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11th International Conference on Power and Energy Systems (ICPES)

作者： Yao, Yiming Li, Chunyan Xie, Kaigui Hu, Bo Shao, Changzheng Yan, Zhichao Chongqing Univ Sch Elect Engn Chongqing 400044 Peoples R China

ISBN: (纸本)9781665413251

High penetration of renewable energy, such as wind power, has brought threats to the security and reliability of power systems due to its uncertainty. To deal with hidden dangers of the imbalance of supply and demand, this paper proposes a model of integrated electrical, heating, natural gas and water distribution system. The price-based integrated demand response program has been formulated for the electrical, gas and water load. The thermal inertia of building materials is modeled to make full use of the thermal storage characteristic and provide a new means of heat demand response. Optimal schedule of the integrated demand response and the coordination among the multi-energy systems can promote the wind power accommodation and improve the operation cost. Considering the uncertainties of wind power and demands, a two-stage robust optimization method is developed, which can not only maximize the total profit but also make the system robust against the worst scenario. The proposed model is evaluated on an integrated test system to verify its correctness and superiority.

关键词： wind power multi-energy system integrated demand response water demand two-stage robust optimization

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two-stage robust optimization Method for Joint System Considering Uncertainty of Electric Vehicles 8

Two-Stage Robust Optimization Method for Joint System Consid...

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8th International Conference on Power Energy Systems and Applications, ICoPESA 2024

作者： Ma, Ying Chen, Yu Liao, Xiaozhong Liu, Bin Liu, Ruyi Li, Zhen School of Automation Beijing Institute of Technology Beijing China Beijing China School of Mathematics and Statistics University of Sydney Sydney Australia

ISBN: (纸本)9798350351668

To conduct the green and low-carbon development, renewable energy needs to play more vigorous role in power generation. Through the cooperative regulation and participation in the spot market, renewable energy, energy storage and electric vehicles (EVs) jointly participate in the regulation so as to improve energy efficiency and increase the penetration of renewable energy. In this paper, a two-stage robust optimization model is established considering the uncertainty of EVs' number. The first and second stages determine the bidding plans of such joint system in the day-ahead and real-time markets respectively, so as to minimize the cost. The associated optimization can provide the robust and economical results. Importantly, the modeling in perspective of uncertainty is applied to EVs participating in spot markets in an aggregated manner. To solve this model, an improved column-and-constraint generation (C&CG) algorithm is proposed, which is solved by combination of genetic algorithm and solver. The test results verify the effectiveness of the model and method proposed in this paper. © 2024 IEEE.

关键词： electric vehicles energy storage renewable energy two-stage robust optimization uncertainty

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Optimal Design of Distributed Energy Resource Systems under Uncertainties Based on two-stage robust optimization

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Journal of Thermal Science 2021年第1期30卷 51-63页

作者： LI Da ZHANG Shijie Key Laboratory of Advanced Energy and Power Institute of Engineering ThermophysicsChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China Research Center for Clean Energy and Power Chinese Academy of SciencesLianyungang 222069China

Distributed energy resource(DER)systems are widely used owing to their excellent economic and environmental ***,uncertainties in the system generate difficulties in the optimal design of DER *** practice,the distribution of uncertain parameters is generally *** this work,a two-stage robust optimization(RO)model was proposed for the optimal design of DER systems considering uncertainties in renewable energy intensity,energy prices,and load *** uncertainty sets(i.e.,the box,ellipsoid,and convex-hull uncertainty sets)were adopted to describe the distribution of uncertain parameters,and the proposed two-stage RO problem was solved using affine decision rules.A typical hospital in Lianyungang,Jiangsu Province,China,was selected as the case study object,and the effectiveness of the model was *** case study results showed that uncertainties in energy prices and load demands have a significant impact on system configuration and economic performance,and mainly affect the installed capacities of gas boilers,absorption chillers,and *** set will affect the optimization results and an appropriate uncertainty set should be adopted to describe uncertainties precisely and increase accuracy of results.

关键词： distributed energy resource optimal design two-stage robust optimization uncertainty uncertainty set

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Multi-objective two-stage robust optimization of wind/PV/thermal power system based on meta multi-agent reinforcement learning

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2024年 162卷

作者： Li, Dengao Zhang, Zhuokai Feng, Ding Zhou, Yu Yu Bai, Xiaodong Zhao, Jumin Shanxi Energy Internet Res Inst Taiyuan 030000 Shanxi Peoples R China Taiyuan Univ Technol Coll Comp Sci & Technol Taiyuan 030024 Shanxi Peoples R China Taiyuan Univ Technol Coll Data Sci Taiyuan 030024 Shanxi Peoples R China Key Lab Big Data Fus Anal & Applicat Shanxi Prov Taiyuan 030024 Shanxi Peoples R China Taiyuan Normal Univ Coll Comp Sci & Technol Taiyuan 030619 Shanxi Peoples R China

The integration of renewable energy into the power grid poses significant challenges for optimization and scheduling of the power system. In recent years, methods based on deep reinforcement learning have surpassed traditional methods on the high complexity and long-term decision-making of power system optimization and scheduling. However, faced with the inherent uncertainty of renewable energy generation and the different optimization objectives in power system, the deep reinforcement learning methods are unable to effectively address them. This paper proposes a method that combines meta reinforcement learning with multi-agent reinforcement learning to solve the multi-objective two-stage robust optimization of wind/PV/thermal power system. We conducts optimization and scheduling experiments on the IEEE39 bus system. The results indicate that our method not only enhances the robustness of the scheduling strategy, but also outperforms baseline methods in terms of convergence, diversity, and uniformity of the Pareto frontier.

关键词： Renewable energy Multi-objective optimization two-stage robust optimization Meta reinforcement learning Multi-agent reinforcement learning

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Designing networks with resiliency to edge failures using two-stage robust optimization

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2019年第3期279卷 704-720页

作者： Matthews, Logan R. Gounaris, Chrysanthos E. Kevrekidis, Ioannis G. Princeton Univ Dept Chem & Biol Engn Princeton NJ 08544 USA Carnegie Mellon Univ Dept Chem Engn Pittsburgh PA 15213 USA Johns Hopkins Univ Dept Chem & Biomol Engn Baltimore MD USA

We study the design of resilient single-commodity flow networks that can remain robust against multiple concurrent edge failures. We model these failures as binary random variables, allowing us to formally formulate the network design problem as a two-stage robust optimization problem. With an objective of minimizing the overall cost of building and operating the network, the capacities of the edges are decided in the first stage, while the optimal flows are determined in the second stage once the uncertainty has been realized. We first examine the standard affine decision rules approach and show that it is not a viable approach when two or more edges are allowed to fail at the same time. We then propose a column and constraint generation algorithm that we tailor to this application. Since the problem does not satisfy the relatively complete recourse assumption, we employ an oracle with two subproblems: one to determine edge failure scenarios that render the required demand satisfaction infeasible, and if no such scenario exists, a second one to determine the flow rerouting plan of highest cost. Our column and constraint generation algorithm is applied to networks adapted from the Survivable Network Design Library. For each instance, we determine sequences of fully adaptive, robust optimal solutions for various levels of resiliency, identifying also the maximum number of concurrent edge failures that can be sustained by these networks. Finally, we demonstrate how our algorithm can be applied to a defender versus attacker context, via the use of a decision-dependent uncertainty set. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Networks Network design Network resiliency two-stage robust optimization Column and constraint generation

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Uncertain parameters adjustable two-stage robust optimization of a rural housing integrated energy system considering biomass on-site utilization

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ENERGY 2024年 296卷

作者： Han, Fengwu Zhao, Yunlong Zeng, Jianfeng Zhang, Shengnan Wu, Tianyu North China Elect Power Univ Dept Econ & Management Baoding 071003 Hebei Province Peoples R China

Rural areas will be an important component to achieve carbon neutrality. However, there are currently issues such as an unreasonable energy consumption structure and low efficiency in renewable energy utilization. It is urgent to propose an energy optimization solution to address these problems. Therefore, considering the advantages of renewable energy in rural areas and the diversified and decentralized energy consumption patterns of rural households, a rural housing integrated energy system (RHIES) is proposed, which includes photovoltaics and a biomass utilization chain through self -production and local consumption. Subsequently, considering the economic and low -carbon aspects of the system ' s operational process, an uncertain parameters adjustable twostage robust optimization model (UPATSRO) is developed to achieve a balance between robustness and economic efficiency during the scheduling optimization process. The results of case study indicate that the proposed RHIES reduces costs and carbon emissions by 28.15% and 93.83%, respectively, compared to conventional approaches. The biomass utilization mode satisfies 52.31% of the gas load and 39.71% of the thermal load for households. Additionally, it is shown that the proposed UPATSRO optimization model can achieve a flexible adjustment of scheduling schemes during the scheduling optimization process.

关键词： Rural housing integrated energy system Biomass on-site utilization mode Carbon emissions two-stage robust optimization Uncertainty parameters adjustment

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Staffing and scheduling flexible call centers by two-stage robust optimization

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OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE 2017年 72卷 25-37页

作者： Mattia, Sara Rossi, Fabrizio Servilio, Mara Smriglio, Stefano CNR Ist Anal Sistemi & Informat Via Taurini 19 I-00185 Rome Italy Univ Aquila Dipartimento Ingn Sci & Informaz & Matemat Via Vetoio I-67010 Coppito AQ Italy

We study the shift scheduling problem in a multi-shift, flexible call center. Differently from previous approaches, the staffing levels ensuring the desired quality of service are considered uncertain, leading to a two-stage robust integer program with right-hand-side uncertainty. We show that, in our setting, modeling the correlation of the demands in consecutive time slots is easier than in other staffing approaches. The complexity issues of a Benders type reformulation are investigated and a branch-and-cut algorithm is devised. The approach can efficiently solve real-world problems from an Italian call center and effectively support managers decisions. In fact, we show that robust shifts have very similar costs to those evaluated by the traditional (deterministic) method while ensuring a higher level of protection against uncertainty. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Call center optimization Shift scheduling two-stage robust optimization Benders decomposition

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Coordinated Planning With Predetermined Renewable Energy Generation Targets Using Extended two-stage robust optimization

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IEEE ACCESS 2020年 8卷 2395-2407页

作者： Tian, Kunpeng Sun, Weiqing Han, Dong Yang, Ce Univ Shanghai Sci & Technol Dept Control Sci & Engn Shanghai 200093 Peoples R China Univ Shanghai Sci & Technol Dept Elect Engn Shanghai 200093 Peoples R China

Coordinated planning is an effective method to balance investment costs and benefits in achieving high renewable target under the renewable-driven power system expansion wave. This paper proposes a coordinated planning model to support the efficient achievement of renewable target considering economy of the system by accounting for the interaction among source, grid, and energy storage system. An adaptive two-stage min-max-min robust optimization model is formulated to take into account renewable target as well as the uncertainty associated with renewable production and load demand. To reduce the conservatism of robust optimization, uncertain budget, multiple uncertain sets, and data-driven method are used to design uncertain sets. The resulting model is transformed into a tractable bi-level programming through strong duality theory and big-M method. A customized column-and-constraint generation algorithm is used to solve the bi-level programming. Simulation results presented for the modified IEEE 30-bus test system corroborates the effectiveness of the methodology, which finds siting and sizing of renewable energy sources and energy storage systems as well as transmission expansion schemes. It is capable to provide a fiexible planning tool driven by renewable target under a reasonable computational burden.

关键词： Coordinated planning renewable target two-stage robust optimization multiple uncertain sets data-driven column-and-constraint generation algorithm

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Oracle-based algorithms for binary two-stage robust optimization

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COMPUTATIONAL optimization AND APPLICATIONS 2020年第2期77卷 539-569页

作者： Kaemmerling, Nicolas Kurtz, Jannis TU Dortmund Univ Inst Transport Logist Leonhard Euler Str 2 D-44227 Dortmund Germany Rhein Westfal TH Aachen Chair Math Informat Proc Pontdriesch 12-14 D-52062 Aachen Germany

In this work we study binary two-stage robust optimization problems with objective uncertainty. We present an algorithm to calculate efficiently lower bounds for the binary two-stage robust problem by solving alternately the underlying deterministic problem and an adversarial problem. For the deterministic problem any oracle can be used which returns an optimal solution for every possible scenario. We show that the latter lower bound can be implemented in a branch and bound procedure, where the branching is performed only over the first-stage decision variables. All results even hold for non-linear objective functions which are concave in the uncertain parameters. As an alternative solution method we apply a column-and-constraint generation algorithm to the binary two-stage robust problem with objective uncertainty. We test both algorithms on benchmark instances of the uncapacitated single-allocation hub-location problem and of the capital budgeting problem. Our results show that the branch and bound procedure outperforms the column-and-constraint generation algorithm.

关键词： two-stage robust optimization Non-linear binary optimization Branch and bound algorithm

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A Feasibility Pump Based Solution Algorithm for two-stage robust optimization With Integer Recourses of Energy Storage Systems

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IEEE TRANSACTIONS ON SUSTAINABLE ENERGY 2021年第3期12卷 1834-1837页

作者： Zhang, Cuo Dong, ZhaoYang Yang, Linfeng Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia Guangxi Univ Sch Comp Elect & Informat Nanning 530004 Peoples R China

To address uncertainties, two-stage robust optimization (TSRO) methods have been widely used for power system operation and planning problems. However, solving TSRO problems with integer recourses of energy storage systems (ESSs) is challenging. This article proposes a feasibility pump based column and constraint generation (FP-CCG) solution algorithm to solve these TSRO problems. In this article, firstly, a TSRO problem of ESS operation with state-of-charge management is formulated. Then, the FP-CCG algorithm is proposed and applied to solve this TSRO problem. Simulation results indicate full solution robustness and high computation efficiency of the proposed FP-CCG solution algorithm.

关键词： Uncertainty Energy storage Microgrids optimization Mathematical model Load modeling Upper bound Column and constraint generation energy storage system feasibility pump integer recourse mixed integer linear programming two-stage robust optimization

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