Tie‐line planning for resilience enhancement in unbalanced distribution networks

Abstract

Over the past decades, there has been a dramatic increase in the frequency of natural disasters, which are the leading causes of large-scale power outages. This paper, therefore, assesses the significance and role of optimal tie-line construction in improving the service restoration performance of unbalanced power distribution systems in the aftermath of high-impact low-probability incidents. In doing so, a restoration process aware stochastic mixed-integer linear programming model is developed to find the optimal locations for new tie-line construction in unbalanced three-phase distribution systems. In particular, the restoration process of distribution systems, including the fault isolation and system reconfiguration, is contemplated to place tie-lines in the most proper locations to enhance the manoeuvring capability of distribution systems and reducing the customer interruption time. Furthermore, the model is a stochastic one wherein uncertainty associated with potential damages alongside demand uncertainty is captured via a set of likely scenarios. To validate the effectiveness of the proposed stochastic mixed-integer linear programming model, it is tested and verified on the IEEE 13-and 123-bus test systems.

A risk‐based resilient distribution system planning model against extreme weather events

“…Constraint () determines the allowable range of voltage magnitudes. The linear relationship between active, reactive, and apparent power transfer on different distribution system feeder sections is presented in () to () [26]. $$\begin{equation}\Delta {\theta _{k,m,t,s}} \le M(1 - {u_{k,m,t,s}} + z_{k,m}^{h.})\end{equation}$$ $$\begin{equation}\Delta {\theta _{k,m,t,s}} \ge - M(1 - {u_{k,m,t,s}} + z_{k,m}^{h.})\end{equation}$$ $$\begin{equation}\Delta {v_{k,m,t,s}} \le M(1 - {u_{k,m,t,s}} + z_{k,m}^{h.})\end{equation}$$ $$\begin{equation}\Delta {v_{k,m,t,s}} \ge - M(1 - {u_{k,m,t,s}} + z_{k,m}^{h.})\end{equation}$$ …”

“…Constraint (25) determines the maximum and minimum amount of allowable load shedding for reactive power in different load points. Constraint (26) determines the voltage magnitude of different busses based on their voltage difference. Constraint (27) determines the allowable range of voltage magnitudes.…”

“…Constraint (27) determines the allowable range of voltage magnitudes. The linear relationship between active, reactive, and apparent power transfer on different distribution system feeder sections is presented in (28) to (31) [26].…”

An MILP model for risk‐based placement of RCSs, DDGs, and tie lines in distribution systems with complex topologies

“…Constraints (27)–(30) determine the linear relationship between active, reactive, and apparent power of tie lines [28]. Moreover, they link the tie line cost in (11) to the power flow equations in (17) and (20) using$$\Gamma _{k,m}^{T.L.}$$.…”

Improving the resilience of the distribution system using the automation of network switches

“…Resilience is defined as the ability of a system to maintain an acceptable level of performance against a severe disturbance and recover over an appropriate period of time. Therefore, evaluating the resilience of the network and its reversibility ability to deal with fault conditions and reducing the effectiveness of the electricity distribution network in the event of accidents should be among the planning priorities for the design and operation of the network [2].…”