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constraint generation algorithm for the Minimum Connectivity Inference Problem 1

Conference on Analysis of Experimental algorithms (SEA2)

作者： Bonnet, Edouard Falamas, Diana-Elena Watrigant, Remi Univ Claude Bernard Lyon 1 Univ Lyon CNRS ENS LyonLIP F-69342 Lyon 07 France Tech Univ Cluj Napoca Cluj Napoca Romania

ISBN: (数字)9783030340292

ISBN: (纸本)9783030340292;9783030340285

Given a hypergraph H, the Minimum Connectivity Inference problem asks for a graph on the same vertex set as H with the minimum number of edges such that the subgraph induced by every hyper-edge of H is connected. This problem has received a lot of attention these recent years, both from a theoretical and practical perspective, leading to several implemented approximation, greedy and heuristic algorithms. Concerning exact algorithms, only Mixed Integer Linear Programming (MILP) formulations have been experimented, all representing connectivity constraints by the means of graph flows. In this work, we investigate the efficiency of a constraint generation algorithm, where we iteratively add cut constraints to a simple ILP until a feasible (and optimal) solution is found. It turns out that our method is faster than the previous best flow-based MILP algorithm on random generated instances, which suggests that a constraint generation approach might be also useful for other optimization problems dealing with connectivity constraints. At last, we present the results of an enumeration algorithm for the problem.

关键词： Hypergraph constraint generation algorithm Connectivity problem

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Multi-Stage Robust Dispatch Considering Demand Response Under Decision-Dependent Uncertainty

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IEEE TRANSACTIONS ON SMART GRID 2023年第4期14卷 2786-2797页

作者： Su, Yifan Liu, Feng Wang, Zhaojian Zhang, Yunfan Li, Boda Chen, Yue Tsinghua Univ Dept Elect Engn State Key Lab Power Syst Beijing 100084 Peoples R China Shanghai Jiao Tong Univ Dept Automat Key Lab Syst Control & Informat Proc Minist Educ China Shanghai 200240 Peoples R China ABB Corp Res Ctr D-68526 Ladenburg Germany Chinese Univ Hong Kong Dept Mech & Automat Engn Hong Kong Peoples R China

Demand response (DR) programs create incentives to effectively exploit the hidden operational flexibility of loads for better supporting power system operations. However, DR programs must consider the increasing demand-side uncertainty due to proliferative devices like electric vehicles and rooftop photovoltaics. Shifting power consumption by DR programs may change the realizations of future uncertainties of loads, leading to decision-dependent uncertainties (DDUs). This paper first proposes a novel multi-stage robust energy and reserve dispatch model with the DR program, where DDUs of deferrable loads and decision-independent uncertainties (DIUs) of curtailable loads are considered simultaneously. Then we develop an improved constraint generation (CG) algorithm with an embedded scenario mapping approach to solve the model effectively. Numerical experiments illustrate the influence of DDUs on the multi-stage robust dispatch problem and verify the effectiveness of the proposed method to cope with DDUs and DIUs in DR programs.

关键词： Demand response multi-stage robust dispatch decision-dependent uncertainty deferrable load constraint generation algorithm

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Robust Optimization Coordinated Optimal UPFC and Unit Dispatch Considering Wind Uncertainty 6

Robust Optimization Coordinated Optimal UPFC and Unit Dispat...

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6th International Conference on Power and Energy Technology, ICPET 2024

作者： Dong, Pan Yu, Chen Xuli, Wang Zhiwei, Li Xuedong, Zhu Lin, Wang State Grid Anhui Electric Power Co. Ltd. Economic Research Institute Hefei China School of Electrical Engineering and Automation Wuhan University Wuhan China

ISBN: (纸本)9798350375855

The UPFC with the characteristics of flexible controllability and fast response is beneficial to improve the flexible ability engaged in the power system operating, the solution of power dispatch is depicted as the coordinate operation of source side, grid side and load side. This paper proposes a union optimization method for robust planning considering the optimal location-allocations of UPFC to improve the flexible operating ability of power system and reduce the risk of wind curtailment and load shedding. Firstly, based on the static operating characteristics of UPFC and non-Convex and nonlinear characteristics of AC power flow, a convex model of power flow considering UPFC is established to improve the accuracy of power flow calculation;Secondly, based on the ability of UPFC, a joint model considering optimal UPFC and optimal dispatch is presented with the uncertainty of wind. Then, based on the strong dual theory and linear relaxation method, the column and constraint generation algorithm is introduced to convert the original model into a two-stage problem to be solved. Finally, the modified IEEE RTS 24 bus system is introduced to verify the rationality and effectiveness of the proposed model, the simulation proved the optimal UPFC is beneficial to improve the dispatch economy and reduce the wind curtailment and load shedding. © 2024 IEEE.

关键词： column constraint generation algorithm convex model robust optimization UPFC

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Blockchain-enabled electric vehicle orderly charging optimization

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JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2024年第12期75卷 2404-2420页

作者： Zhang, Ningwei Zhi, Tonghui Zhang, Yuli Lanzhou Univ Sch Management Lanzhou Peoples R China Beijing Inst Technol Sch Management & Econ Beijing Peoples R China Beijing Inst Technol Sch Management & Econ Beijing 100081 Peoples R China

The increasing prevalence of electric vehicles (EVs) urges charging station operators (CSOs) to expand their networks. However, currently CSOs operate independent EV charging service platforms separately, which poses inconvenience for EV users and hinders the efficient utilization of charging infrastructure. This paper introduces a consortium blockchain-enabled EV charging service platform (CB-EVCSP), which leverages novel orderly charging guidance models (OCGMs) encoded as smart contracts to integrate and coordinate the efforts of CSOs. The CB-EVCSP not only mitigates trust and security concerns but also enhances the utilization of charging facilities. Both multinomial logit (MNL) and mixed MNL models are developed to characterize the interaction between charging station recommendations and EV users' choice behaviors, where a coefficient of variation based fairness criterion is used to ensure distribution fairness. We provide equivalent reformulations and design efficient constraint generation algorithms. Numerical experiments are conducted to validate the effectiveness of the proposed models and algorithms.

关键词： Electric vehicle orderly charging management blockchain smart contract constraint generation algorithm

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Hierarchical two-stage robust optimisation dispatch based on co-evolutionary theory for multiple CCHP microgrids

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IET RENEWABLE POWER generation 2020年第19期14卷 4121-4131页

作者： Tan, Bifei Chen, Haoyong Zheng, Xiaodong South China Univ Technol Sch Elect Power Engn Guangzhou 510641 Guangdong Peoples R China

Combined cooling, heating, and power (CCHP) microgrids are a special form of a microgrid that is attracting increasing attention. This study contributes to the goal of minimising the operation cost of CCHP microgrids by proposing a hierarchical two-stage robust optimisation dispatch model for multiple CCHP microgrid systems. The uncertainties associated with wind power output, electric power, heating, and cooling loads, and transmission line failures are considered in the proposed model. Moreover, the electricity purchasing and selling prices of each microgrid are independently determined. The proposed model applies the outputs of fuel cells, energy storage devices, and gas turbines, the distribution factor of waste heat, and the power transmission between the microgrids and an external grid as control variables. The optimised dispatch problem is solved using McCormick envelopes relaxation and a novel column and constraint generation algorithm that provides enhanced optimisation performance by implementing co-evolutionary theory. In this way, the microgrid system is divided into several sections, and each section is represented as an individual min-max-min problem. The rationality and validity of the proposed model and the superiority of the solution performance of the improved algorithm are verified through simulation case studies involving a system composed of four CCHP microgrids.

关键词： power generation economics gas turbines power generation control wind power plants cogeneration distributed power generation minimax techniques power generation dispatch fuel cells power transmission co-evolutionary theory two-stage robust optimisation dispatch model multiple CCHP microgrid systems wind power output electric power cooling loads transmission line failures waste heat power transmission optimised dispatch problem enhanced optimisation performance hierarchical robust optimisation dispatch combined cooling-heating-power microgrids electricity purchasing fuel cells energy storage devices gas turbines distribution factor McCormick envelopes relaxation column generation algorithm constraint generation algorithm min-max-min problem

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Data-driven distributionally robust economic dispatch for distribution network with multiple microgrids

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IET generation TRANSMISSION & DISTRIBUTION 2020年第24期14卷 5712-5719页

作者： Wang, Lingling Jiang, Chuanwen Gong, Kai Si, Ruihua Shao, Hongbo Liu, Wanxun Shanghai Jiao Tong Univ Key Lab Control Power Transmiss & Convers Shanghai 200240 Peoples R China State Grid Corp China Henan Econ Res Inst Zhengzhou 450052 Henan Peoples R China

With a high penetration level of renewable energy resources (RESs) in the distribution network (DN) and microgrids (MGs), how to realise the coordination between the two entities while takes the uncertain RESs into consideration becomes an urgent problem. A data-driven distributionally robust economic dispatch (DRED) model for both DN and MGs is proposed in this study, wherein the 1-norm and infinity-norm are used to construct the confidence set for the probability distribution of the uncertainties based on historical data. The DN and each MG are considered as independent entities to minimise their own operation cost. The alternating direction method of multipliers is utilised to coordinate the power exchange between DN and MGs and realise the autonomy of each entity. The column and constraint generation algorithm is used to solve the proposed data-driven DRED model for each entity. Considering the special structure of the proposed DRED problem, a duality-free decomposition method is adopted. Thus the computational burden is reduced. Numerical results on a modified IEEE 33-bus DN with three MGs validate the effectiveness of the proposed method.

关键词： power generation economics renewable energy sources power generation dispatch distributed power generation power distribution economics convex programming minimisation statistical distributions data-driven DRED model DRED problem modified IEEE 33-bus DN distribution network multiple microgrids renewable energy resources uncertain RES data-driven distributionally robust economic dispatch model probability distribution operation cost minimisation alternating direction method of multipliers constraint generation algorithm duality-free decomposition method

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Resilient co-expansion planning between gas and electric distribution networks against natural disasters

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IET generation TRANSMISSION & DISTRIBUTION 2020年第17期14卷 3561-3570页

作者： Zou, Bo Wang, Cheng Zhou, Ying Wang, Jianhui Chen, Chen Wen, Fushuan State Grid Zhejiang Elect Power Co Ltd Econ Res Inst Hangzhou 310008 Peoples R China Zhejiang Univ Sch Elect Engn Hangzhou 310027 Peoples R China North China Elect Power Univ Sch Elect & Elect Engn Beijing 102206 Peoples R China Zhejiang Huayun Elect Power Engn Design Consultin Hangzhou 310014 Peoples R China Southern Methodist Univ Dept Elect & Comp Engn Dallas TX 75275 USA Argonne Natl Lab Energy Syst Div Argonne IL 60439 USA

Resiliently designed and constructed integrated gas-electric distribution networks (GEDNs) against natural disasters are crucial to social welfare. In this study, a two-stage robust optimisation-based co-expansion planning model is proposed to attain an integrated GEDN with a given resilience level, by optimising the investment strategies of hardening and selective expansion of power distribution feeders and natural gas pipelines, as well as the location and capacity of natural-gas-fired distributed generation. In the first stage, the overall annual investment and operation cost is minimised under normal operation conditions while in the second stage, the feasibility of the investment decisions under the identified worst-case natural disaster scenario is checked with an adjustable load shedding cost criterion. The proposed model is formulated as a mixed integer second-order cone programming problem with the column and constraint generation algorithm employed to seek the optimal solution. Case studies on two integrated GEDNs demonstrate the performance of the proposed methodology.

关键词： optimisation load shedding power generation planning natural gas technology investment distributed power generation power distribution planning power distribution feeders natural gas pipelines investment decisions load shedding integrated GEDN resilient co-expansion planning natural disasters integrated gas-electric distribution networks social welfare two-stage robust optimisation investment strategies expansion planning mixed integer second-order cone programming problem constraint generation algorithm natural-gas-fired distributed generation

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Adaptive uncertainty sets-based two-stage robust optimisation for economic dispatch of microgrid with demand response

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IET RENEWABLE POWER generation 2020年第18期14卷 3608-3615页

作者： Qiu, Yibin Li, Qi Huang, Lanjia Sun, Cai Wang, Tianhong Chen, Weirong Southwest Jiaotong Univ Sch Elect Engn Chengdu 611756 Peoples R China

It is an effective way to regard the electric vehicles as the demand response for reducing the negative impact of large-scale introduction on the power system. Aiming at the microgrid with demand response, the adaptive uncertainty sets-based two-stage robust optimisation method is established in this study. The coordination of micro-gas turbine, energy storage, and demand response etc. are considered in the economic dispatch model. To effectively consider the uncertain variable contained in the microgrid, the concept of adaptive uncertainty sets is proposed in this study. The uncertainty sets are achieved by the long short-term memory network and modified fuzzy information granulation. To handle the adaptive uncertainty sets-based robust optimisation model, the column and constraint generation algorithm and strong duality theory are introduced to decompose the model into a master problem and a subproblem with mixed-integer linear structure. To verify the performance of the proposed adaptive uncertainty sets-based two-stage robust optimisation method, measured data from a plateau city of China are introduced in the simulation test. The simulation results demonstrate the effectiveness of the model and solution strategy.

关键词： duality (mathematics) fuzzy set theory linear programming distributed power generation power generation dispatch gas turbines integer programming power generation economics demand side management recurrent neural nets power engineering computing electric vehicles energy storage demand response robust optimisation model adaptive uncertainty sets-based two-stage robust optimisation method electric vehicles economic dispatch model power system microgrid microgas turbine coordination energy storage long short-term memory network modified fuzzy information granulation constraint generation algorithm column generation algorithm strong duality theory mixed-integer linear structure simulation test

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Optimal allocation of soft open points in active distribution network with high penetration of renewable energy generations

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IET generation TRANSMISSION & DISTRIBUTION 2020年第26期14卷 6732-6740页

作者： Cong, Pengwei Hu, Zechun Tang, Wei Lou, Chengwei Zhang, Lu Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China China Agr Univ Coll Informat & Elect Engn Beijing 100083 Peoples R China Aston Univ Sch Engn & Appl Sci Birmingham W Midlands England

With the integration of more and more renewable energy generations (REGs), the structure of traditional distribution networks is hard to accommodate the volatile power injections of REGs. As a new power electronic device, soft open point (SOP) can be installed to control both active and reactive power flow among active distribution networks (ADNs). This paper presents a comprehensive optimization method for allocating SOPs within an ADN with high penetration of REGs. In order to find proper SOP candidate locations, a selection strategy based on two technical indices is proposed. To mitigate the risk of voltage violation caused by REG forecast errors and improve the adaptiveness of allocation results, a two-stage robust optimization model for SOP allocation is formulated to minimize the total cost of SOP investment and network operation. The proposed model is converted into a mixed-integer second-order cone programming (MISCOP) problem, which is then decoupled into a master problem of planning and a subproblem of operation and solved by column and constraint generation (CCG) algorithm. Simulation results show that the proposed method can effectively find the optimal SOP allocation schemes. Comparisons with different mathematical formulation and solution methods show the advantages of the proposed method.

关键词： reactive power distributed power generation integer programming load flow power distribution planning power generation planning soft open point active distribution network renewable energy generations volatile power injections power electronic device reactive power flow ADN comprehensive optimisation method SOP candidate locations two-stage robust optimisation model network operation second-order cone programming problem constraint generation algorithm optimal SOP allocation IEEE 123 bus systems IEEE 33 bus systems PG& E 69 bus systems REG forecast errors column and constraint generation algorithm

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Robust expansion co-planning of electricity and natural gas infrastructures for multi energy-hub systems with high penetration of renewable energy sources

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IET RENEWABLE POWER generation 2019年第13期13卷 2287-2297页

作者： Karamdel, Shahab Moghaddam, Mohsen Parsa Tarbiat Modares Univ Dept Elect Engn Tehran Iran

High penetration of renewable energy sources will cause crucial challenges for future energy systems. This study presents a three-level model for adaptive robust expansion co-planning of electricity and natural gas infrastructures in multi-energy-hub networks, which is robust against uncertainties of maximum production of wind generation and gas-fired power plants as well as estimated load levels. The proposed min-max-min model is formulated as a mixed integer linear programming problem. The first level minimises the investment cost of electricity and natural gas infrastructures, the worst possible case is determined through the second level, and the third level minimises the overall operation cost under that condition. To solve this model, the final minimisation problem is replaced by its Karush-Kuhn-Tucker conditions and a two-level problem is determined. Finally, by using the column and constraint generation algorithm the original problem is decomposed to master and sub-problems and the optimal solution is derived iteratively. The proposed robust expansion co-planning model is tested on modified Garver's 6-hub, modified IEEE RTS 24-hub, and modified IEEE 118-hub test systems and numerical results show its effectiveness to cope with uncertainties with regard to control conservativeness of the plan.

关键词： load flow renewable energy sources linear programming minimax techniques integer programming power generation planning natural gas technology adaptive robust expansion co-planning electricity natural gas infrastructures multienergy-hub networks wind generation min-max-min model mixed integer linear programming problem final minimisation problem two-level problem robust expansion co-planning model modified IEEE 118-hub test systems multienergy-hub systems high penetration renewable energy sources future energy systems three-level model gas-fired power plants constraint generation algorithm estimated load levels

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