DC constrained fuzzy power flow for transmission expansion planning studies

Abstract

Summary In restructured power systems, the adequacy of the transmission network may be defined as the ability to meet reasonable demands by transmission of electricity (as stated by the Directive 2009/72/EC). The symmetric/constrained fuzzy power flow (CFPF) was recently proposed as a suitable tool to quantify that adequacy. In this paper, the use of the symmetric fuzzy power flow/CFPF is extended to support the decision process of investment in network components to accomplish a specific adequacy criteria. A technique based on dual variables, obtained from the linear formulation of the CFPF, is used. The importance of the duality information concerning the adequacy indices is explained. The proposed methodology is applied on IEEE 14 bus reliability test system to demonstrate its applicability.

Transmission expansion planning in presence of electric vehicles at the distribution level

“…As a consequence, the dual variable progress (due to branch reinforcement actions) demonstrates also a nonlinear behavior. Figure 3 suggests a dual variable evolution for a generic node when the branch limit ( S ik ) is increased 14 . In CFPF, a dual variable regarding branch restrictions means a repression situation in a specific node.…”

“…In this paper, we use the constrained fuzzy power flow (CFPF) 13,14 to describe the consumption of EVs. In this approach, the uncertain demand is modeled using a membership function 15 .…”

Application of information gap decision theory in practical energy problems: A comprehensive review

“…These techniques are possibilistic-scenario based technique [5] and possibilistic-Monte Carlo technique [15]. 3…”

“…To resolve the issues mentioned above, various methods and techniques have been studied and utilized to control the outcomes caused by possible uncertainty in the behavior of parameters. Previously implemented techniques according to [2] are possibilistic technique [3], probabilistic technique [4], hybrid possibilisticprobabilistic techniques [5], robust optimization technique [6,7], information gap decision theory (IGDT) [8,9] and interval analysis [10]. The mentioned approaches and techniques are briefly illustrated in Fig.…”

“…To resolve the issues mentioned above, various methods and techniques have been studied and utilized to control the outcomes caused by possible uncertainty in the behavior of parameters. Previously implemented techniques according to [2] are possibilistic technique [3],…”