Article
Energy & Fuels
Zhengmao Li, Yan Xu, Peng Wang, Gaoxi Xiao
Summary: This paper proposes a coordinated restoration method for the renewable energy-integrated multi-energy distribution system (MDS) to address the threats of low-probability but high-impact extreme events, such as floods, earthquakes, and hurricanes, to the security of the energy system. The method comprehensively models the MDS restoration with coupled power and thermal network constraints, utilizing thermal inertia and smart buildings' thermal demand response as buffers to reduce post-disaster energy supply cost. Preparation and load recovery stage measures are employed for efficient and reliable system restoration. Multiple uncertainties are dealt with through a risk-averse two-stage stochastic programming approach. Simulation results validate the effectiveness and superiority of the method.
Article
Engineering, Electrical & Electronic
Amid Shahbazi, Jamshid Aghaei, Sasan Pirouzi, Taher Niknam, Miadreza Shafie-khah, Joao P. S. Catalao
Summary: This paper introduces an optimal framework for resilience-oriented design in distribution networks to protect against extreme weather events and minimize associated costs. The approach considers various factors and utilizes advanced computational methods to achieve a globally optimal solution.
ELECTRIC POWER SYSTEMS RESEARCH
(2021)
Article
Engineering, Industrial
Javier Leon, Begona Vitoriano, John Hearne
Summary: Hazard reduction is a complex task that involves efforts to prevent and mitigate disaster consequences. Prescribed burning is a fuel management strategy to reduce wildfire hazard, but it impacts animal habitat and has uncertainties in scheduling. A mathematical programming model is proposed to schedule prescribed burns, considering uncertainty and safety criteria. The model aims to minimize worst-case achievement of criteria in different scenarios and is applied to a real case study in Andalusia, showing better performance than the risk-neutral solution.
Article
Engineering, Industrial
Erica Arango, Maria Nogal, Ming Yang, Helder S. Sousa, Mark G. Stewart, Jose C. Matos
Summary: The severe effects of recent extreme wildfire events have shown that simply suppressing fires is not enough. Instead, it is important to accept the inevitability of wildfire hazards and focus on building resilient systems. However, existing decision-making tools based on resilience have significant drawbacks. This paper proposes a new approach and methodology for assessing the resilience of road traffic networks to wildfires, addressing these drawbacks and considering the different functions of the system under various wildfire conditions. The methodology is demonstrated on five traffic networks and highlights the importance of appropriate wildfire management for enhancing the capacity of traffic networks to cope with wildfires.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Energy & Fuels
Mehdi Izadi, Seyed Hossein Hosseinian, Shahab Dehghan, Ahmad Fakharian, Nima Amjady
Summary: This paper presents a robust scheduling model based on information-gap decision theory (IGDT) to enhance the resilience of a distribution network exposed to wildfires. The model considers the impact of temperature and current on the thermal rating of transmission/distribution lines and accurately evaluates the impact of wildfires on the ampacity of tie-lines. By solving a multi-horizon IGDT-based optimization problem, a robust operation plan is found under a specific uncertainty budget. A posteriori out-of-sample analysis is used to find the best solution and the optimal uncertainty budget.
Article
Environmental Sciences
Matthew Hamilton, Jonathan Salerno, Alexandra Paige Fischer
Summary: The study evaluates the significance and function of feedback loops embedded within cognitive maps among stakeholders in a fire-prone region in the U.S. West. The findings indicate that cognition of feedback loops is limited among individuals but becomes prominent within groups, highlighting the importance of collaborative decision-making and identifying areas of cognitive biases.
GLOBAL ENVIRONMENTAL CHANGE-HUMAN AND POLICY DIMENSIONS
(2022)
Article
Green & Sustainable Science & Technology
Abodh Poudyal, Shiva Poudel, Anamika Dubey
Summary: Enhancing the resilience of power distribution systems to extreme weather events is critical. By upgrading the infrastructure and investing in smart grid technologies, grid resilience can be effectively enhanced.
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
(2023)
Article
Computer Science, Information Systems
Mostafa Ghasemi, Ahad Kazemi, Mohammad Amin Gilani, Miadreza Shafie-Khah
Summary: This study presents a new two-stage stochastic mixed-integer linear programming model for enhancing the resilience of distribution systems against natural disaster uncertainty. The model involves making investment decisions and creating dynamic microgrids to minimize the cost of loss of load in uncertain outage scenarios.
Article
Engineering, Electrical & Electronic
Fatemeh Rajaei, Mohammad Amin Latify, Akbar Ebrahimi
Summary: This paper proposes a resilience-oriented distribution system planning (RODP) model, SA-RODP, which takes into account the DSO's operational situational awareness to enhance system resilience and reduce investment costs. The model incorporates hardening measures and the installation of emergency distributed generation and energy storage systems. Decisions made in two stages enable the DSO to effectively respond to extreme weather events.
IEEE TRANSACTIONS ON SMART GRID
(2023)
Article
Engineering, Electrical & Electronic
Yu Lan, Qiaozhu Zhai, Xiaoming Liu, Xiaohong Guan
Summary: The multistage economic dispatch solution is crucial for achieving optimal unit commitment and decentralized decision-making in smart grid operations. The stochastic dual dynamic programming (SDDP) has been effective in solving the multistage stochastic programming (SP) problem, but lacks a guarantee of finite termination. To address this, we propose a fast stochastic dual dynamic programming (FSDDP) method that accelerates convergence rate by updating candidate points based on the maximum difference between upper and lower bounds of optimal value functions, adding a backward-forward-backward inner scheme, and initializing bounds with finite constant values. Numerical tests on various distribution systems demonstrate that FSDDP can significantly improve computational efficiency while approaching optimal economic performance.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2023)
Article
Engineering, Electrical & Electronic
Zhenning Pan, Tao Yu, Wenqi Huang, Yufeng Wu, Junbin Chen, Kedong Zhu, Jidong Lu
Summary: This paper proposes an approximate dynamic programming with imitation learning (ADP-IL) based real-time dispatch policy for integrated electricity and thermal system (IETS) with electrical and thermal storages. The algorithm reformulates the real-time dispatch problem as a multistage stochastic sequential optimization and addresses non-convex terms using mixed integer programming. It incorporates off-line pre-learning and imitation learning to improve computation efficiency and solution quality. Comprehensive studies verify the optimality, adaptability, efficiency, and scalability of ADP-IL.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2023)
Review
Energy & Fuels
Ching-Ming Lai, Jiashen Teh
Summary: The dynamic thermal rating (DTR) system safely determines the thermal limits of power components based on environmental conditions. However, existing reviews of the DTR system only focus on transmission lines and networks, neglecting the thermal limits of transformers and distribution cables, and have limited research themes. This review article addresses these drawbacks by collecting and categorizing existing research articles, providing a comprehensive summary for prospective researchers of the DTR system.
Article
Engineering, Electrical & Electronic
Jianquan Zhu, Jiajun Chen, Yelin Zhuo, Xiemin Mo, Ye Guo, Linpeng Liu, Mingbo Liu
Summary: The energy management of active distribution network under uncertainties is a challenging stochastic, nonconvex, and nonlinear problem. This paper proposes an improved approximate dynamic programming algorithm to simplify the problem by decomposing it into subproblems and solving them successively. By directly obtaining approximate value functions using the Galerkin method, the algorithm eliminates the need for iterative value function updates and accelerates the process by evaluating the influence of each basis function on the approximate value function.
IEEE TRANSACTIONS ON SMART GRID
(2022)
Article
Computer Science, Information Systems
Sergio F. Santos, Matthew Gough, Desta Z. Fitiwi, Jose Pogeira, Miadreza Shafie-khah, Joao P. S. Catalao
Summary: This paper presents an improved model for dynamic distribution system reconfiguration (DNSR) that optimizes system operation in a coordinated manner, considering DRES, ESS, and DNSR as well as the uncertainty of these resources. The results show that using DNSR, DRES, and ESS can significantly reduce energy demand and lead to a healthier, more efficient, and higher quality system.
IEEE SYSTEMS JOURNAL
(2022)
Article
Engineering, Electrical & Electronic
Bruno Colonetti, Erlon Finardi, Samuel Brito, Victor Zavala
Summary: Unit commitment is a complex problem in power system operations that has yet to be fully solved. Operators currently use optimization solvers and simplifications to address the problem, but solving it in a timely manner remains a challenge. This study proposes a parallel dynamic integer programming approach for solving the unit commitment problem, which has been successfully applied to different power systems with impressive speed-ups compared to sequential strategies.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2023)