Abstract
Reliability is an essential factor in distribution networkt expansion planning. However, standard distribution reliability assessment techniques rely on quantifying the impact of a pre-specified set of events on service continuity through the simulation of component outages, one at a time. Due to such a simulation-based nature, the incorporation of reliability into distribution network expansion planning has customarily required the application of heuristic and metaheuristic approaches. Recently, alternative mixed-integer linear programming (MILP) models have been proposed for distribution network expansion planning considering reliability. Nonetheless, such models suffer from either low computational efficiency or over-simplification. To overcome these shortcomings, this paper proposes an enhanced MILP model for multistage reliability-constrained distribution network expansion planning. Leveraging an efficient, yet accurate reliability evaluation model, proposing a customized technique for effectively imposing radial operation, as well as utilizing pragmatic measures to model reliability-related costs are the salient features of this work. In this respect, practical reliability-related costs are considered based on reliability incentive schemes and the revenue lost due to undelivered energy during customer outages. The proposed planning approach is tested on four networks with 24, 54, 86, and 138 nodes to illustrate its efficiency and applicability.
Highlights
W ITH a share of over 90%, distribution systems are responsible for the majority of electric power outages [1]
Under such policies, considering reliability in multistage distribution network expansion planning is crucial to improve the quality of service provision to consumers [4], [5]
Such benefits include 1) reducing the loss of revenue due to unserved energy during customer interruptions, 2) decreasing the financial penalties imposed by distribution system regulators in the case of poor service reliability, and even 3) receiving bonuses offered by the regulators if the reliability level is higher than a specific threshold [2], [3], [6]
Summary
W ITH a share of over 90%, distribution systems are responsible for the majority of electric power outages [1]. 2) In contrast to recent applications of mixed-integer linear programming [24], [31]–[33], the proposed approach features the following major distinctive aspects: 1) compared to [31], the number of variables and constraints is significantly reduced with identical modeling capability, thereby giving rise to substantial computational superiority, as empirically evidenced, 2) the issues associated with [24] and [32] are overcome by precisely handling switching interruptions, which is of utmost relevance in terms of solution quality [26], and 3) unlike [33], computational tractability is gained without resorting to simplifications related to radiality, transfer nodes, and demand characterization. The main contributions of this paper are twofold: 1) On the modeling side, an alternative and computationally efficient formulation is presented for multistage reliability-constrained distribution network expansion planning wherein analytical reliability assessment including the effect of switching interruptions, radial operation, and reliability incentive schemes is explicitly cast in terms of the optimization variables. All branches are equipped with a switch that enables
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