In recent years, the transmission network expansion planning (TNEP) problem has become increasingly complex. As this problem is a nonlinear and nonconvex optimization problem, researchers have traditionally focused on approximate models of power flows to solve the TNEP problem. Until recently, these approximations have produced results that are straightforward to adapt to the more complex problem. However, the power grid is evolving towards a state where the adaptations are no longer as easy (e.g., large amounts of limited control, renewable generation), necessitating new approaches. In this paper, we propose a discrepancy-bounded local search (DBLS) that encapsulates the complexity of power flow modeling in a black box that may be queried for information about the quality of a proposed expansion. This allows the development of an optimization algorithm that is decoupled from the details of the underlying power model. Case studies are presented to demonstrate cost differences in plans developed under different power flow models.

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Abstract-In recent years the grid expansion planning problem has become increasingly complex and challenging. The integration of renewable generation is a source of many of these challenges. These challenges often include a deficiency in transmission capacity in regions with high potential for renewable energy production. Historically, this lack of capacity has had adverse effects such as negative price market conditions or the curtailing of other green generation sources. This paper considers a combined generation and transmission expansion model to avoid the curtailment of existing green generation sources, in other words maximize the realized carbon reduction of adding renewable generation. Recent work on Randomized Constructive Heuristics (RCH) has shown this approach to be quite effective in addressing the Transmission Network Expansion Planning (TNEP) problem. In this paper, we propose a generalization of RCH to handle simultaneous carbon reduction and expansion cost objectives as well as multi-scenario planning.

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This article discusses interdependent energy infrastructure simulation system (IEISS). The primary function of IEISS is to enable homeland security analysts and decision makers to understand and accurately assess intrinsic vulnerabilities in critical US infrastructures and those vulnerabilities that result from other forms of attack. It provides a high‐quality, flexible, and extensible actor‐based approach that has been used for analyzing interdependent infrastructures; understanding and answering questions of importance to homeland security such as identifying potential events that cause catastrophic system failure; developing policy procedures that will aid in the prevention of physical terrorism.