AN ACCELERATED BENDERS DECOMPOSITION ALGORITHM FOR SUSTAINABLE POWER GENERATION AND TRANSMISSION EXPANSION PLANNING UNDER UNCERTAINTY CONSIDERING

Integrated planning of power generation,transmission expansion is very complicated in the presence of uncertainties in future electricity demand,fuel prices,greenhouse gas emissions,and disturbances. It becomes more complex whenever several sustainability policies related to greenhouse gas emissions,allowable noise level,and social acceptance are adopted. These policies significantly influence the total operational cost,network configuration of a power system. Hence,the managers of power systems should carefully decide on such policies,then precisely apply them to the planning phase. There are optimization models for integrated expansion planning of power systems in such situations,however,they cannot be solved exactly,efficiently in practice. This may produce very misleading insights into the impact of different sustainability policies since the accuracy level of optimization procedure is unknown. To fill this research gap,this paper presents an efficient exact algorithm for an existing multi-stage stochastic programming model that is developed for integrating two planning tasks of generation,transmission expansion for a centralized power system. The model considers the disruptionrisk,all the three sustainability aspects: economic,social,and environmental. The algorithm is developed based on Benders decomposition,and enhanced by acceleration techniques where multi-cut optimality cuts are used. The algorithm initially solves the relaxation of the master problem to find a good feasible solution using a rounding algorithm combined with a scenario selection procedure. The rounding algorithm is first used to determine the fixed first-stage variables,and then the deterministic equivalent model is solved for the selected scenarios to determine the unfixed first-stage variables. The resulting solution provides a set of effective cuts for the master problem,consequently better bounds in the next iterations. The computational results show the efficiency of the algorithm when compared with the solution method that directly solves the extended equivalent form of the two-stage model using exiting mixed-integer linear programming solvers. The proposed Benders decomposition algorithm enables us to practically find optimal solutions for cases with a large number of uncertainty scenarios. The results for a case study in Iran are also included.