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Pathway planning of Nuclear Power Development Incorporating Assessment of Nuclear Event Risk

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JOURNAL OF MODERN POWER SYSTEMS AND CLEAN ENERGY 2024年第2期12卷 500-513页

作者： Yang, Xinxin Xue, Yusheng Cai, Bin Southeast Univ Sch Elect Engn Nanjing Peoples R China NARI Grp Corp State Grid Elect Power Res Inst Nanjing Peoples R China State Key Lab Smart Grid Protect & Operat Control Nanjing Peoples R China

The nuclear event risk (NER) is an important and disputed factor that should be reasonably considered when planning the pathway of nuclear power development (NPD) to assess the benefits and risks of developing nuclear power more objectively. This paper aims to explore the impact of nuclear events on NPD pathway planning. The influence of nuclear events is quantified as a monetary risk component, and an optimization model that incorporates the NER in the objective function is proposed. To optimize the pathway of NPD in the lowcarbon transition course of power supply structure evolution, a simulation model is built to deduce alternative NPD pathways and corresponding power supply evolution scenarios under the constraint of an exogenously assigned carbon emission pathway (CEP); moreover, a method is proposed to describe the CEP by superimposing the maximum carbon emission space and each carbon emission reduction (CER) component, and various CER components are clustered considering the emission reduction characteristics and resource endowments of different power generation technologies. A case study is conducted to explore the impact of NER and its risk valuation uncertainty on NPD pathway planning. The method presented in this paper allows the impact of nuclear events on NPD pathway planning to be quantified and improves the level of coordinated optimization of benefits and risks.

关键词： Costs Carbon dioxide planning Renewable energy sources Power generation Generators Power systems Low-carbon transition power supply structure evolution nuclear power development (NPD) quantitative analysis nuclear event risk evaluation TECHNOLOGIES

Enterprise-wide optimization of integrated planning and scheduling for refinery-petrochemical complex with heuristic algorithm

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Frontiers of Chemical Science and Engineering 2023年第10期17卷 1516-1532页

作者： Lifeng Zhang Haoyang Hu Zhiquan Wang Zhihong Yuan Bingzhen Chen State Key Laboratory of Chemical Engineering Department of Chemical EngineeringTsinghua UniversityBeijing 100084China Department of Chemical Engineering Tsinghua UniversityBeijing 100084China

This paper focuses on the integrated problem of long-term planning and short-term scheduling in a largescale refinery-petrochemical complex,and considers the overall manufacturing process from the upstream refinery to the downstream petrochemical site.Different time scales are incorporated from the planning and scheduling subproblems.At the end of each discrete time period,additional constraints are imposed to ensure material balance between different time scales.Discrete time representation is applied to the planning subproblem,while continuous time is applied to the scheduling of ethylene cracking and polymerization processes in the petrochemical site.An enterprise-wide mathematical model is formulated through mixed integer nonlinear programming.To solve the problem efficiently,a heuristic algorithm combined with a convolutional neural network(CNN),is proposed.Binary variables are used as the CNN input,leading to the integration of a data-driven approach and classical optimization by which a heuristic algorithm is established.The results do not only illustrate the detailed operations in a refinery and petrochemical complex under planning and scheduling,but also confirm the high efficiency of the proposed algorithm for solving large-scale problems.

关键词： planning scheduling refinery-petrochemical convolutional neural network heuristic algorithm

Electrical System planning of Large-scale Offshore Wind Farm Based on N+ Design Considering Optimization of Upper Power Limits of Wind Turbines

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Journal of Modern Power Systems and Clean Energy 2023年第6期11卷 1784-1794页

作者： Shurong Wei Hao Wang Yang Fu Fangxing Li Lingling Huang IEEE Department of Electrical Power Engineering Shanghai University of Electric PowerShanghai 200090China Department of Electrical Engineering and Computer Science The University of TennesseeKnoxvilleTN 37996USA

Electrical system planning of the large-scale offshore wind farm is usually based on N-1 security for equipment lectotype. However, in this method, owing to the aggregation effect in large-scale offshore wind farms, offshore electrical equipment operates under low load for long periods, thus wasting resources. In this paper, we propose a method for electrical system planning of the large-scale offshore wind farm based on the N+ design. A planning model based on the power-limited operation of wind turbines under the N+ design is constructed, and a solution is derived with the optimization of the upper power limits of wind turbines. A comprehensive evaluation and game analysis of the economy, risk of wind abandonment, and environmental sustainability of the planned offshore electrical systems have been conducted. Moreover, the planning of an infield collector system, substation, and transmission system of an offshore electrical system based on the N+ design is integrated. For a domestic offshore wind farm, evaluation results show that the proposed planning method can improve the efficiency of wind energy utilization while greatly reducing the investment cost of the electrical system.

关键词： Electrical system N+design offshore wind farm planning optimization

Two-Stage Production planning Under Stochastic Demand:Case Study of Fertilizer Manufacturing

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Computers, Materials & Continua 2022年第1期70卷 1195-1207页

作者： Chia-Nan Wang Shao-Dong Syu Chien-Chang Chou Viet Tinh Nguyen Dang Van Thuy Cuc Department of Industrial Engineering and Management National Kaohsiung University of Science and TechnologyKaohsiung80778Taiwan Sunline NP Telecommunications Co.Ltd Kaohsiung80778Taiwan Department of Shipping Technology National Kaohsiung University of Science and TechnologyKaohsiung80778Taiwan Faculty of Commerce Van Lang UniversityHo Chi Minh City70000Vietnam Faculty of Industrial Engineering and Management International UniversityHo Chi Minh City70000Vietnam

Agriculture is a key facilitator of economic prosperity and nourishes the huge global population.To achieve sustainable agriculture,several factors should be considered,such as increasing nutrient and water efficiency and/or improving soil health and quality.Using fertilizer is one of the fastest and easiest ways to improve the quality of nutrients inland and increase the effectiveness of crop yields.Fertilizer supplies most of the necessary nutrients for plants,and it is estimated that at least 30%-50%of crop yields is attributable to commercial fertilizer nutrient inputs.Fertilizer is always a major concern in achieving sustainable and efficient agriculture.Applying reasonable and customized fertilizerswill require a significant increase in the number of formulae,involving increasing costs and the accurate forecasting of the right time to apply the suitable formulae.An alternative solution is given by two-stage production planning under stochastic demand,which divides a planning schedule into two stages.The primary stage has non-existing demand information,the inputs of which are the proportion of raw materials needed for producing fertilizer products,the cost for purchasing materials,and the production cost.The total quantity of purchased material and produced products to be used in the blending process must be defined to meet as small as possible a paid cost.At the second stage,demand appears under multiple scenarios and their respective possibilities.This stage will provide a solution for each occurring scenario to achieve the best profit.The two-stage approach is presented in this paper,the mathematical model of which is based on linear integer programming.Considering the diversity of fertilizer types,themathematicalmodel can advise manufacturers about which products will generate as much as profit as possible.Specifically,two objectives are taken into account.First,the paper’s thesis focuses on minimizing overall system costs,e.g.,including inventory cost,purchasing cost,unit cost,and ordering cost at Stag

关键词： Two-stage stochastic programming demand uncertainty planning blending fertilizer

Incorporating Optimal Operation Strategies into Investment planning for Wind/Electrolyser System

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CSEE Journal of Power and Energy Systems 2022年第2期8卷 347-359页

作者： Yi Zheng Shi You Henrik W.Bindner Marie Munster the Department of Electrical Engineering Technical University of Denmark2800 Kgs.LyngbyDenmark the Department of Technology Management and EconomicsTechnical University of Denmark2800 Kgs.LyngbyDenmark

As a conducive and prevalent technique for producing green hydrogen,hybrid wind-based electrolyzer system requires both effective planning and operation to realize its techno-economic value.Majority of the existing studies are focused on either of these two,but none of them sufficiently emphasize on their interrelationship.In this paper,we propose a two-stage multi-objective optimization framework to reveal optimal investment plans considering various operational strategies,such as economic revenue maximization and green hydrogen production maximization.The results reveal that:1)A trade-off exists between system investment and the capacity to accomplish optimal operational performance.For example,the system demands flexibility to boost operational profits,but this results in high investment costs.2)Differentiated operation objectives generate different component capacities during the planning phase.3)Regarding a wind-hydrogen system with gas storage,the Pareto optimal design manifesting the trade-off between system investment and prime operational performance can be actualized along the margins of a feasible solution.

关键词： Hybrid wind-hydrogen system multi-objective optimization optimal operation planning sizing

Improved sparrow search algorithm for RFID network planning

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中国邮电高校学报：英文版 2023年第1期30卷 93-102页

作者： Zhang Jiangbo Zheng Jiali Quan Yixuan Lin Zihan Xie Xiaode School of Computer Electrics and InformationGuangxi UniversityNanning 530004China Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi UniversityNanning 530004China

To solve the problem that the performance of the coverage,interference rate,load balance andweak power in the radio frequency identification(RFID)network planning.This paper proposes an elite opposition-based learning and Lévy flight sparrow search algorithm(SSA),which is named elite opposition-based learning and Levy flight SSA(ELSSA).First,the algorithm initializes the population by an elite opposed-based learning strategy to enhance the diversity of the population.Second,Lévy flight is introduced into the scrounger’s position update formula to solve the situation that the algorithm falls into the local optimal solution.It has a probability that the current position is changed by Lévy flight.This method can jump out of the local optimal solution.In the end,the proposed method is compared with particle swarm optimization(PSO)algorithm,grey wolf optimzer(GWO)algorithm and SSA in the multiple simulation tests.The simulated results showed that,under the same number of readers,the average fitness of the ELSSA is improved respectively by 3.36%,5.67%and 18.45%.By setting the different number of readers,ELSSA uses fewer readers than other algorithms.The conclusion shows that the proposed method can ensure a satisfying coverage by using fewer readers and achieving higher comprehensive performance.

关键词： radio frequency identification network planning sparrow search algorithm elite opposition-based learning Levy flight

Parallel planning:A New Motion planning Framework for Autonomous Driving

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IEEE/CAA Journal of Automatica Sinica 2019年第1期6卷 236-246页

作者： Long Chen Xuemin Hu Wei Tian Hong Wang Dongpu Cao Fei-Yue Wang IEEE School of Data and Computer Science Sun Yat-sen University the School of Computer Science and Information Engineering Hubei University Institute of Measurement and Control Systems Karlsruhe Institute of Technology Department of Mechanical and Mechatronics Engineering University of Waterloo the State Key Laboratory of Management and Control for Complex Systems.Institute of Automation Chinese Academy of Sciences the Research Center for Military Computational Experiments and Parallel Systems Technology National University of Defense Technology

Motion planning is one of the most significant technologies for autonomous driving. To make motion planning models able to learn from the environment and to deal with emergency situations, a new motion planning framework called as"parallel planning" is proposed in this paper. In order to generate sufficient and various training samples, artificial traffic scenes are firstly constructed based on the knowledge from the reality.A deep planning model which combines a convolutional neural network(CNN) with the Long Short-Term Memory module(LSTM) is developed to make planning decisions in an end-toend mode. This model can learn from both real and artificial traffic scenes and imitate the driving style of human drivers.Moreover, a parallel deep reinforcement learning approach is also presented to improve the robustness of planning model and reduce the error rate. To handle emergency situations, a hybrid generative model including a variational auto-encoder(VAE) and a generative adversarial network(GAN) is utilized to learn from virtual emergencies generated in artificial traffic scenes. While an autonomous vehicle is moving, the hybrid generative model generates multiple video clips in parallel, which correspond to different potential emergency scenarios. Simultaneously, the deep planning model makes planning decisions for both virtual and current real scenes. The final planning decision is determined by analysis of real observations. Leveraging the parallel planning approach, the planner is able to make rational decisions without heavy calculation burden when an emergency occurs.

关键词： Autonomous driving artificial traffic scene deep learning emergencies motion planning parallel planning

Periodic Motion planning and Control for Double Rotary Pendulum via Virtual Holonomic Constraints

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IEEE/CAA Journal of Automatica Sinica 2019年第1期6卷 291-298页

作者： Zeguo Wang Leonid B.Freidovich Honghua Zhang Beijing Institute of Control Engineering IEEE the Robotics and Control Laboratory (www.control.tfe.umu.se) Department of Applied Physics and ElectronicsUmea University

Periodic motion planning for an under-actuated system is rather difficult due to differential dynamic constraints imposed by passive dynamics, and it becomes more difficult for a system with higher underactuation degree, that is with a higher difference between the number of degrees of freedom and the number of independent control inputs. However, from another point of view, these constraints also mean some relation between state variables and could be used in the motion planning.We consider a double rotary pendulum, which has an underactuation degree 2. A novel periodic motion planning is presented based on an optimization search. A necessary condition for existence of the whole periodic trajectory is given because of the higher underactuation degree of the system. Moreover this condition is given to make virtual holonomic constraint(VHC) based control design feasible. Therefore, an initial guess for the optimization of planning a feasible periodic motion is based on this necessary condition. Then, VHCs are used for the system transformation and transverse linearization is used to design a static state feedback controller with periodic matrix function gain. The controller gain is found through another optimization procedure. The effectiveness of initial guess and performance of the closed-loop system are illustrated through numerical simulations.

关键词： Double rotary pendulum periodic motion planning under-actuated mechanical systems virtual holonomic constraint (VHC)

A comparison of strategic mine planning approaches for in-pit crushing and conveying, and truck/shovel systems

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International Journal of Mining Science and Technology 2018年第2期28卷 205-214页

作者： M.Nehring P.F.Knights M.S.Kizil E.Hay School of Mechanical and Mining Engineering The University of Queensland

In a global environment where energy and labor are becoming increasingly expensive, continuous mining systems such as In-Pit Crushing and Conveying(IPCC) systems have been advanced as offering a real alternative to conventional truck haulage systems. The implementation of IPCC systems in hard rock operations in open pit mines however requires different and more comprehensive planning approaches in order to adequately reflect the practical aspects associated with these. This paper investigates the impact that these approaches may have on the implementation of IPCC systems on a basic metalliferous deposit amenable to open pit exploitation. A strategic life of mine plan to provide numerous economic indicators for each approach is analyzed and compared to traditional truck haulage systems. The mine planning and evaluation process highlights the increased overall resource recovery that may accompany the use of IPCC systems. This investigation also provides insights into the issues associated with IPCC and the scale and type of operation and orebody that is likely to provide a feasible alternative to truck haulage.

关键词： In pit crusher conveyor systems Strategic mine planning Resource recovery Surface metalliferous mining Mine optimization Truck and shovel