A novel meta-heuristic model for the multi-year transmission network expansion planning

Probabilistic decomposition‐based security constrained transmission expansion planning incorporating distributed series reactor

“…Indeed, the independent variable for total number of existed and installed lines is n i j not n i j c . However, the proposed model uses the binary variables n i j c to calculate the load flow of each parallel line of each corridor as utilised in (10)- (12) and 17- (19).…”

“…Here, for the sake of simplicity the index s is dropped. Therefore, in addition to the non-linearity caused by DSR in (11), (12), (18), and (19), the huge number of required scenarios for the proposed probabilistic formulation is another challenge, which makes the problem size very large and difficult to be solved by MIP solvers. In the following section, a novel solution algorithm is proposed to overcome these issues.…”

“…Step 3: Knowing network configuration and uncertain parameters, in the next step, a DC load flow is performed to determine the lines' power flow directions, which is used to overcome the non-linearity caused by (11), (12), (18) and (19). It is noted that this assumption that the direction of power flow after incorporation of DSRs does not change might not be always true and may lead to a sub-optimal solution.…”

“…A wide range of probabilistic models has been proposed, generally categorised as robust [14][15][16][17] and stochastic [18][19][20] models. These models may utilise mathematical techniques such as Benders decomposition (BD) [14,17,18], minmax optimisation [15], or meta-heuristic algorithms such as tabu search [16], ant colony [19], and hybrid approaches [20]. Among these solution methodologies, the BD technique has attracted a great interest since it is able to perform contingency analysis without enlarging the problem size and without sacrificing the optimality of the problem solution.…”

Efficient sensitivity based approach for AC transmission expansion planning

“…A variation of evolutionary programming with a cultural algorithm is used to solve AC TEP in [28]. A multistage AC TEP is solved by PSO in [29]. In general, meta‐heuristic approaches are population based and require good choice of parameters and variance in the population pool to provide an optimal solution.…”