnew_{i}^{g}\) :

ith learner of the matrix

ELD:

Economic load dispatch

EELD:

Economic emission load dispatch

CEED:

Combined economic emission dispatch

DED:

Dynamic economic dispatch

FPA:

Flower pollination algorithm

ED:

Economic dispatch

ANN:

Artificial neural network

EA:

Evolutionary algorithm

GA:

Genetic algorithm

SA:

Simulated annealing

EP:

Evolutionary programming

TS:

Tabu search

PSO:

Particle swarm optimization

EBSO:

Elephant-based swarm optimization

SOA-SQP:

Seeker optimization algorithm and sequential quadratic programming

GSA:

Gravitational search algorithm

ABC:

Artificial bee colony

QP:

Quadratic programming

BBTLBO:

Bare bones teaching learning-based optimization algorithm

QTLBO:

Quasi-oppositional teaching learning-based optimization algorithm

TLBO:

Teaching learning-based optimization algorithm

MODE:

Multi-objective differential evolution

NSGA-II:

Non-dominated Sorting Genetic Algorithm-II

PDE:

Pareto differential evolution

SPEA-2:

Strength pareto evolutionary algorithm 2

NN-EPSO:

Neural network with efficient particle swarm optimization

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Authors and Affiliations

  1. EE Department, Netaji Subhash Engineering College, Kolkata, 700152, India

    Deblina Maity

  2. EE Department, Dr. B C Roy Engineering College, Durgapur, 732101, India

    Sumit Banerjee

  3. EE Department, Indian Institute of Engineering Science and Technology, Shibpur, India

    Chandan Kumar Chanda

Authors
  1. Deblina Maity
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  2. Sumit Banerjee
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  3. Chandan Kumar Chanda
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Correspondence to Sumit Banerjee.

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Maity, D., Banerjee, S. & Chanda, C.K. Bare Bones Teaching Learning-Based Optimization Technique for Economic Emission Load Dispatch Problem Considering Transmission Losses. Iran J Sci Technol Trans Electr Eng 43 (Suppl 1), 77–90 (2019). https://doi.org/10.1007/s40998-018-0158-1

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