Multiobjective multistage distribution system planning using tabu search

Abstract

This study presents a multiobjective tabu search algorithm to solve the multistage planning problem of a distribution system formulated as a multiobjective dynamic mixed integer non-linear programming problem. Multiobjective problems do not have a specific solution, but a set of solutions that allows us to observe the trade-off among the analysed objectives. Taking into account this concept, the objective functions of the model proposed in this study are: costs (investment and operational) and reliability. The actions deemed in this model for each period of the planning horizon are: increase in the capacity of existing substations (or construction of new ones), exchange of cables in existing lines (and construction of new feeders), reconfiguration of the network, allocation of sectionalising switches and construction of tie lines. The system's reliability is evaluated by means of the non-supplied energy under contingencies using the n − 1 criterion. By line switching and the use of tie lines, part of the loads affected by a contingency can be restored, thus, the non-supplied energy can be evaluated by solving a distribution network restoration problem. Numerical results are presented for a 54-bus system.

Intelligent decision support algorithm for distribution system restoration

“…In order to overcome the limitations of decision conditions related to grey relational method and fuzzy logic tool limitations, the two decision making tools with incomplete and vague information are fused together to form a hybrid fuzzy-grey relational tool (Lin et al 2008; Basu and George 2014; Pereira Junior et al 2014; Shahsavari et al 2014; Liu et al 2015). This tool overcomes the limitations in the two methods, and make the decision making more relevant and effective.…”

A review of power distribution planning in the modern power systems era: Models, methods and future research

“…A PDP considering DG and uncertainties is solved by TS with an embedded Monte Carlo simulation based probabilistic power flow model [79]. Multiobjective TS solves a dynamic PDP [74]. Multiobjective reactive TS in combination with CHA and GA solve a PDP [54].…”

A Multi-Objective Distribution System Expansion Planning Incorporating Customer Choices on Reliability

“…Considering the technical requirements, the loading of substations must fall within an acceptable margin as shown in (13). Moreover, (14) and (15) ensure that electric consumers receive the proper supply with permissible voltage drop at load points and allowed thermal capacity limit in network branches [13]. Finally, (16) ensures that service reliability to each customer meets the minimum and maximum permissible values.…”

“…Despite aforementioned literature, some researchers have studied the DSP problem with conflicting objectives using multi-objective approaches [15]- [19]. In [15]- [17], the application of a multi-objective tabu search algorithm for DSP is evaluated; while the total planning costs and reliability are considered as objectives in [15] and [16], the objectives of [17] are modeled using fuzzy sets, including fuzzy economic cost, level of fuzzy reliability, and exposure.…”

“…In [15]- [17], the application of a multi-objective tabu search algorithm for DSP is evaluated; while the total planning costs and reliability are considered as objectives in [15] and [16], the objectives of [17] are modeled using fuzzy sets, including fuzzy economic cost, level of fuzzy reliability, and exposure. The strength of [15] over the other two is that it allocates both sectionalizing switches and the construction of tie lines. In [18], a dynamic programming approach for multi-objective optimal feeder routing is presented; the objectives are to simultaneously minimize total installation and operational costs, as well as interruption cost.…”