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robust optimal dispatch of distribution system considering carbon capture and demand response

FRONTIERS IN ENERGY RESEARCH 2022年 10卷

作者： Ge, Yulin Wang, Chong Hao, Yuchen Han, Guigang Lu, Yu Hohai Univ Nanjing Peoples R China State Grid Jiangsu Elect Power Co Ltd Nanjing Peoples R China China Elect Power Res Inst Nanjing Peoples R China

Participation of carbon capture power plants and demand response in power system dispatch is an important mean to achieve the carbon-neutral goal. In order to take into account uncertainty of wind power and low-carbon economic operation of the distribution system, a two-stage robust optimal dispatch model of the distribution system considering carbon capture and demand response is proposed in this paper. In the day-ahead stage, the unit commitment plan and the price demand response (PDR) schedule are established. In the intra-day stage, the carbon related cost is included in the optimization objective. The strategies for the generators and the incentive demand response (IDR) are optimized under the worst-case output of wind power based on the results of the day-ahead stage. Karush-Kuhn-Tucker (KKT) conditions and the column-and-constraint generation (C & CG) algorithm are used to solve the proposed two-stage model. A revised IEEE 33-bus distribution system is used to verify the feasibility and effectiveness of the proposed model.

关键词： carbon capture demand response carbon trading column-and-constraint generation algorithm two-stage robust optimization

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Distributed adaptive expansion approach for transmission and distribution networks incorporating source-contingency-load uncertainties

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2022年 136卷 107711-107711页

作者： Liu, Jia Tang, Zao Zeng, Peter Pingliang Li, Yalou Wu, Qiuwei Hangzhou Dianzi Univ Dept Automat Hangzhou 310018 Peoples R China Zhejiang Univ Dept Elect Engn Hangzhou 310027 Peoples R China China Elect Power Res Inst Beijing 100192 Peoples R China Tech Univ Denmark Ctr Elect Power & Energy CEE Dept Elect Engn DK-2800 Lyngby Denmark

The plurality of active distribution networks (DNs) has made it ready to connect the transmission network (TN) for the dynamic power interaction. Coordinated expansion of transmission and distribution networks is the key to the economic and reliable operation of the integrated system in the future uncertain environment. This paper proposes a distributed decision-making framework to determine the robust TN expansion solution and stochastic DN expansion solutions. Uncertainty sets are deployed to model the uncertainties in TN while the scenario-based technique is implemented to tackle the uncertainties in DNs. To guarantee the consistency of transmissiondistribution interaction, the deviation penalties of the interactive power are introduced into the objective function of each level. The iteration solution algorithm is used to transform the robust min-max-min problem for transmission expansion into a two-stage mixed-integer linear programming problem, and coordinate transmission and distribution expansion problems in a parallel manner. Case studies validate the effectiveness and high performance of the proposed distributed expansion method and the solution procedure.

关键词： Distributed expansion Transmission and distribution Stochastic optimization two-stage robust optimization Source-contingency-load uncertainties

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robust optimal dispatching of integrated electricity and gas system considering refined power-to-gas model under the dual carbon target

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JOURNAL OF CLEANER PRODUCTION 2022年 371卷

作者： Qin, Guangyu Yan, Qingyou Kammen, Daniel M. Shi, Chaofan Xu, Chuanbo North China Elect Power Univ Sch Econ & Management Beijing 102206 Peoples R China North China Elect Power Univ Beijing Key Lab New Energy & Low Carbon Dev Beijing 102206 Peoples R China Univ Calif Renewable & Appropriate Energy Lab Berkeley CA 94720 USA Univ Calif Energy & Resources Grp Berkeley CA 94720 USA Univ Calif Goldman Sch Publ Policy Berkeley CA 94720 USA Univ Calif Dept Nucl Engn Berkeley CA 94720 USA

With the advancement of China's energy revolution and the proposal of carbon neutrality, an integrated elec-tricity and gas system (IEGS) characterized by regional energy interconnection and interaction has developed rapidly. IEGS integrates renewable energy such as wind energy and solar energy, improves the comprehensive utilization rate of regional energy, and reduces pollution and greenhouse gas emissions. Considering the con-sumption problem caused by uncertainty in wind power and photovoltaics, firstly, adding Power-to-Gas(P2G) equipment in IEGS to build a refined model of P2G operation, secondly, the two-stage robust optimization model of min-max-min structure is established. The improved genetic algorithm is used to solve the scheduling scheme with the lowest operation cost in the worst scenario, and the uncertain adjustment parameters are introduced to realize the flexible adjustment of the conservatism of the scheduling scheme. Through the simu-lation analysis of the difference of system operating cost under different scenarios and adjustment parameters, the validity of the established model and solution algorithm is verified. The results show that the final cost of robust optimization is lower than that of deterministic optimization, and choosing to add P2G equipment can reduce the cost by 6.19% and 5.96% in winter and summer respectively.

关键词： Integrated electricity and gas system Refined power-to-gas model two-stage robust optimization Uncertain adjustment parameters

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two-stage robust Optimal Scheduling of "Nearly-Zero-Carbon Park" Considering the Synergy of Waste Disposal and Carbon Capture Gas-Fired Power Plants

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FRONTIERS IN ENERGY RESEARCH 2022年 10卷

作者： Yao, Zongjun Zhang, Tieyan Zhao, Yan Shenyang Univ Technol Sch Elect Engn Shenyang Peoples R China Shenyang Inst Engn Sci & Technol Dept Shenyang Peoples R China Key Lab Reg Multienergy Syst Integrat & Control Li Shenyang Peoples R China

Aiming at the demand characteristics of "Nearly-zero Carbon Parks" (NZCP) powered by all renewable energy for low-carbon emissions and waste reduction, a NZCP topology and a two-stage robust optimal scheduling model based on the Carbon Capture Gas-fired Power Plant (CCGPP)-Power to Gas(P2G)-Waste Disposal (WD) collaborative model are proposed in this paper. First, the relationship between WD, CCGPP and energy supply and demand that can be generated from renewable energy is studied, and according to the system energy regulation requirements, the energy flexibility of residential loads, small industrial loads and electric vehicles are considered, and the NZCP mathematical model based on the CCGPP-P2G-WD collaborative model is established. Secondly, NZCP energy supply and demand characteristics are studied, and an energy coordination model for NZCP energy conversion and storage powered by 100% renewable energy is established. Then, wind power, photovoltaic power, and combined demand response uncertainties are considered, and the NZCP two-stage robust hybrid optimal dispatch model is established based on second-order cone relaxation and dual theory. Finally, the simulation analysis is carried out based on the operation data of the integrated energy system of a park in a certain area in northern my country. The simulation results show that the proposed two-stage robust optimal scheduling model of NZCP coordinated with WD and CCGPP can effectively improve the economy of the system at a higher energy balance level, improve the absorption capacity of renewable energy, and provide better multi-time scale energy regulation characteristics for NZCP with a larger energy supply scale.

关键词： nearly zero carbon park carbon capture gas-fired power plants waste disposal demand side response two-stage robust optimization

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two-stage adjustable robust optimal dispatching model for multi-energy virtual power plant considering multiple uncertainties and carbon trading

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JOURNAL OF CLEANER PRODUCTION 2022年第0期336卷 130400-130400页

作者： Yan, Qingyou Zhang, Meijuan Lin, Hongyu Li, Wei North China Elect Power Univ Sch Econ & Management Beijing 102206 Peoples R China North China Elect Power Univ Beijing Key Lab New Energy & Low Carbon Dev Beijing 102206 Peoples R China

In the context of high proportion renewable energy access and multi-energy synergy, low-carbon multi-energy virtual power plants (MEVPP) are gradually getting hot. This paper introduces the carbon trading mechanism into a MEVPP and constructs a novel dynamic robust optimization model considering the source-load multiple uncertainties. Firstly, the Adaptive Neuro-Fuzzy Inference System and spectral clustering method are applied to generate the source-side output scenarios, and then by the strength of the budget uncertainty set, the concept of uncertainty measurement is established to characterize the system's uncertainties. Secondly, a two-stage robust adjustable scheduling model is developed by integrating the carbon trading cost into the objective function, and the model derived from the decomposition is solved using the column and constraint generation algorithm and strong duality theory. Finally, the economics, robustness, and low-carbon performance of the model in addressing the MEVPP scheduling issues are examined by case studies. The proposed model is capable of getting the optimal cost under the worst-case scenario, dynamically balancing the economics and robustness of the scheduling strategy with adjustable uncertainty measurement, as well as contributing to guidance of energy saving and emission reduction in MEVPP.

关键词： Multi-energy virtual power plant Carbon trading mechanism two-stage robust optimization Budget uncertainty set

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two-stage robust telemedicine assignment problem with uncertain service duration and no-show behaviours

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COMPUTERS & INDUSTRIAL ENGINEERING 2022年第0期169卷 108226页

作者： Ji, Menglei Wang, Shanshan Peng, Chun Li, Jinlin Beijing Inst Technol Sch Management & Econ Beijing 100081 Peoples R China HEC Montreal Dept Decis Sci Montreal PQ H3T 2A7 Canada GERAD Montreal PQ H3T 2A7 Canada

The current pandemic of COVID-19 has caused significant strain on medical center resources, which are the main plac healthcare managers to make an effective assignment plan for the patients and telemedical doctors when providing telemedicine services. Motivated by this, we present the first comprehensive study of a two-stage robust telemedicine assignment problem when three different sources of uncertainty are incorporated, including uncertain service duration, no-show behaviours of both patients and telemedical doctors. From an algorithmic viewpoint, we propose an efficient nested column-and-constraint generation (C&CG) solution scheme that decomposes the model into an outer level problem and an inner level problem. Our results show that we can solve the problems of realistic sizes within a reasonable time (e.g., up to 100 patients, 10 telemedical doctors, and 200 scenarios within two hours). On the empirical side, we demonstrate how the hyper-parameters make a balance between cost management and the coverage level of the served patients in the presence of three different sources of uncertainty. Our comparison with a two-stage stochastic programming model implies that our model is not overly conservative and seems to provide a relatively cheaper modeling alternative that requires much less information support when hedging against three different sources of uncertainty under a worst-case situation.

关键词： Telemedicine assignment two-stage robust optimization Mixed-integer linear program NestedC&CG Uncertain service duration No-show behaviours

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Integrated Mobile Command Platform for UAV Operation, Inspection and Dispatching 22

Integrated Mobile Command Platform for UAV Operation, Inspec...

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Proceedings of the 2022 4th International Conference on Software Engineering and Development

作者： Yonglong Ma Binghua Fang Mingjie Xiao Xiaocong Wang Guangzhou Conghua Power Supply Bureau Guangzhou Power Supply Bureau China

ISBN: (纸本)9781450397940

In order to effectively solve the problems of poor infrastructure in remote areas and high human distribution cost in developed areas, the design of integrated UAV network for urban and rural logistics and the scheduling method of UAV onboard platform are *** the situation that self-run logistics stocks goods in advance according to the forecast of demand, in this essay, under the distribution situation of dispersed customer demand and frequent demand fluctuation over time, the uncertain demand of customers is described by polyhedron set, and the two-stage robust optimization model of plane-carrying platform scheduling is established, and the L-type algorithm is designed to solve the *** experimental results show that when, the target value obtained by the robust optimization method is close to that of the stochastic programming, and the scheduling path obtained by the two methods in the first stage is basically the same, except for the difference in the determination of the service relationship in the second *** the budget level was increased from 0.0 to 0.2, the number of on-board platforms used remained at 8, while the penalty cost and unmet requirements ***: The efficiency of robust optimization is better than that of stochastic programming, and the decision economy is greatly improved by relaxing the model robustness *** the face of uncertain demand, the system will first adjust the scheduling path of the on-board platform, and then consider increasing the number of on-board platforms. The scheduling scheme shows a rule of gradual adjustment.

关键词： unmanned aerial vehicle platform scheduling integration of urban and rural logistics two-stage robust optimization L-type algorithm

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robust collaborative maintenance logistics network design and planning

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INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 2022年第0期244卷 108370-108370页

作者： Kafiabad, Shayan Tavakoli Zanjani, Masoumeh Kazemi Nourelfath, Mustapha Interuniv Res Ctr Enterprise Networks Logist & Tr Montreal PQ Canada Concordia Univ Dept Mech Ind & Aerosp Engn 1515 St Catherine St W Montreal PQ H3G 1M8 Canada Laval Univ Dept Mech Engn Quebec City PQ Canada

Maintenance service providers to advanced technical devices are confronted with uncertain demand, high cost of components, and the need for certified operators. Collaboration in terms of sharing scarce resources among different facilities in a maintenance logistics network is expected to reduce the delays in the delivery of repaired devices. This study proposes a two-stage robust optimization model for collaborative design and planning of maintenance networks under demand uncertainty. The goal of this model is to determine the optimal allocation of customers to each maintenance center along with the initial stock level of different components in each facility so as to minimize the cost of late deliveries under worst-case demand scenarios. Component and operator sharing strategies are proposed as the recourse actions in this model to hedge against the demand surge. The proposed approach is compared with a deterministic model by the aid of Monte-Carlo simulation on several test instances inspired by a real case study. Our numerical experiments demonstrate the significance of adopting the proposed collaborative mechanisms among maintenance facilities in terms of cost reduction, especially when the demand fluctuation is relatively high.

关键词： Maintenance logistics network Collaboration Repairable components two-stage robust optimization Monte-Carlo simulation

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robust optimization for hurricane preparedness

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INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 2020年 221卷 107464-107464页

作者： Wang, Xinfang (Jocelyn) Paul, Jomon A. Georgia Southern Univ Dept Logist & Supply Chain Management Coll Business Adm Statesboro GA 30460 USA Kennesaw State Univ Dept Econ Finance & Quantitat Anal Kennesaw GA 30144 USA

This paper applies the concept of social cost (i.e., logistic, deprivation, and fatality costs) to analyze the optimal deployment time, location and capacities of stockpiles for Points of Distribution (PODs) in hurricane preparedness. We first propose a single-stage, adaptive robust model to determine the optimal deployment time, given the time-variant characteristics of hurricanes. The model is nested in an optimal stopping-time framework that captures the trade-offs between increasing deployment costs and reduced uncertainty as the hurricane approaches landfall. Once the optimal deployment time has been determined, we then propose a less conservative, two-stage robust optimization model with recourse actions to determine the PODs' location, stockpile capacities and flow. Tested on a case study, results show that 1) a non-adaptive model leads to poor decisions about the optimal deployment time;2) improperly modeled deprivation costs pose significant hidden risks to decisionmakers;3) deprivation costs increase $3-5 for every dollar cut when the available budget is strictly binding;and 4) significant savings in social cost result from the wait-and-see strategy implemented through the two-stage robust model.

关键词： Disaster planning Location-allocation Deprivation costs Adaptive robust optimization two-stage robust optimization Recourse actions

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Funding allocations for disaster preparation considering catastrophe insurance

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SOCIO-ECONOMIC PLANNING SCIENCES 2022年 84卷

作者： Liu, Tongxin Shao, Jianfang Wang, Xihui Univ Sci & Technol China Sch Management Hefei Peoples R China Univ Sci & Technol China Int Inst Finance Sch Management Hefei Peoples R China

In disaster management, material reserves are defined by uncertain demand. Overstock may lead to wasted funds, while shortages increase purchase pressures in the post-disaster environment. The funding allocation for pre disaster reserve and post disaster procurement is a dilemma. Catastrophe insurance is intended to moderate this dilemma. The relief organization is intended to insure against the surge in demand that accompanies disaster, which may require it to purchase a large amount of materials urgently post disaster. This study develops a funding allocation optimization model for disaster response planning and preparedness, with the added leverage of catastrophe insurance. The goal of this paper is to determine the relief organization's optimal allo-cation of funds for material reserve, insurance premiums and possible post-disaster purchases to minimize long-term total relief costs. We model the problem as a two-stage robust optimization model designed to minimize the overall costs, and adopt an efficient column and constraint generation algorithm to calculate it. Using data from the flood disasters of Heilongjiang, China, some of the scenarios indicate a reduction in the government's relief costs by adopting insurance instead of applying option contract or just returning unused relief supplies. Our study confirms the advantages of catastrophe insurance in leveraging capital, and reducing disaster material overstocks and insufficient risks.

关键词： Humanitarian relief Funding allocation Catastrophe insurance two-stage robust optimization

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