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Robust Resilience Enhancement by EV Charging Infrastructure Planning in Coupled Power Distribution and Transportation Systems

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IEEE TRANSACTIONS ON SMART GRID 2025年第1期16卷 491-504页

作者： Wen, Jianfeng Gan, Wei Chu, Chia-Chi Jiang, Lin Luo, Jiajie Univ Liverpool Dept Elect Engn & Elect Liverpool L69 3GJ England Univ Cardiff Sch Engn Hsinchu 30013 Taiwan Cardiff Univ Sch Engn Cardiff CF24 3AA Wales Arup Power Syst Consultant Dept Birmingham B3 3AY England

Due to the recent rapid developments in fast charging technology for electric vehicles (EVs), these flexible mobile storage resources can provide auxiliary services to the power grid in emergency circumstances. Therefore, it is imperative to develop a resilient enhancement planning scheme for this coupled network under severe contingencies. To this end, this paper investigates a novel robust resilient enhancement scheme for planning charging infrastructure in coupled networks. The objective is to minimize both (i) the investment and operation cost of the coupled network under uncertain traffic demands, and (ii) the EV participation cost for the grid support scheme during contingencies. The investment scheme for power distribution lines and charging stations is determined before the uncertainty realization in the first stage, while the objective function is minimized in the worst possible manner within a specified uncertainty set in the second stage. The nested column-and-constraint generation (NC&CG) algorithm is applied to solve this robust optimization problem. Numerical simulations of two coupled networks are conducted to demonstrate the effectiveness of the proposed robust resilience enhancement scheme

关键词： Planning Costs Resilience Vehicle-to-grid Investment Contingency management Power distribution lines Electric vehicles fast charging stations robust resilience enhancement coupled traffic and power networks nested column-and-constraint generation algorithm

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Two-Stage Robust Optimization Model for Capacity Configuration of Biogas-Solar-Wind Integrated Energy System

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2023年第1期59卷 662-675页

作者： Li, Xiaoqing Zhang, Lizhi Wang, Ruiqi Sun, Bo Xie, Weijian Shandong Univ Sch Control Sci & Engn Jinan 250061 Peoples R China State Grid Tianjin Elect Power Co Chengxi Power Supply Branch Tianjin 300190 Peoples R China State Grid Shandong Integrated Energy Serv CO LTD Jinan 250001 Peoples R China

Biogas-solar-wind integrated energy systems are effective for optimizing rural energy consumption and improving agricultural production. The performance of an integrated energy system generally depends on its capacity configuration. However, the uncertainties of renewable energy sources and loads deepen the coupling relationship between the capacity configuration and energy dispatching of integrated energy systems, which makes optimizing the capacity difficult. We propose a two-stage robust optimization model for the capacity configuration of a biogas-solar-wind integrated energy system that is applicable to rural areas. First, a framework of the biogas-solar-wind integrated energy system was designed and diverse evaluation indices were introduced. Then, the integrated energy system model was transformed into a two-stage robust optimization problem, where the column-and-constraint generation algorithm is used to solve the installed capacity problem of the system equipment in the first stage, and the nested column-and-constraint generation algorithm is used to optimize the energy dispatching schedule in the second stage. Finally, the proposed model was applied to a rural area in China to confirm the rationality and effectiveness of the optimization results.

关键词： Resistance heating Uncertainty Wind turbines Heat recovery Optimization Costs Load modeling Capacity configuration integrated energy system nested column-and-constraint generation algorithm two-stage robust optimization

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An Adjustable Robust Optimization Approach for the Expansion Planning of a Virtual Power Plant 5

An Adjustable Robust Optimization Approach for the Expansion...

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5th International Conference on Smart Energy Systems and Technologies (SEST)

作者： Baringo, Ana Baringo, Luis Arroyo, Jose M. ETSI Ind Univ Castilla La Mancha Dept Ingn Elect Elect Automat & Comunicac Ciudad Real Spain

ISBN: (数字)9781665405577

ISBN: (纸本)9781665405577

This paper proposes a novel approach based on adjustable robust optimization for the expansion planning of a virtual power plant (VPP) that participates in the energy electricity market. The VPP comprises conventional, renewable, and storage units, as well as flexible demands, and analyzes the possibility of building new conventional, renewable, and storage units with the aim of maximizing its profit. The uncertainty related to future production costs of the conventional generating units, future consumption levels of the flexible demands, and future energy market prices is modeled using confidence bounds and uncertainty budgets. The resulting model is formulated as a trilevel program with lower-level binary variables that is solved using a nested column-and-constraint generation algorithm. Results from a case study show the effective performance of the proposed approach.

关键词： Adjustable robust optimization expansion planning nested column-and-constraint generation algorithm uncertainty virtual power plant

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A two-stage robust generation expansion planning framework for regional integrated energy systems with carbon growth constraints

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FRONTIERS IN ENERGY RESEARCH 2023年 11卷

作者： Nan, Junpei Feng, Jieran Deng, Xu Guan, Li Sun, Ke Zhou, Hao Zhejiang Univ Coll Elect Engn Hangzhou Peoples R China South China Univ Technol Sch Elect Power Engn Guangzhou Peoples R China State Grid Corp China Natl Power Dispatching & Control Ctr Beijing Peoples R China State Grid Zhejiang Elect Power Co Ltd Hangzhou Peoples R China

After proposing the carbon peaking and carbon neutrality target, China further proposed a series of specific carbon emission growth limit sub-targets. How to decarbonize the energy system to ensure the realization of the carbon growth limit sub-targets is a meaningful topic. At present, generation expansion planning of renewable energy in integrated energy systems has been well studied. However, few of the existing studies consider specific carbon emission growth targets. To address this research gap, a two-stage robust generation expansion planning framework for regional integrated energy systems with carbon growth constraints is proposed in this paper, which takes into account multiple uncertainties. In this framework, the objective function is to minimize the total operation cost and wind turbine investment cost. The first stage is the decision-making level of the wind turbine capacity configuration scheme. The second stage is the optimal economic dispatching in the worst-case scenario, which is a bi-level problem of max-min form. Thus, the two-stage robust optimization framework constitutes a problem of min-max-min form, which is pretty hard to solve directly with a commercial solver. Therefore, a nested column-and-constraint generation algorithm is adopted and nested iterations are performed to solve the complex problem. Finally, case studies are carried out on a regional electric-gas integrated energy system. The MATLAB/YALMIP simulation platform with the Gurobi solver is used to verify the effectiveness and superiority of the proposed framework. Compared with other four cases, 5,000 Monte Carlo scheduling tests demonstrate that the proposed framework can ensure the system carbon emission to be controlled within a certain limit even in the worst scenario. Due to the consideration of multiple uncertainties, the proposed framework planning results are both robust and economical for investment. This study can provide theoretical support for the actual regional in

关键词： carbon emission carbon growth constraints electric-gas integrated energy system generation expansion planning nested column-and-constraint generation algorithm two-stage robust optimization

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A comprehensive strategy for the distribution network resilience enhancement considering the time-varying behaviors of typhoon path

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ELECTRIC POWER SYSTEMS RESEARCH 2023年第PartA期214卷

作者： Huang, Zhanghao Zhang, Yachao Xie, Shiwei Fuzhou Univ Sch Elect Engn & Automat Fuzhou City Fujian Province Peoples R China

Typhoon, as a high-impact and low-probability extreme event, can damage the components in power networks, thereby resulting in power outages or blackouts. To enhance the distribution network resilience against typhoon attacks, this paper proposes a resilience-oriented two-stage robust optimization model, in which resilience-constrained unit commitment schemes and planning-operational restoration measures are incorporated into a prevention and emergency response framework. In the prevention response stage, line hardening, flexible devices deployment, and unit commitment are performed before the typhoon attacks. During the typhoons, the emergency response is conducted to mitigate power outages by regulating soft open points, battery storage systems, and generation units. Moreover, considering the time-varying behaviors of typhoon path, the simulation technique for a sequential typhoon attack is developed to construct the spatially and temporally extended N-k uncertainty set for overhead line status. Thereafter, a tight approximation method is introduced to allow the proposed model to be a tractable mixed-integer linear programming problem, which can be easily solved by a nested column-and-constraint generation algorithm. Numerical results show that the comprehensive resilience-oriented strategies can respond rapidly to the worst-case scenario of typhoon attacks with cost-effective performance.

关键词： Typhoon events Resilience-oriented scheduling framework Spatially and temporally extended N-k uncertainty set nested column-and-constraint generation algorithm

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Security-constrained robust dynamic power system planning with discrete recourse

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ELECTRIC POWER SYSTEMS RESEARCH 2023年第PartA期214卷

作者： Haghighat, Hossein Zeng, Bo Islamic Azad Univ Elect & Comp Engn Dept Jahrom Branch Jahrom Iran Univ Pittsburgh Dept Ind Engn Pittsburgh PA 15260 USA Univ Pittsburgh Dept Elect & Comp Engn Pittsburgh PA 15260 USA

In this study, we present a two-stage robust optimization model for dynamic generation and transmission expansion planning considering discrete recourse decisions and n - K security constraints. Unlike the mainstream literature, our approach exactly considers cost non-convexities, binary commitment decisions, and security constraints. By customizing a nested column-and-constraint generation procedure, we compute this complex problem accurately. On two test systems, we illustrate the efficacy of the method and the impacts of security constraints, and the discreteness of recourse decisions on grid expansions.

关键词： Two-stage robust optimization Security-constrained power system expansion planning nested column-and-constraint generation algorithm Uncertainty Discrete recourse

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A tri-level optimization model for power grid defense with the consideration of post-allocated DGs against coordinated cyber-physical attacks

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2021年 130卷 106903-106903页

作者： He, Huihui Huang, Shengjun Liu, Yajie Zhang, Tao Natl Univ Def Technol Coll Syst Engn Changsha 410073 Hunan Peoples R China Hunan Key Lab Multienergy Syst Intelligent Interc Changsha 410073 Hunan Peoples R China

Due to the extensive integration of communication infrastructures, the power grid is vulnerable to a range of cyber-physical coordinated attacks. To increase the reliability of the power grid against coordinated attacks, a defensive strategy considering post-allocated Distributed Generators (DGs) is developed in this paper. The problem is formulated as a tri-level optimization model: the upper-level problem represents the action of the planner to determine the optimal plan for the defensive line and the pre-allocated DGs before attacks;the middlelevel problem formulates the behaviour of the attacker in identifying the targets;the lower-level problem simulates the reaction of the defender to optimize power flow and the placement of post-allocated DGs on the remaining microgrid system. Minimizing the system load shed is the objective of this tri-level optimization framework. Furthermore, the traditional Load Redistributed (LR) attack model is reformulated in a discrete form to facilitate the utilization of duality-based method in the tri-level model. The solution process is developed based on the nested column-and-constraint generation (NCCG) algorithm with the duality-based method. Case studies are conducted on the IEEE 14-bus system and the IEEE RTS79 system and they indicate that the tri-level model and algorithm are productive and promising. In addition, a sensitivity analysis is implemented on the parameters and settings.

关键词： Tri-level optimization model Power grid Post-allocated DGs Cyber-physical coordinated attacks nested column-and-constraint generation algorithm

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Robust transmission network expansion planning considering non-convex operational constraints

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ENERGY ECONOMICS 2021年 98卷 105246-105246页

作者： Garcia-Cerezo, Alvaro Baringo, Luis Garcia-Bertrand, Raquel Univ Castilla La Mancha Escuela Tecn Super Ingn Ind Power & Energy Anal & Res Lab Campus Univ S-N Ciudad Real 13071 Spain

This paper proposes a two-stage robust optimization model for the transmission network expansion planning problem. Long-term uncertainties in the peak demand and generation capacity are modeled using confidence bounds, while the short-term variability of demand and renewable production is modeled using a set of representative days. As a distinctive feature, this work takes into account the non-convex operation of conventional generating units and storage facilities, which results in a two-stage robust optimization model with a discrete recourse problem. The resulting problem is solved using a nested column-and-constraint generation algorithm that guarantees convergence to the global optimum in a finite number of iterations. An illustrative example and a case study are used to show the performance of the proposed approach. Numerical results show that neglecting the non-convex operation of conventional generating units and storage facilities leads to suboptimal expansion decisions. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Two-stage robust optimization Transmission network expansion planning Commitment status Storage units Uncertainty nested column-and-constraint generation algorithm

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Robust Energy and Reserve Scheduling Considering Bulk Energy Storage Units and Wind Uncertainty

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IEEE TRANSACTIONS ON POWER SYSTEMS 2018年第5期33卷 5206-5216页

作者： Cobos, Noemi G. Arroyo, Jose M. Alguacil, Natalia Wang, Jianhui Univ Castilla La Mancha ETSI Ind Dept Ingn Elect Elect Automat & Comunicac E-13071 Ciudad Real Spain Southern Methodist Univ Dept Elect Engn Dallas TX 75205 USA Argonne Natl Lab Div Energy Syst 9700 S Cass Ave Argonne IL 60439 USA

In the restructured power industry, bulk energy storage may play a crucial role to provide the flexibility required by system operators to cater for the unprecedented levels of uncertainty. Within the context of co-optimized electricity markets for energy and reserves under wind uncertainty, this paper addresses the incorporation of bulk energy storage units in day-ahead network-constrained energy and reserve scheduling. A novel two-stage robust optimization approach is presented whereby the nonconvex and time-coupled operation of storage devices is precisely modeled while accounting for the anticipativity of the two-stage setting. The resulting robust counterpart is cast as a mixed-integer trilevel program with lower-level binary variables. In order to address the nonconvexity of the recourse problem, this paper proposes the application of an exact nested column-and-constraint generation algorithm. Numerical results illustrate the effective performance of the proposed approach.

关键词： Bulk energy storage energy and reserve scheduling nested column-and-constraint generation algorithm two-stage robust optimization wind uncertainty

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