Probabilistic–proactive distribution network scheduling against a hurricane as a high impact–low probability event considering chaos theory

Abstract

High impact–low probability (HILP) incidents, such as hurricanes, usually and gravely damage electric distribution networks. A resilient distribution network must have an ability to recover itself with a fast restoration methodology against the effects of HILP events. Due to improve the electric distribution network resiliency against HILP incidents, this paper suggests a probabilistic–proactive distribution network operation model based upon the chaos theory (P‐PDNOMC(. Here, a resilience index based upon operational cost and load shedding cost is employed to decrease the effects of a hurricane as an HILP incident. P‐PDNOMC is formulated as a framework that consists of a novel hurricane modelling and operation scheduling in normal and emergency conditions with uncertainties considering. It should be noted that a prediction approach based on the chaos theory and the least‐squares support vector machine (LS‐SVM) is also designed to consider the provisional and spatial behaviour of the hurricane. Furthermore, this paper proposes a novel optimization framework for damaged lines determination based upon the P‐PDNOM C (DLsP‐PDNOM C) by considering multi‐zone and multi‐period damaged equipment budget constraints. Here, the load shedding cost is applied to present the efficiency of the proposed model. The numerical results indicate the resiliency enhancement of the electric distribution network in the face of HILP incidents.

Distributionally Robust Microgrid Formation Approach for Service Restoration Under Random Contingency

“…Additionally, (21) indicates that the active load shedding at node n should be less than the active load demand. Analogously, we define the reactive load shedding at node i 𝑠 𝑖 𝑞 orthogonal to 𝑠 𝑖 𝑝 as defined by (22). Finally, ( 23) and ( 24) restrict the power flow on the lines.…”

“…However, discrete uncertainties, such as random contingency (e.g., outage of transmission lines and generators), also pose a security risk to the system operation. Although extensive studies [6], [22] have been conducted to calculate the component failure probabilities under extreme events, it is still difficult to estimate an accurate probability distribution. To model uncertainties of contingency, [23] formulated a security evaluation subproblem to explore possible violations when considering wind/load variations and N-k contingencies.…”

Design of photovoltaic-energy storage-load forecasting and optimal control method for distributed distribution network based on reinforcement learning

“…With the increasing global energy demand and the rapid development of renewable energy, the photovoltaic-energy storage-load system of distributed distribution network has gradually become an important part of the future energy system. The wide application of photovoltaic power generation and energy storage technology provides a more flexible and sustainable solution for energy supply [1]. However, due to the instability and intermittence of renewable energy, the operation and management of distributed distribution network are facing a series of challenges [2].…”