Investment decision making in the energy sector is a complex process due to inherent long-term uncertainty. This work investigates importance of representing wide range economic and physical sources uncertainty modelling electricity market, both for investment descriptive market modelling. The results demonstrate that difference between deterministic stochastic solution increases non-linearly w...

In this paper, we study the long-term power generation investment expansion planing problem under uncertainty. We propose a bilevel optimization model that includes an upper-level multistage stochastic expansion planning problem and a collection of lower-level economic dispatch problem. This model seeks for the optimal sizing and siting for both thermal and wind power units to be built to maxim...

This paper proposes a novel method for transmission network expansion planning (TNEP) that take into account uncertainties in loads and renewable energy resources. The goal of TNEP is to minimize the expansion cost of candidate lines without any load curtailment. A robust linear optimization algorithm is adopted to minimize the load curtailment with uncertainties considered under feasible expan...

The paper presents a modeling framework to analyze some important issues associated with operation planning of a power system. Major activities involved in operations planning of large integrated power systems are considered simultaneously to ensure optimal utilization of generation and transmission capacity. The model also examines optimal transmission expansion plans vis-aÁ -vis fuel supply i...

This paper presents an application of parallel genetic algorithm to optimal long-range generation expansion planning. The problem is formulated as a combinatorial optimization problem that determines the number of newly introduced generation units of each technology during different time intervals. A new string representation method for the problem is presented. Binary and decimal coding for th...