Sajad Najafi Ravadanegh - Smart Distribution Grid Research Lab., Electrical Engineering Department, Azarbaijan Shahid Madani University, Tabriz, Iran
Sajad Kouhi - Smart Distribution Grid Research Lab., Electrical Engineering Department, Azarbaijan Shahid Madani University, Tabriz, Iran
Mohammad Reza Jannati Oskuee - Smart Distribution Grid Research Lab., Electrical Engineering Department, Azarbaijan Shahid Madani University, Tabriz, Iran

The conventional economic load dispatch (ELD) of power generation involves allocation of power generation to various thermal units to minimize the operation costs; it means that conventional economic load dispatch cannot meet the environmental protection requirements, since it only considers minimizing the total fuel costs. But, regards to stricter regulations on environmental issues, some strategies must be devised in order to reduce the pollutant emissions produced by fossil-fueled electric power plants. In this regards, emission reduction must be defined as an objective function in ELD problem. Also, the economic load dispatch considering transmission system losses can reasonably improve real power dispatch. In order to minimize the power loss, another objective is defined for this purpose. So, in this paper in order to operate the power plants in the best possible condition multi-objective functions are proposed and have been solved using NSGA II (Non-Dominated Sorting Genetic Algorithm). The employed objectives contain all economical and environmental and technical aspects with considering the constraints. Also, the power system enforced with uncertainty of the load forecasting. This issue must be considered in operation of power system in the case of probabilistic environmental/economic dispatch (PEED) problem. Therefore, this paper presents a strategy for solving of PEED. To validate the effectiveness of proposed scheme, the simulation results are provided.

Probabilistic Environmental/Economic Dispatch, Multi-Objective, Environmental Issues, Transmission Loss, Monte Carlo Simulation