Abstract
The economic emission dispatch (EED) is a highly constrained nonlinear multiobjective optimization problem with a convex (or nonconvex) solution space. These characteristics and constraints make the EED a difficult problem to solve. Several approaches for a solution have been proposed, such as deterministic techniques, stochastic techniques, or a combination of both. This work presents the use of an algebraic (deterministic) technique, the numerical polynomial homotopy continuation (NPHC) method, to solve the EED problem. A comparison with the sequential quadratic programming (SQP) algorithm and the nondominated sorting genetic algorithm II (NSGA-II) is also presented. Results show that the NPHC algorithm finds all the roots (solutions) of the problem starting from any initial point and assures an accurate solution with a good convergence time. In addition, the NPHC algorithm provides a more accurate solution than the SQP algorithm and the NSGA-II.
Highlights
Power dispatch is a complicated task for the energy industry because a highly variable and unpredictable load demand from the customers needs to be satisfied using the most suitable mix of producers [1,2]
It is observed that the Numerical Polynomial Homotopy Continuation (NPHC) and the sequential quadratic programming (SQP) algorithms provide the best result for the minimization of fuel cost and SO2 emissions, and the nondominated sorting genetic algorithm II (NSGA-II) algorithm provides the worst result
The NPHC algorithm is compared with the SQP algorithm, which is based on a deterministic approach, and the NSGA-II algorithm, which is based on a stochastic approach
Summary
Power dispatch is a complicated task for the energy industry because a highly variable and unpredictable load demand from the customers needs to be satisfied using the most suitable (less expensive) mix of producers [1,2]. The characteristics of the producers are modeled by considering linear/nonlinear [11,13] or smooth/nonsmooth [11,14,15] objective functions, and by including valve point effects [12], ramp rate limits [16], generation limits [17], prohibited operating zones [18], spinning reserve [13], among others. These characteristics may turn the problem convex or nonconvex [11,18,19]
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