An enhanced Borg algorithmic framework for solving the hydro-thermal-wind Co-scheduling problem

Abstract

An enhanced Borg (EBorg) algorithm has been proposed to solve the dual-objective short-term hydro-thermal-wind co-scheduling (HTW-CS) problem, aiming at minimizing the cost and emissions associated with electric power generation while satisfying various hydraulic and electric constraints. The sophisticatedly designed evolution framework of the EBorg consists of i) ε-dominance-based archive, ii) Pareto-dominance and crowding-distance-based population updating mechanism and iii) auto-adaptive multi-operator recombination, which guarantees the convergence capability and diversity and can avoid blindness selection of recombination operators. Meanwhile, a randomness-priority-based repairing constraint handling technique (CHT) is developed, and the performances of another two popular existing CHTs incorporated in the proposed search framework are compared and discussed. The proposed approaches are tested on the widely used HTW-CS case studies, and the results show that the proposed EBorg can achieve a decrease of cost and emissions compared with the existing methods. Additionally, energy performance in the case studies has shown an average 50% decrease of emissions with wind power integration, while the total cost is largely dependent on the cost coefficients related to wind uncertainty. Varying wind power cost coefficients and water inflow levels will result in different wind power and hydropower integration.