Abstract
In the face of increasing natural disasters and an aging grid, utilities need to optimally choose investments to the existing infrastructure to promote resiliency. This paper presents a new investment decision optimization model to minimize unserved load over the recovery time and improve grid resilience to extreme weather event scenarios. Our optimization model includes a network power flow model which decides generator status and generator dispatch, optimal transmission switching (OTS) during the multi-time period recovery process, and an investment decision model subject to a given budget. Investment decisions include the hardening of transmission lines, generators, and substations. Our model uses a second order cone programming (SOCP) relaxation of the AC power flow model and is compared to the classic DC power flow approximation. A case study is provided on the 73-bus RTS-GMLC test system for various investment budgets and multiple hurricane scenarios to highlight the difference in optimal investment decisions between the SOCP model and the DC model, and demonstrate the advantages of OTS in resiliency settings. Results indicate that the network models yield different optimal investments, unit commitment, and OTS decisions, and an AC feasibility study indicates our SOCP resiliency model is more accurate than the DC model.
Highlights
B OTH aging infrastructure and the increasing frequency of weather-caused electrical grid outages threaten the resilience of our grid
An AC feasibility study is performed to further evaluate the optimal investment decisions obtained with the second order cone programming (SOCP) relaxation model versus the DC optimal power flow (OPF) model, which shows that the SOCP model investment decisions, optimal transmission switching (OTS), and generator dispatch decisions result in fewer locally-minimal over-voltage and power balance violations than the linear DC model
This paper presents a resiliency investment optimization model for determining optimal investments in the transmission grid to protect against extreme weather events using a recovery model based on a second order cone programming (SOCP) relaxation of AC power flow
Summary
B OTH aging infrastructure and the increasing frequency of weather-caused electrical grid outages threaten the resilience of our grid. We propose a model for choosing optimal investments in the existing transmission grid infrastructure to strengthen the resiliency of the system to weather-related scenarios, minimizing weighted unserved load in the network. Our resiliency investment model uses both optimal transmission switching (OTS) and the second order cone programming (SOCP) relaxation of the AC optimal power flow (OPF) model for the network. This means the model captures both real and reactive power flows, and voltages. We compare the optimal resiliency investments to those obtained when the model uses a linear power flow approximation (DC OPF), as in [5], [6]. We provide results justifying OTS in a resiliency setting and perform an AC feasibility study to demonstrate the optimal investment, unit commitment, and OTS decisions with the relaxed SOCP model are far more accurate compared to the linear DC model
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