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.

A shared pool of grid-scale storage resources called Cloud Energy Storage (CES) can bring substantial benefits to the economical and reliable operation of MGs. However, the investment cost of CES may not be affordable for a single microgrid (MG). As a solution, we propose an approach in which neighboring microgrids in a distribution network collaborate and form a multi-microgrid (multi-MG) to install a shared CES to increase their profit and improve their reliability. Different investment scenarios are evaluated by considering the yearly reward from TSO and DSO. For each of the investment scenarios, TSO and DSO give a yearly reward based on the contribution of CES in peak-shaving and distribution network operation yearly cost reduction. Afterward, a decision table is provided in which, for all investment scenarios, profit, reliability index based on expected energy not supplied (ENS), and TSO-DSO yearly reward are determined. Finally, the microgrids select one of the investment scenarios using a multi-attribute decisionmaking approach. Simulation results of a case study validate the effectiveness of the proposed collaborative decision-making framework in increasing the economic value of CES investment, reliability enhancement in multi-MG, and peak-shaving.