Resilient gas dependency–based planning of electricity distribution systems considering energy storage systems

This article presents a planning framework to improve the weather-related resilience of natural gas-dependent electricity distribution systems. The problem is formulated as a two-stage stochastic mixed integer linear programing model. In the first stage, the measures for distribution line hardening, gas-fired distributed generation (DG) placement, electrical energy storage resource allocation, and tie-switch placement are determined. The second stage minimizes the electricity distribution system load shedding in realized hurricanes to achieve a compromise between operational benefits and investment costs when the dependence of electricity distribution system on the natural gas exists. The proposed stochastic model considers random failures of electricity distribution system lines and random errors in forecasting the load demand. The Monte Carlo simulation is employed to generate multiple scenarios for defining the uncertainties of electricity distribution system. For the sake of simplicity, weather-related outages of natural gas pipelines are considered deterministic. The nonlinear natural gas constraints are linearized and incorporated into the stochastic optimization model. The proposed framework was successfully implemented on an interconnected energy system composed of a 33-bus electricity distribution system and a 14-node natural gas distribution network. Numerical results of the defined case studies and a devised comparative study validate the effectiveness of the proposed framework as well.

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Publication Details

Journal
Risk Analysis
Published
2024-12-26
DOI
https://doi.org/10.1111/risa.17695
Citations
2
Primary Topic
Integrated Energy Systems Optimization
Type
article
Field-Weighted Citation Impact
0.42
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article

Resilient gas dependency–based planning of electricity distribution systems considering energy storage systems

Mohammad Hassan Amirioun, Mohammad Amin Gilani, Mostafa Ghasemi
2 citations
Risk Analysis
Integrated Energy Systems Optimization
0.42
article

Resilient gas dependency–based planning of electricity distribution systems considering energy storage systems

Mohammad Hassan Amirioun, Mohammad Amin Gilani, Mostafa Ghasemi
article en
2 citations

Abstract

This article presents a planning framework to improve the weather-related resilience of natural gas-dependent electricity distribution systems. The problem is formulated as a two-stage stochastic mixed integer linear programing model. In the first stage, the measures for distribution line hardening, gas-fired distributed generation (DG) placement, electrical energy storage resource allocation, and tie-switch placement are determined. The second stage minimizes the electricity distribution system load shedding in realized hurricanes to achieve a compromise between operational benefits and investment costs when the dependence of electricity distribution system on the natural gas exists. The proposed stochastic model considers random failures of electricity distribution system lines and random errors in forecasting the load demand. The Monte Carlo simulation is employed to generate multiple scenarios for defining the uncertainties of electricity distribution system. For the sake of simplicity, weather-related outages of natural gas pipelines are considered deterministic. The nonlinear natural gas constraints are linearized and incorporated into the stochastic optimization model. The proposed framework was successfully implemented on an interconnected energy system composed of a 33-bus electricity distribution system and a 14-node natural gas distribution network. Numerical results of the defined case studies and a devised comparative study validate the effectiveness of the proposed framework as well.

Risk AnalysisVol. 45(7)
University of Isfahan (IR), Iran University of Science and Technology (IR)
Openalex Percentile: Top 36%
Integrated Energy Systems Optimization
0.42
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Resilient gas dependency–based planning of electricity distribution systems considering energy storage systems — Mohammad Hassan Amirioun, Mohammad Amin Gilani, et al. · Risk Analysis (2024) | TGRS Research Map | TGRS