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IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)

作者： Xu, K. Zheng, M. M. Liu, X. Shanghai Jiao Tong Univ Sch Mech Engn Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ China Inst Urban Governance Shanghai 200240 Peoples R China

ISBN: (纸本)9781665437714

In this paper, the vehicles choosing and routes planning problem in the urban food waste collection network is addressed. Considering service demands uncertainty and traversing costs uncertainty on roads, a bi-objective two-stage binary robust model is formulated to derive cost-effective and public-friendly strategies for collection vehicles. One objective is to minimize the worst-case total cost, while the other minimizes the environmental-disutility. A solution procedure based on the combination of the.-constraint method and the modified column-and-constraint generation algorithm is developed to solve the model. A case study is finally performed to validate the effectiveness of the robust model and the solution procedure.

关键词： food waste collection uncertainty robust optimization epsilon-constraint method column-and-constraint generation algorithm

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Distributed robust optimization scheduling of a steel plant integrated energy system considering the uncertainty of byproduct coal gas

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FRONTIERS IN ENERGY RESEARCH 2022年 10卷

作者： Li, Fan Li, Yuxiao Niu, Tao Fang, Sidun Wu, Wenguo Chongqing Univ Sch Elect Engn Chongqing Peoples R China Zhejiang Univ Affiliated Hosp 1 Dept Obstet & Gynecol Sch Med Hangzhou Peoples R China

The steel plant integrated energy system (SPIES) is an important form in the steel industry. Improving the utilization efficiency of steam, electricity, coal gas and other energy flows is of great significance for both economic and environmental benefits. In this paper, a SPIES scheduling model is established according to the operation characteristics of coal gas holders, boilers and other equipment in steel plants. Meanwhile, to cope with the uncertainty of byproduct coal gas, this paper adopts an imprecise Dirichlet model (IDM) to construct a fuzzy set containing multisource coal gas production information. Then, according to duality theory and the big-M method, the original distributed robust optimization (DRO) model is transformed into a traditional mixed integer linear programming (MILP) model, which is solved by the column-and-constraint generation (CC & G) algorithm. Finally, a real steel production system is given in a case study. Case study illustrate that compared with the traditional robust method, the method proposed in this paper for a SPIES can effectively reduce the conservatism of the scheduling decision. Numerical simulation show that the proposed method can reduce total cost by 55,307.1 yen , accounting for 1.91% of the total cost compared with robust optimization method and save 1,326.94 s of computational time compared with the stochastic optimization method, thus reaching balance between conservatism and computational efficiency.

关键词： steel plant integrated energy system distributed robust optimization imprecise dirichlet model MILP model column-and-constraint generation algorithm

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Coordinated planning of multi-energy systems considering demand side response

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ENERGY REPORTS 2020年 6卷 745-751页

作者： Wang, Jinpeng Zeng, (Peter) Pingliang Liu, Jia Li, Yalou Hangzhou Dianzi Univ Sch Automat Hangzhou 310000 Peoples R China Elect Power Res Inst Beijing 100010 Peoples R China

Integrated multi-energy systems, due to their flexibility and complementary characteristics, are considered an important energy system structure for accommodating high renewable penetration, and thus have attracted great attention of global research recently. Based on the energy hub, a two-stage optimal design and planning method for regional integrated energy systems (IES) is proposed in this paper, Furthermore, the demand side response (DSR) is taken into consideration. The EnergyPlus simulation software is used to produce multi-energy load data for various types of buildings in urban areas. Then, the improved k-means data clustering method and the Kolmogorov Distance scene reduction technology are applied to generate three types of typical scenarios. The objective of optimal IES planning is to minimize total capital cost, O&M cost, and the shifting load compensation cost while making sure the system operates reliably and safely. The column-and-constraint (CCG) generation algorithm is implemented to achieve fast optimization solution. The simulation results show the effectiveness of the proposed method, and the benefits of IES with complementary multi-energies are reflected. (C) 2020 TheAuthors. Published by Elsevier Ltd.

关键词： Multi-energy systems column-and-constraint generation algorithm Demand side response

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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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Optimal energy management for grid-connected microgrids via expected-scenario-oriented robust optimization

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ENERGY 2021年 216卷 119224-119224页

作者： Zhu, Junjie Huang, Shengjun Liu, Yajie Lei, Hongtao Sang, Bo 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 Naval Aeronaut & Astronaut Univ Sch Basic Sci Aviat Yantai 264000 Shandong Peoples R China

In order to facilitate the integration of distributed energy resources (DERs), grid-connected microgrids (MGs) have been rapidly deployed over the last decade. However, their potential negative impact on the security of utility grids cannot be ignored. For the existence of various uncertainties, uncertainty quantification method, e.g., robust optimization (RO), has been proposed for the energy management of MGs. Targeting the conservatism problem presented by the traditional two-stage RO method, which is oriented towards the worst-case scenario, this paper proposes a novel method targeting the expected scenario. Furthermore, in the proposed model, the decision variables representing the charging/discharging states of energy storage system (ESS) is considered in the re-dispatch stage, in contrast to existing models that putting them in the pre-dispatch stage, to increase the operational flexibility of ESS for energy management. Notably, this shift in the treatment of ESS translates the proposed two-stage RO model into a mixed integer programming (MIP) model with recourse. Therefore, the nested column-and-constraint generation (N-C&CG) algorithm is adopted. Results of numerical experiments illustrated the superiorities of the proposed model in minimizing the cost of system operation and reducing the negative impact on the utility grid. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Grid-connected microgrid Energy management Robust optimization Expected-scenario-oriented Two-stage decision process column-and-constraint generation algorithm

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Operations of a microgrid with renewable energy integration and line switching

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ENERGY SYSTEMS-OPTIMIZATION MODELING SIMULATION AND ECONOMIC ASPECTS 2019年第2期10卷 247-272页

作者： Ruiz Duarte, Jose Luis Fan, Neng Univ Arizona Dept Syst & Ind Engn Tucson AZ 85721 USA

With the development of new technologies and their integration to the conventional power grid, the smart grid with the capacity of satisfying power demand by large amount of renewable energy is emerging. Microgrid, a small-scale power system with clearly defined electrical boundaries and ability of self-supply, especially by distributed renewable energy, plays a big role in this process. In this paper, we study the operations of a microgrid with solar photovoltaic generators, energy storage system, and power exchanges with main power grid. More specifically, a mixed integer programming model is formulated for decision-making, such as scheduling of generators within the microgrid, islanding operations through line switching and power trades between microgrid and the main grid, charging and discharging operations of storage system, and also line switching within the microgrid, by robust optimization for capturing the uncertainties of solar power generation. To solve the robust optimization formulation, we formulate our model in order to apply the column-and-constraint generation algorithm, and perform numerical experiments on several test cases to validate the proposed model and algorithm.

关键词： Microgrid Renewable energy sources Strorage systems Line switching Robust optimization column-and-constraint generation algorithm

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Energy management for aggregate prosumers in a virtual power plant: A robust Stackelberg game approach

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2020年第0期117卷 105605-000页

作者： Yin, Shuangrui Ai, Qian Li, Zhaoyu Zhang, Yufan Lu, Tianguang Shanghai Jiao Tong Univ Minist Educ Key Lab Control Power Transmiss & Convers Shanghai 200240 Peoples R China

In this paper, a novel two-stage robust Stackelberg game is proposed to solve the problem of day-ahead energy management for aggregate prosumers considering the uncertainty of intermittent renewable energy output and market price. The aggregate prosumers operate in the form of virtual power plant (VPP) and participate in day-ahead (DA) and real-time (RT) market transactions. As the initiator and leader of the VPP, the superior prosumer with thermal units and interruptible loads is responsible for formulating the internal price mechanism and energy management strategy of the aggregate prosumers. Inferior prosumers, including renewable energy and shiftable loads, are responsible for providing renewable energy output information and responding to the price signals from the superior prosumer. The two-stage robust Stackelberg game model is linearized and solved by column-and -constraint generation (CC&G) algorithm. In addition, the thermal unit operating in the automatic generation control (AGC) mode ensures the realization of real-time optimal scheduling of aggregate prosumers for the entire dispatching cycle. Simulation results prove the rationality and validity of the proposed model and method.

关键词： Energy management Aggregate prosumers Virtual power plant Two-stage robust Stackelberg game column-and-constraint generation algorithm

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On the Solution of Large-Scale Robust Transmission Network Expansion Planning Under Uncertain Demand and generation Capacity

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IEEE TRANSACTIONS ON POWER SYSTEMS 2018年第2期33卷 1242-1251页

作者： Minguez, Roberto Garcia-Bertrand, Raquel Arroyo, Jose M. Alguacil, Natalia Univ Castilla La Mancha Spin Off UCLM Hydraul Lab Hidralab Ingn & Desarrollo SL E-13071 Ciudad Real Spain Univ Castilla La Mancha Dept Ingn Elect Elect Automat & Comunicac ETSI Ind E-13071 Ciudad Real Spain

Two-stage robust optimization has emerged as a relevant approach to deal with uncertain demand and generation capacity in the transmission network expansion planning problem. Unfortunately, the solution of practical large-scale instances remains a challenge. In order to address this issue, this paper presents an alternative column-and-constraint generation algorithm wherein the max-min problem associated with the second stage is solved by a block coordinate descent method. As a major salient feature, the proposed approach does not rely on the transformation of the second-stage problem to a single-level equivalent. As a consequence, bilinear terms involving dual variables or Lagrange multipliers do not arise, thereby precluding the use of computationally expensive big-M-based linearization schemes. Thus, not only is the computational effort reduced, but also the typically overlooked case-dependent, nontrivial, and time-consuming tuning of bounding parameters for dual variables or Lagrange multipliers is avoided. The practical applicability of the proposed methodology is confirmed by numerical testing on several benchmarks including a case based on the Polish 2383-bus system, which is well beyond the capability of the robust methods available in the literature.

关键词： Block coordinate descent method column-and-constraint generation algorithm transmission network expansion planning two-stage robust optimization uncertainty

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Data-adaptive robust unit commitment in the hybrid AC/DC power system

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APPLIED ENERGY 2019年 254卷 113784-000页

作者： Zhou, Bo Ai, Xiaomeng Fang, Jiakun Yao, Wei Zuo, Wenping Chen, Zhe Wen, Jinyu Huazhong Univ Sci & Technol Sch Elect & Elect Engn State Key Lab Adv Electromagnet Engn & Technol Wuhan 430074 Hubei Peoples R China Aalborg Univ Dept Energy Technol DK-9220 Aalborg Denmark

This paper proposes the data-adaptive robust optimization for the optimal unit commitment in the hybrid AC/DC power system. With the convexified branch flow model interconnecting the AC and DC power grid, the unit commitment problem in the hybrid AC/DC power system is formulated as a mixed-integer second-order cone programming. Considering the temporal and spatial correlations of multiple wind farms, the data-adaptive uncertainty set and the extreme scenarios are introduced to reformulate the robust optimization. The column-and-constraints generation algorithm is adopted to solve the multi-scenario problem. Case studies in the modified IEEE 14-bus system and the Henan provincial power system demonstrate the applicability of the proposed model. Comparative results with the pure AC power system show the improvement of the flexibility by DC interconnections. Both the operational cost and the times of generator startup/shutdown are reduced. The regulation capability of the DC lines can be fully utilized to cope with the uncertainties introduced by wind power.

关键词： Data-adaptive robust optimization Unit commitment Hybrid AC/DC power system Branch flow model Mixed-integer second-order cone programming column-and-constraint generation algorithm

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Data-Driven Adaptive Nested Robust Optimization: General Modeling Framework and Efficient Computational algorithm for Decision Making Under Uncertainty

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AICHE JOURNAL 2017年第9期63卷 3790-3817页

作者： Ning, Chao You, Fengqi Cornell Univ Smith Sch Chem & Biomol Engn Ithaca NY 14853 USA

A novel data-driven adaptive robust optimization framework that leverages big data in process industries is proposed. A Bayesian nonparametric model-the Dirichlet process mixture model-is adopted and combined with a variational inference algorithm to extract the information embedded within uncertainty data. Further a data-driven approach for defining uncertainty set is proposed. This machine-learning model is seamlessly integrated with adaptive robust optimization approach through a novel four-level optimization framework. This framework explicitly accounts for the correlation, asymmetry and multimode of uncertainty data, so it generates less conservative solutions. Additionally, the proposed framework is robust not only to parameter variations, but also to anomalous measurements. Because the resulting multi-level optimization problem cannot be solved directly by any off-the-shelf solvers, an efficient column-and-constraint generation algorithm is proposed to address the computational challenge. Two industrial applications on batch process scheduling and on process network planning are presented to demonstrate the advantages of the proposed modeling framework and effectiveness of the solution algorithm. (C) 2017 American Institute of Chemical Engineers

关键词： big data data-driven adaptive robust optimization Dirichlet process mixture model column-and-constraint generation algorithm process design and operations

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