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Optimal Energy Scheduling for Data Center With Energy Nets Including CCHP and Demand Response

IEEE ACCESS 2021年 9卷 6137-6151页

作者： Wang, Dongxiao Xie, Changhong Wu, Runji Lai, Chun Sing Li, Xuecong Zhao, Zhuoli Wu, Xueqing Xu, Yi Lai, Loi Lei Wei, Jinxiao Guangdong Univ Technol Sch Automat Dept Elect Engn Guangzhou 510006 Peoples R China Brunel Univ London Brunel Interdisciplinary Power Syst Res Ctr London UB8 3PH England Guangdong Foshan Power Construct Corp Grp Co Ltd Foshan 528010 Peoples R China

Internet data centers are growing rapidly in recent years and they operate with intensive energy activity. Combined cooling, heating and power (CCHP) brings new opportunities for reducing the electricity cost in internet data centers. The main objective of this study is to optimize the energy resources scheduling in the data center coupled energy nets considering the involvement of CCHP and different demand response techniques. In this paper, internet data center coupled energy nets are proposed, where power grid, solar photovoltaic, CCHP, and battery energy storage systems are the primary energy sources. The adjunct residential buildings and commercial buildings near the internet data centers are also included in the proposed energy nets, where different types of load and demand response characteristics are utilized. A two-stage optimized energy management model considering the coordinated operation of CCHP and demand response technologies is established for internet data center coupled energy nets. In the day-ahead stage, the control objective is to minimize system cost while satisfying various constraints. Consider the electricity tariff chance between day-ahead market and real-time market, real-time control is implemented to minimize the imbalance cost between two electricity markets. Case studies are conducted on a practical internet data center coupled energy nets in Foshan City, China. It is observed that the proposed control framework can optimally schedule the energy resources in the energy network to meet system demand and improve the energy efficiency. The economic evaluation demonstrates that the proposed control scheme reduces system daily cost by 22.01%.

关键词： Data centers Real-time systems Load management Batteries Turbines Resistance heating Servers Combined cooling heating and power data centers mixed integer linear programming renewable energy sources two-stage optimal scheduling

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Unit commitment optimization of a micro-grid with a MILP algorithm: Role of the emissions, bio-fuels and power generation technology

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ENERGY REPORTS 2021年 7卷 8639-8651页

作者： Nicolosi, Francesco F. Alberizzi, Jacopo C. Caligiuri, Carlo Renzi, Massimiliano Free Univ Bozen Bolzano Fac Sci & Technol Piazza Univ 5 I-39100 Bozen Bolzano South Tyrol Italy

Management strategies of complex energy systems composed by different technologies is mandatory to exploit optimally the characteristics of each power generator, to reduce the cost of energy, the impact of greenhouse gases emissions and to increase the penetration of mini- and micro-grids into energy systems. To this purpose, optimization methods and algorithms have to be developed to assess the unit commitment of generators and to suggest decision variables in the definition of the emission costs. In this paper, a novel mixed integer linear programming (MILP) optimization algorithm has been developed to compute the optimal management of a micro-energy grid composed either by four Internal Combustion Generators (ICGs), or three ICGs and a Micro Gas Turbine (MGT). The algorithm optimizes a multi objective function that takes in consideration the total cost, the NOx and the CO2 emissions of the system, while setting some technological constraints, like start-ups and transients that are typically neglected. Moreover, different fuelling of the devices is evaluated. The model proved the importance of including an accurate model of the greenhouse gases emissions as they can significantly affect the optimization results. Furthermore, it proved to be very flexible and to be a proper basis to be adopted in more complex systems embedding energy storage devices and renewable energy systems. (C) 2021 The Author(s). Published by Elsevier Ltd.

关键词： Energy systems unit commitment mixed integer linear programming Internal Combustion Engines Micro Gas Turbine Greenhouse gases emissions

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Revisiting the Optimization of Cauchy Reed-Solomon Coding Matrix for Fault-Tolerant Data Storage

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IEEE TRANSACTIONS ON COMPUTERS 2021年第8期71卷 1839-1846页

作者： Makovenko, Mykyta Cheng, Min Tian, Chao Texas A&M Univ Dept Ind & Syst Engn College Stn TX 77843 USA Texas A&M Univ Dept Elect & Comp Engn College Stn TX 77843 USA

Cauchy Reed-Solomon (CRS) codes are a class of erasure-resilient codes which are widely applicable in modern data storage systems. Several existing works have considered making the coding process of the extended CRS codes more efficient. It has been found that this efficiency is highly dependent on the underlying Cauchy coding matrices, particularly, the density of the associated bitmatrices. In this work, we revisit the problem of optimizing the coding bitmatrices, and propose three approaches aiming to find the bitmatrices with the lowest density in this context, namely a mixed integer linear programming approach, a local optimal algorithm with heuristic perturbation, and a branch-and-bound algorithm. Experimental results show that the proposed approaches are able to find bitmatrices with significantly lower density than those found using existing techniques. Moreover, the local optimal algorithm with heuristic perturbation is surprisingly efficient in finding good solutions under constrained computation time.

关键词： Codes Encoding Systematics Reed-Solomon codes Perturbation methods Generators Heuristic algorithms Reed-Solomon codes erasure codes integer programming mixed integer linear programming

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Optimal Operation of Transient Gas Transport Networks

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OPTIMIZATION AND ENGINEERING 2021年第2期22卷 735-781页

作者： Hoppmann-Baum, Kai Hennings, Felix Lenz, Ralf Gotzes, Uwe Heinecke, Nina Spreckelsen, Klaus Koch, Thorsten Tech Univ Berlin Chair Software & Algorithms Discrete Optimizat Str 17 Juni 135 D-10623 Berlin Germany Zuse Inst Berlin Takustr 7 D-14195 Berlin Germany Open Grid Europe GmbH Kallenbergstr 5 D-45141 Essen Germany

In this paper, we describe an algorithmic framework for the optimal operation of transient gas transport networks consisting of a hierarchical MILP formulation together with a sequential linear programming inspired post-processing routine. Its implementation is part of the KOMPASS decision support system, which is currently used in an industrial setting. Real-world gas transport networks are controlled by operating complex pipeline intersection areas, which comprise multiple compressor units, regulators, and valves. In the following, we introduce the concept of network stations to model them. Thereby, we represent the technical capabilities of a station by hand-tailored artificial arcs and add them to network. Furthermore, we choose from a predefined set of flow directions for each network station and time step, which determines where the gas enters and leaves the station. Additionally, we have to select a supported simple state, which consists of two subsets of artificial arcs: Arcs that must and arcs that cannot be used. The goal is to determine a stable control of the network satisfying all supplies and demands. The pipeline intersections, that are represented by the network stations, were initially built centuries ago. Subsequently, due to updates, changes, and extensions, they evolved into highly complex and involved topologies. To extract their basic properties and to model them using computer-readable and optimizable descriptions took several years of effort. To support the dispatchers in controlling the network, we need to compute a continuously updated list of recommended measures. Our motivation for the model presented here is to make fast decisions on important transient global control parameters, i.e., how to route the flow and where to compress the gas. Detailed continuous and discrete technical control measures realizing them, which take all hardware details into account, are determined in a subsequent step. In this paper, we present computational resu

关键词： Transient gas network optimization Hierarchical optimization mixed integer linear programming

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Optimal planning for high renewable energy integration considering demand response, uncertainties, and operational performance flexibility

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ENERGY 2024年 313卷

作者： Adewuyi, Oludamilare Bode Aki, Hirohisa Univ Tsukuba Inst Syst & Informat Engn 1-1-1 Tennodai Tsukuba Ibaraki 3058573 Japan

The operational performance of the grid needs to be considered and incorporated with other verified system flexibility measures, such as energy storage system and demand response, when planning a hybrid renewable energy system with a high fraction of renewable energy sources. Therefore, this study developed an integrated model that considers the effects of time-of-use demand response and risk-based uncertainty analysis on the operational performance of a grid-connected hybrid energy system for high renewable power penetration. A multi-stage approach for optimal scheduling of the energy systems with time-of-use pricing considering uncertainties' impacts on grid and energy storage systems operations is modeled. Techno-economic metrics are developed to assess the decision-making effectiveness of the energy system planning and grid operation under four scenarios based on the information gap decision theory to quantify the energy system and grid operation performance flexibility. The considered scenarios are without and with demand response using risk-neutral, risk-averse, and risk-seeking strategies. The analysis of the results indicates that the risk-averse approach achieves the best techno-economic tradeoff, guaranteeing improved technical performances compared to the other risk strategies for uncertainty management in renewable power outputs and grid operational performances within the cost deviation tolerance limit. The risk-averse strategy further justified the substantial inclusion of energy storage and the adoption of the demand response to provide better operational flexibility to ensure improved grid performance, as seen in the reduced grid loss and improved voltage profile with higher power contribution from the RES. Significantly, the considered model can help system planners manage the tradeoff for efficient system performance under substantial renewable energy integration, considering the impacts of uncertainties.

关键词： Renewable energy integration Uncertainties Time-of-use pricing mixed integer linear programming Second-order cone programming Information gap decision theory

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Algorithmic Development and Industrial Applications of Short-term Hydro Scheduling in the Nordic Power Market 14

Algorithmic Development and Industrial Applications of Short...

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14th IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC)

作者： Skjelbred, Hans Ivar Kong, Jiehong Aasgard, Ellen Krohn Belsnes, Michael Martin Fosso, Olav Bjarte SINTEF Energy Res Dept Energy Syst Trondheim Norway Norwegian Univ Sci & Technol Dept Elect Power Engn Trondheim Norway

ISBN: (纸本)9781665467384

This paper summarizes the algorithmic development and industrial applications of short-term hydro scheduling (STHS) in the Nordic power market. It focuses on the modeling techniques and solution methodologies applied to solve new challenges related to the large-scale integration of non-dispatchable renewable energy in the electric power system. Three optimization and simulation tools developed for STHS are presented. The tools are widely used in the Nordic hydropower industry and are part of business-critical daily scheduling, trading, and energy management operations.

关键词： Optimization mixed integer linear programming Simulation Short-term hydro scheduling

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A Study of Multi-Constraints Emergency Transportation Problem in Disaster Response

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ASIA-PACIFIC JOURNAL OF OPERATIONAL RESEARCH 2021年第2期38卷

作者： Shao, Wenhan Liu, Xiaolu Chen, Jiaming Lu, Zhipeng Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430074 Peoples R China Natl Univ Def Technol Coll Syst Engn Changsha 410073 Peoples R China

The paper studies a real-world Multi-Constraints Emergency Transportation Problem in disaster response under travel time uncertainty(MCETP), whose objective is to determine the set of flights and routes and the airplane and vehicle assignment for delivering necessary living supplies to disaster areas as quickly as possible. The MCETP can be modeled as a variant of the Vehicle Routing Problem (VRP), which considers a heterogeneous fleet with multi-depots delivering supplies to customers in order to minimize the maximal travel time of all routes where travel time of vehicles is predicted by experts because ground transportation network is destroyed and affected degree is unknown. By adding copy points and imaginary depots, the MCETP can be transformed into a variant of the Travelling Salesman Problem (TSP), for which a mixed integer linear programming (MILP) formulation is presented. With the proposed initial solution construction algorithm, vehicle assignment strategy and penalty function, the transformed problem can be solved by the extension of Lin-Kernighan-Helsgaun solver (LKH-3). Computational experiments on two sets of totally 50 instances show that the idea of transforming a challenging complex problem to a widely studied problem and using its state-of-the-art algorithm to solve the original problem is highly effective and efficient for the MCETP. Specifically, it can obtain the same solution quality as GUROBI but with much less time for small-scale instances. Furthermore, the proposed algorithm can solve large-scale instances while GUROBI fails to obtain a feasible solution in most cases in a reasonable time.

关键词： Disaster response emergency transportation VRP TSP mixed integer linear programming LKH-3

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Modeling and solving assembly line worker assignment and balancing problem with sequence-dependent setup times

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SOFT COMPUTING 2021年第20期25卷 12899-12914页

作者： Yilmaz, Hamid Bayburt Univ Fac Engn Ind Engn Dept Bayburt Turkey

Assembly lines appear with various differentiations in order to better include the disabled in the labor market and to increase production efficiency. In this way, the optimal workforce assignment problem that emerges heterogeneously is called assembly line worker assignment and balancing problem (ALWABP). This paper addresses the ALWABP where the simple version is enriched by considering sequence-dependent setup times between tasks. A mixed integer linear programming model is presented, and a simulated annealing algorithm is developed such as an NP-hard problem. In order to test the proposed solutions, 640 benchmark problems in the literature were combined and used. The solutions obtained through using the proposed algorithm are compared with the mixed integer programming model on the small-size test problems. Experimental results show that the proposed algorithm is more effective and robust for a large set of benchmark problems.

关键词： mixed integer linear programming Assembly line balancing Simulated annealing Sequence-dependent setup times

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Stealthy Cyberattacks on Loads and Distributed Generation Aimed at Multi-Transmission Line Congestions in Smart Grids

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IEEE TRANSACTIONS ON SMART GRID 2021年第3期12卷 2518-2528页

作者： Khazaei, Javad Penn State Harrisburg Elect Engn Dept Middletown PA 17057 USA Penn State Univ Architectural Engn Dept University Pk PA 16802 USA

Developing attack models is the first step to understand cyberattacks in smart grids and develop countermeasures. In this article, a three-level nonlinear programming formulation is proposed for false data injection (FDI) cyberattacks that could result in multiple transmission line congestions without being detected by conventional bad data detection (BDD) algorithms. The model is then converted to a mixed integer linear programming (MILP) formulation to guarantee a global optimum exists. A detection framework based on recursive least-square estimation (RLSE) is developed that can successfully detect the stealthy FDIs. The developed model with the detection framework is validated through various case studies in IEEE 118-bus benchmark.

关键词： Biological system modeling Load modeling Power transmission lines Data models Transmission line measurements Smart grids Estimation Stealthy false data injection cyber security mixed integer linear programming state-estimation

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A multi-objective particle swarm optimisation for integrated configuration design and scheduling in reconfigurable manufacturing system

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2021年第13期59卷 3975-3995页

作者： Dou, Jianping Li, Jun Xia, Dan Zhao, Xia Southeast Univ Sch Mech Engn Nanjing Peoples R China Southeast Univ Sch Automat Nanjing Peoples R China Nanjing Univ Finance & Econ Collaborat Innovat Ctr Modern Grain Circulat & Sa Nanjing Peoples R China

To provide accurate capacity and functionality needed for each demand period (DP), a reconfigurable manufacturing system (RMS) is able to change its configuration with time. For the RMS with multi-part flow line configuration that concurrently produces multiple parts within the same family, the cost and delivery time are dependent on its configuration and relating scheduling for any DP. So far, the study on solution method for the integrated optimisation problem of configuration design and scheduling for RMS is scarce. To efficiently find solutions with tradeoffs between total cost and tardiness, a multi-objective particle swarm optimisation (MoPSO) based on crowding distance and external Pareto solution archive is presented to solve practical-sized problems. The devised encoding and decoding methods along with the particle updating mechanism of MoPSO ensure any particle a feasible solution. The comparison between MoPSO and epsilon-constraint method versus small-sized cases illustrates the effectiveness of MoPSO. The comparative results between MoPSO and nondominated sorting genetic algorithm II (NSGA-II) against eight problems show that the MoPSO outperforms the NSGA-II in both solution quality and computation efficiency for the integrated optimisation problem.

关键词： Reconfigurable manufacturing system configuration design scheduling mixed integer linear programming multi-objective particle swarm optimisation

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