Notice of Violation of IEEE Publication Principles: A Benders Decomposition and Fuzzy Multicriteria Approach for Distribution Networks Remuneration Considering DG

Abstract

In this paper, the remuneration of fixed costs of distribution networks with distributed generation is evaluated by means of an efficient planning strategy that includes the concept of fuzzy robustness of a given solution plan. As a key contribution, it is included the possibility of choice among different conductor sizes in order to assess the deep costs by overcapacity introduced by distributed generation. A single-stage multicriteria nonlinear problem is stated using three different criteria: investment cost in lines and transformers or distributed generators, power losses cost and unserved energy cost. Pareto or efficient plans are identified using the -Constraint/Weighting method and solved by Benders decomposition algorithm.Uncertainty associated to load demand and power injections of distributed resources are integrated using a fuzzy power flow in order to obtain the robustness indexes of each Pareto solution. The annualized fixed charge rate (AFCR) associated to new and existing distribution lines and transformers or distributed generators is assessed through a with-without analysis permitting to compute the annual avoided charge rate (AACR) in order to send incentives or charges to distributed generation promoters by the avoided or added investment costs on the system. The model is programmed in GAMS mathematical modeling language. The effectiveness of the proposal is demonstrated through a real 201-node distribution network.

A probabilistic methodology for distributed generation location in isolated electrical service area

“…This optimization problem is a non-linear one, it contains binary variables (0, 1) that are the decision to install lines and generators and continuous positive variables that represents the ˇ power circulating on the lines [20]. This problem can be solved by deterministic or meta-heuristic mathematical optimization technique.…”

A practical eco-environmental distribution network planning model including fuel cells and non-renewable distributed energy resources

“…The dynamic of investments is eliminated from the proposed model and the new static model, referred to here by M1, is solved using the proposed solution algorithm. Such static model is similar to those models presented in [12,13]. In another alternative static model, referred to by M2, DG panning is eliminated from M1 which basically leaves network reinforcement as the only option to deal with the future load growth.…”

“…When many objectives are of interest, the problem is either translated to a single-objective problem (usually adding objectives into a single measure of performance [5]), or formulated as "true" multi-objective problem using Pareto optimality concept [11,10]. In static models, [12] and [13] consider network reinforcement along with DG investment. The value of multi-objective problem formulation of DG planning is that the objectives are usually in conflict or they can not be easily converted into a single-objective problem [14].…”

Possibilistic-Scenario Model for DG Impact Assessment on Distribution Networks in an Uncertain Environment

“…These models are even probabilistic (like [21] which assumes that a PDF is available for load values or [9] which describes the load values in discrete values with priory known probabilities) or possibilistic (like [1], [22]- [24]). Here, it assumed that no statistical data of load values is available.…”