Hybrid stochastic/robust optimization model for resilient architecture of distribution networks against extreme weather conditions

2021 ◽  
Vol 126 ◽  
pp. 106576
Author(s):  
Amid Shahbazi ◽  
Jamshid Aghaei ◽  
Sasan Pirouzi ◽  
Miadreza Shafie-khah ◽  
João P.S. Catalão
Keyword(s):  
Robust Optimization ◽  
Optimization Model ◽  
Weather Conditions ◽  
Distribution Networks ◽  
Extreme Weather

A Bi-level Interval Robust Optimization Model for Service Restoration in Flexible Distribution Networks

2020 ◽  
pp. 1-1
Author(s):  
Wenxia Liu ◽  
Mengdi Fu ◽  
Mengyao Yang ◽  
Yanhui Yang ◽  
Lingfeng Wang ◽  
...  
Keyword(s):  
Robust Optimization ◽  
Optimization Model ◽  
Distribution Networks ◽  
Service Restoration ◽  
Flexible Distribution

scholarly journals Distributionally Robust Distributed Generation Hosting Capacity Assessment in Distribution Systems

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 2981 ◽  
Author(s):  
Mohammad Seydali Seyf Abad ◽  
Jin Ma ◽  
Ahmad Ahmadyar ◽  
Hesamoddin Marzooghi
Keyword(s):  
Robust Optimization ◽  
Distributed Generation ◽  
Optimization Model ◽  
Distribution Systems ◽  
Historical Data ◽  
Distribution Networks ◽  
Distributionally Robust Optimization ◽  
Distributed Generations ◽  
Distributionally Robust ◽  
The Impact

Uncertainties associated with the loads and the output power of distributed generations create challenges in quantifying the integration limits of distributed generations in distribution networks, i.e., hosting capacity. To address this, we propose a distributionally robust optimization-based method to determine the hosting capacity considering the voltage rise, thermal capacity of the feeders and short circuit level constraints. In the proposed method, the uncertain variables are modeled as stochastic variables following ambiguous distributions defined based on the historical data. The distributionally robust optimization model guarantees that the probability of the constraint violation does not exceed a given risk level, which can control robustness of the solution. To solve the distributionally robust optimization model of the hosting capacity, we reformulated it as a joint chance constrained problem, which is solved using the sample average approximation technique. To demonstrate the efficacy of the proposed method, a modified IEEE 33-bus distribution system is used as the test-bed. Simulation results demonstrate how the sample size of historical data affects the hosting capacity. Furthermore, using the proposed method, the impact of electric vehicles aggregated demand and charging stations are investigated on the hosting capacity of different distributed generation technologies.

scholarly journals MULTIOBJECTIVE OPTIMIZATION MODEL FOR SCHEDULING OF CONSTRUCTION PROJECTS UNDER EXTREME WEATHER

2016 ◽  
Vol 22 (3) ◽  
pp. 373-381 ◽  
Author(s):  
Ahmed B. SENOUCI ◽  
Saleh A. MUBARAK
Keyword(s):  
Optimization Model ◽  
Construction Projects ◽  
Weather Conditions ◽  
Extreme Weather ◽  
Construction Time ◽  
Multi Objective ◽  
Trade Offs ◽  
The Impact ◽  
Construction Schedules ◽  
Cost Of Construction

Extreme weather significantly impacts construction schedules and costs and can be a source of schedule de­lays and budget overruns. A multi-objective optimization model, presented herein for the scheduling of construction projects under extreme weather conditions, can generate optimal/near optimal schedules that minimize the time and cost of construction projects in extreme weather regions. The model computations are organized as follows: (1) a scheduling module for developing practical schedules for construction projects, (2) a cost module for computing total project cost, and (3) a multi-objective module for determining optimal/near optimal trade-offs between project time and cost. Two practical examples of the effects of extreme weather on construction time and direct cost are provided, the first of which shows the impact of extreme weather on construction time and cost, and the second of which demonstrates the ability of the model to generate and visually present the optimal trade-offs between the duration and costs of construction projects under extreme weather conditions.

Robust Optimization Model for Runway Configurations Management

2014 ◽  
Vol 5 (3) ◽  
pp. 1-26 ◽  
Author(s):  
Rui Zhang ◽  
Rex Kincaid
Keyword(s):  
New York ◽  
Robust Optimization ◽  
Optimization Model ◽  
Weather Conditions ◽  
Capacity Constraints ◽  
Computational Experience ◽  
Test Case ◽  
Management Problem ◽  
International Airport ◽  
Demand Pattern

The Runway Configuration Management problem governs what combinations of airport runways are in use at a given time for an airport or a collection of airports. Runway configurations (groupings of runways), operate under Runway Configuration Capacity Envelopes (RCCEs) which limit arrival and departure capacities. The RCCE identifies unique capacity constraints based on which runways are used for arrivals, departures, and their direction of travel. When switching between RCCEs, due to a change in weather conditions or a change in the demand pattern, a decrement in arrival and departure capacities is incurred during the transition. This paper reports computational experience with two distinct models—a robust optimization model that addresses uncertainty in the arrival demand, and a previously studied model that does not include uncertainty in any of the parameters. Test case scenarios are based on data from the John F. Kennedy international airport in New York.

Resilience of Electricity Distribution Networks Against Extreme Weather Conditions

2017 ◽  
Vol 3 (2) ◽  
Author(s):  
Kim Forssén ◽  
Kari Mäki ◽  
Minna Räikkönen ◽  
Riitta Molarius
Keyword(s):  
Distribution System ◽  
Situational Awareness ◽  
Weather Conditions ◽  
Distribution Networks ◽  
Extreme Weather ◽  
Electricity Supply ◽  
Electricity Distribution ◽  
Overhead Lines ◽  
Electrical Grid ◽  
Adverse Weather

Extreme weather forms a major threat to electricity distribution networks and has caused many severe power outages in the past. A reliable electrical grid is something most of us take for granted, but storms, heavy snowfall, and other effects of extreme weather continue to cause disruptions in electricity supply. This paper contributes to ensuring the continuity of electricity supply under adverse weather events. The aim is to describe and to analyze how the continuity of electricity supply can be ensured in the case of extreme weather. Based on the research, the energy sector is highly dependent on the existing locations and structures of the current infrastructure. Aging infrastructure is commonly seen as a main vulnerability factor. The most vulnerable parts of the electricity distribution system to extreme weather conditions are the networks built as overhead lines. However, the resilience of the networks against extreme weather can be increased significantly in all phases of a disaster management cycle. Methods and technological solutions proposed in this paper to alleviate such problems include adjacent forest management and periodic aerial inspections, situational awareness, distributed generation and microgrids, placement of overhead lines, underground cabling, and unmanned air vehicles. However, it must be noticed that the methods and their value for stakeholders are context-dependent. Thus, their applicability and appropriateness may change over time.

scholarly journals Precipitation Modeling for Extreme Weather Based on Sparse Hybrid Machine Learning and Markov Chain Random Field in a Multi-Scale Subspace

Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1241
Author(s):  
Ming-Hsi Lee ◽  
Yenming J. Chen
Keyword(s):  
Machine Learning ◽  
Markov Chain ◽  
Random Field ◽  
Long Range ◽  
Weather Conditions ◽  
Extreme Weather ◽  
Prediction Algorithm ◽  
Learning Method ◽  
Multi Scale ◽  
Hybrid Machine

This paper proposes to apply a Markov chain random field conditioning method with a hybrid machine learning method to provide long-range precipitation predictions under increasingly extreme weather conditions. Existing precipitation models are limited in time-span, and long-range simulations cannot predict rainfall distribution for a specific year. This paper proposes a hybrid (ensemble) learning method to perform forecasting on a multi-scaled, conditioned functional time series over a sparse l1 space. Therefore, on the basis of this method, a long-range prediction algorithm is developed for applications, such as agriculture or construction works. Our findings show that the conditioning method and multi-scale decomposition in the parse space l1 are proved useful in resisting statistical variation due to increasingly extreme weather conditions. Because the predictions are year-specific, we verify our prediction accuracy for the year we are interested in, but not for other years.

Multi-objective resilience enhancement program in smart grids during extreme weather conditions

2021 ◽  
Vol 129 ◽  
pp. 106824
Author(s):  
Rahman Ashrafi ◽  
Meysam Amirahmadi ◽  
Mohammad Tolou-Askari ◽  
Vahid Ghods
Keyword(s):  
Smart Grids ◽  
Weather Conditions ◽  
Extreme Weather ◽  
Multi Objective
Sign in / Sign up

Export Citation Format

Share Document