Abstract
In the present climate, due to the cost of investments, pollutants of fossil fuel, and global warming, it seems rational to accept numerous potential benefits of optimal generation expansion planning. Generation expansion planning by regarding these goals and providing the best plan for the future of the power plants reinforces the idea that plants are capable of generating electricity in environmentally friendly circumstances, particularly by reducing greenhouse gas production. This paper has applied a teaching–learning-based optimization algorithm to provide an optimal strategy for power plants and the proposed algorithm has been compared with other optimization methods. Then the game theory approach is implemented to make a competitive situation among power plants. A combined algorithm has been developed to reach the Nash equilibrium point. Moreover, the government role has been considered in order to reduce carbon emission and achieve the green earth policies. Three scenarios have been regarded to evaluate the efficiency of the proposed method. Finally, sensitivity analysis has been applied, and then the simulation results have been discussed.
Highlights
It is evident that energy has always been an indispensable part of nations’ plans
9 different kinds of generation units are regarded in the case study of Iran in order to go to prove the efficiency of the Teaching–learning-based optimization algorithm (TLBO) algorithm unto another well-known way in the generation expansion planning (GEP) problem
From what has been discussed, it can be drawn that the GEP problem is one of the practical and economical ways to achieve planning goals such as distinct costs of the power plants and decreasing pollution generated by fossil-fuel power plants
Summary
It is evident that energy has always been an indispensable part of nations’ plans. Several decades ago, it used to supply energy for a limited number of household items . Considering the presence of renewable energy resources for environmental reasons in the future of a system is necessary, in [17], the differential evolution algorithm (DE) is exerted to GEP problem with the wind power plant for different goals. A new way which is called Bayesian network for the dynamic behavior of the system with considering uncertainties of renewable power plants in a multi-objective generation expansion planning decision model regarding permanent development. Game theory is applied to make a competitive situation for power plants to be able to achieve the maximum profit by providing the optimal strategy over the regarded period This scenario has been considered without applying any penalty for fossil fuel or subsidy for renewable resources. The lower limit Mmin is the slightest fraction of generating electricity by each kind of unit
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