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The linearized DC model is widely used in optimization of power systems but few studies evaluate the accuracy and feasibility of its solutions. This paper studies the source of errors in the linearized DC model and proposes three new models to improve its accuracy. In particular, it proposes a cold-start model capturing line losses and hot-starts models for approximating apparent power and more accurate phase angles. All of the models are linear programs and can easily be used as a building block for more complex applications. Experimental results on well-known benchmarks show the significant benefits in accuracy of the new models and the high correlation of their solutions with AC solutions. [ABSTRACT FROM PUBLISHER]

This paper studies the applicability of the linearized DC model in optimizing power restoration after significant network disruptions. In such circumstances, no AC base-point solution exists and the objective is to maximize the served load. The paper demonstrates that the accuracy of the linearized DC model degrades with the size of the disaster and that it can significantly underestimate active and apparent power. To remedy these limitations, the paper proposes an Angle-Constrained DC Power Flow (ACDCPF) model that enforces constraints on the line phase angles and has the ability to shed load and generation across the network. Experimental results on N-3 contingencies in the IEEE30 network and power restoration instances from disaster recovery show that the ACDCPF model provides significantly more accurate approximations of active and apparent power. In the restoration context, the ACDCPF model is shown to be much more reliable and produces significant reduction in the size of the blackouts. [ABSTRACT FROM PUBLISHER]