Scenario-based dynamic economic emission dispatch considering load and wind power uncertainties

This study protracts the multi-objective dynamic economic emission dispatch (DEED) problem by integrating wind power output of wind turbines. DEED simultaneously minimizes total electrical energy costs and emissions over a 24-h time span. In order to model the random nature of load demand and wind forecast errors, a scenario-based stochastic programming framework is presented. A scenario set is generated by a roulette wheel mechanism based on the probability distribution functions of these input uncertain variables. Therewith, the stochastic DEED (SDEED) problem is transformed into an equivalent deterministic scenario-based DEED. Thus, to solve the complicated nonlinear, non-smooth, and non-differentiable SDEED, an enhanced particle swarm optimization (PSO) algorithm is applied to obtain the best solution for the corresponding scenarios. In order to improve the quality of the solutions attained by PSO a self-adaptive probabilistic mutation strategy is used to escape from local minima. The proposed framework is successfully applied to four test systems with small, medium, and large-scale characteristics. In this regard, some metrics are used in order to characterize the effective performance of the solution method.

► This paper extends the dynamic economic emission dispatch (DEED) problem.
► In the DEED the total fuel cost and emissions are simultaneously minimized.
► A stochastic approach is proposed to model the hourly load and wind forecasts.
► An improved multi-objective PSO algorithm is implemented for optimization solution.