A quantum class topper optimization algorithm to solve combined emission economic dispatch problem
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RESEARCH PAPER
A quantum class topper optimization algorithm to solve combined emission economic dispatch problem Abhishek Srivastava1 · Dushmanta Kumar Das1 · Pradeep Kumar Gupta1 Received: 5 November 2019 / Revised: 18 August 2020 / Accepted: 7 November 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract A combined emission and economic dispatch problem is referred as a complex multi-objective problem related to power system. In this article, an hybridized version of class topper optimization known as quantum class topper optimization is proposed to solve this problem. In quantum class topper optimization, quantum mechanism is used to enhance the searching ability of the proposed algorithm. The exploration, exploitation and convergence behavior of quantum class topper optimization is validated using benchmark functions. Later, four test cases on combined emission and economic dispatch are used to test the effectiveness of quantum class topper optimization. These four tests prove that the proposed optimization algorithm is effective to solve this real time optimization problem. Keywords Economic load dispatch (ELD) · Class topper optimization (CTO) · Optimization techniques
1 Introduction Economic load dispatch (ELD) problem plays an important role in a complex power system network. ELD is used to allocate power to the generator units such that minimum operating cost is incurred and at the same time, the power supply should meet the load demand requirement. To solve ELD problem, many traditional optimization methods such as lambda-iteration method [1], gradient method [2], Newton method [1] and base point-participation method [3] etc. are used. But, with the increasing number of constraints and non-linearity caused by prohibited operating zone, valve point effect, etc. in the power system, these tools were not sufficient enough to solve this problem. Hence, numerous optimization techniques were proposed to solve the ELD problem. Meta-heuristic optimization techniques are widely used to solve numerous optimization problems related to different fields. These methods provide a sub-optimal result that is very close to the global optimal result in a short computational time. Numerous optimization method have been * Dushmanta Kumar Das [email protected] Abhishek Srivastava [email protected] 1
Department of Electrical and Electronics Engineering, National Institute of Technology Nagaland, Dimapur, India
developed and presented in different articles such as harris hawk optimization (HHO) [4], sailfish optimizer (SFO) [5], moth-flame optimization algorithm (MFO) [6], salp swarm algorithm (SSA) [7], krill herd algorithm (KHA) [8], etc. Similarly, many techniques such as particle swarm optimization (PSO) [9], evolutionary programming (EP) [10], differential evolution (DE) [11], grey wolf optimization (GWO) [12], Teaching Learning-based optimization (TLBO) [13], Krill herd optimization (KHO) [14], emended salp swarm algorithm (ESSA) [15] etc. were introduced to
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