Stochastic multi-objective optimization of combined heat and power economic/emission dispatch

In this paper, an optimization technique based on time-varying acceleration particle swarm optimization (TVAC-PSO) is applied for optimizing economic dispatch and emission reduction problems in a system consisting of CHP units. At first algorithm is used for deterministic model that for comparison with previous works. Besides, its convergence speed is adequately high. Then Monte Carlo method is implemented to solve the stochastic model providing real situation. In the Monte Carlo simulation you have a range of values as a result, and you are able to understand the risk and uncertainty in the model. Deterministic models used for conventional economic dispatch problems are not able to address some real conditions in practical applications. Deterministic models of power system applications are incapable to express the uncertainties that come from energy demand and supply of intermittent renewable systems. Due to the presence of these various uncertain variables stochastic attitude is inevitable in practical applications. Therefore, solving economic dispatch and emission reduction problems simultaneously and stochastically that is the main objective of this paper has a great level of importance. For modeling accurately, both power transmission losses and nonlinear fuel cost function are considered. The proposed method is applied to a 7-unit test system. •A multi-objective economic dispatch and emission reduction model was developed.•A time-varying acceleration particle swarm optimization (TVAC-PSO) was applied.•A stochastic approach was implemented by Monte Carlo method.•Stochastic model is more appropriate for demand and environment uncertainties.