Solving Dynamic Economic Emission Dispatch Problem with Uncertainty of Wind and Load Using Whale Optimization Algorithm

Abstract

Nowadays, the energy utilization deeply depends on fossil fuels about 70–90% against the whole energy. The environment is polluted due to global warming and air due to different gasses which motivates us to use clean and fresh renewable energy sources like solar energy and wind energy. Dynamic economic emission dispatch simultaneously minimizes total cost of energy and produced emissions in a 24-h time span. There with the stochastic dynamic economic emission dispatch (SDEED), problem is transferred to its corresponding deterministic dynamic economic emission dispatch. Thus, for solving the complex nonlinear non-smooth and non-differentiable SDEED, a new whale optimization algorithm (WOA) is introduced for minimization of overall cost and emission for wind operated thermal power system. Here the arbitrary character of wind power is designed with the use of probabilistic distribution function. The proposed approach has focused on the nature of encouraged meta-heuristic optimization algorithm where the social actions of hump back whale algorithm are considered. The results were obtained from the proposed optimization technique which proves about the effectiveness of WOA as compared to the other meta-heuristic algorithm. The uncertainty in wind power and load is considered in the cost model by including the power imbalance term such as overestimation and underestimation costs of the wind power available. To verify the efficiency of the proposed method, different technical constrains are considered. Further, the effect of wind power generation on dispatch, cost and emission is analyzed. A comparative analysis with other techniques is taking into account, and it was observed that the proposed method is more economical and accurate.