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An MILP-based model for short-term peak shaving operation of pumped-storage hydropower plants serving multiple power grids

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ENERGY 2018年 163卷 722-733页

作者： Cheng, Chuntian Su, Chengguo Wang, Peilin Shen, Jianjian Lu, Jianyu Wu, Xinyu Dalian Univ Technol Inst Hydropower & Hydroinformat Dalian 116024 Peoples R China Yellow River Conservancy Tech Inst Sch Hydraul Engn Kaifeng 475004 Peoples R China East China Elect Power Dispatching & Control Sub Shanghai 200120 Peoples R China

China's power grids have constructed many large pumped-storage hydropower plants (PSHPs) to relieve their increasing peak shaving pressure. Unlike PSHPs in a single power grid, the PSHPs directly operated by the dispatch center of regional power grids are required to simultaneously provide peak regulation services for several subordinate provincial power grids. This makes the daily operation of these PSHPs very challenging for both system operators and researchers. Hence, this paper develops a mixed-integer linear programming (MILP) based model for determining the optimal hourly scheduling of PSHPs serving several provincial power grids. The objective is to minimize the peak-valley difference of the residual load series of each power grid. The performance of individual units in the model will be considered, as well as the head effect for each unit in both generating mode and pumping mode. The study focuses mainly on the linearization of the commonly-used nonlinear objective function, constraints on the operation status of units, and turbine performance curves. These nonlinearities are then linearized with the aid of binary integer variables. The optimization results obtained from two real-world case studies are used to demonstrate that the proposed model is computationally efficient and shows good performance in relieving the peak regulation pressure of each power grid. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Peak shaving Pumped-storage hydropower plant mixed-integer linear programming Multiple power grids

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On the Location of Fog Nodes in Fog-Cloud Infrastructures

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SENSORS 2019年第11期19卷 2445.-2445.页

作者： da Silva, Rodrigo A. C. da Fonseca, Nelson L. S. Univ Estadual Campinas UNICAMP IC Av Albert Einstein 1251 BR-13083852 Campinas SP Brazil

In the fog computing paradigm, fog nodes are placed on the network edge to meet end-user demands with low latency, providing the possibility of new applications. Although the role of the cloud remains unchanged, a new network infrastructure for fog nodes must be created. The design of such an infrastructure must consider user mobility, which causes variations in workload demand over time in different regions. Properly deciding on the location of fog nodes is important to reduce the costs associated with their deployment and maintenance. To meet these demands, this paper discusses the problem of locating fog nodes and proposes a solution which considers time-varying demands, with two classes of workload in terms of latency. The solution was modeled as a mixed-integer linear programming formulation with multiple criteria. An evaluation with real data showed that an improvement in end-user service can be obtained in conjunction with the minimization of the costs by deploying fewer servers in the infrastructure. Furthermore, results show that costs can be further reduced if a limited blocking of requests is tolerated.

关键词： fog computing cloud computing facility location mixed-integer linear programming

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Generator Maintenance Schedule Considering Characteristics of Multi-type Renewable Energy

Generator Maintenance Schedule Considering Characteristics o...

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International Conference on Power System Technology (POWERCON)

作者： Wang, Yanan Shao, Chengcheng Sun, Pei Wang, Xifan Wang, Xiuli Li, Ding Xi An Jiao Tong Univ Shaanxi Key Lab Smart Grid Xian Shaanxi Peoples R China North West Elect Power Design Inst Xian Shaanxi Peoples R China

ISBN: (纸本)9781538664612

Generator maintenance scheduling plays an important role in the reliable and economic operation of power system. However, it is greatly challenged by the integration of large-scale renewable energy due to the intermittent and volatile characteristics of wind and solar power. This paper proposes a maintenance scheduling method for generators based on the load duration curve, considering maintenance requirements and the seasonal characteristics of renewable energy and hydro energy as well as takes the system operation constraints into consideration to minimize the cost of system operation. It constructs the weekly model for system operation and conducts the unified optimization for yearly maintenance scheduling and hydro distribution. The model improves the reliability and decreases the cost effectively. The case study on the modified IEEE-RTS verifies the validity and feasibility of the proposed method.

关键词： maintenance scheduling renewable energy load duration curve mixed-integer linear programming

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Security-Constrained Optimal Scheduling of Transmission Outages With Load Curtailment

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IEEE TRANSACTIONS ON POWER SYSTEMS 2018年第1期33卷 921-931页

作者： Liu, Jenny Kazemi, Mostafa Motamedi, Amir Zareipour, Hamidreza Rippon, Jim AltaLink Calgary AB T2A 7W7 Canada Univ Shahreza Esfahan *** Iran Alberta Elect Syst Operator Calgary AB T2P 0L4 Canada Univ Calgary Dept Elect & Comp Engn Calgary AB T2N 1N4 Canada

Transmission outage scheduling from a transmission facility owner's (TFO's) perspective in a deregulated electricity market has been one of the lesser explored topics in academia. This paper presents a security-constrained one-step optimal transmission outage scheduling model that can be used by any TFO. The proposed mixed-integer linear programming model incorporates a new load shedding methodology that allows transmission branches to radially connected and in-and-out connected buses to be taken out-of-service under outage and contingency conditions, which further improves its versatility. The proposed model is tested on two networks, a simple four-bus system and the 62-bus AltaLink 500 kV/240 kV backbone system in Alberta.

关键词： Contingency analysis DC power flow deregulatedmarket load curtailment mixed-integer linear programming Transmission outage scheduling

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Quality-Aware Control for Optimizing Meat Supply Chains 18

Quality-Aware Control for Optimizing Meat Supply Chains

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18th European Control Conference (ECC)

作者： Sprong, J. P. Lin, X. Maestre, J. M. Negenborn, R. R. Delft Univ Technol Dept Maritime & Transport Technol Delft Netherlands Univ Seville Dept Ingn Sistemas & Automat Seville Spain

ISBN: (纸本)9783907144008

Controlling food quality and reducing waste is one of the most challenging tasks in the food industry, as it is facing high rates of wastage, leading to negative environmental impact. This research focuses on improving the scheduling and control of the supply chain of Irish lamb meat using real-time quality and temperature information. Temperature controlled reefers and sensor technologies can be used to monitor and set the temperature during transport and storage. In order to minimize waste, while at the same time optimizing cooling and transport costs, a mathematical model is proposed. The model consists of two aspects. The quality aspect considers the shelf life of Irish lamb which is related to temperature. The logistic aspect considers supply chain scheduling. With this model, a strategy is proposed to determine the movements of meat, as well as the temperature setting of cooling equipment. Results of simulation experiments indicate a sustainable approach can reduce or even eliminate waste and decrease operational costs when real-time monitoring and control is used.

关键词： Fresh food logistics meat supply chain shelf life cooling costs quality-aware modeling mixed-integer linear programming

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Adaptive Two-stage Stochastic programming with an Analysis on Capacity Expansion Planning Problem

arXiv

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arXiv 2019年

作者： Basciftci, Beste Ahmed, Shabbir Gebraeel, Nagi Department of Business Analytics Tippie College of Business University of Iowa Iowa CityIA52242 United States H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology AtlantaGA30332 United States

Problem Definition: Multi-stage stochastic programming is a well-established framework for sequential decision making under uncertainty by seeking policies that can be dynamically adjusted as uncertainty is realized. Often, e.g., due to contractual constraints, such flexible policies are not desirable, and the decision maker may need to commit to a set of actions for a certain number of periods. Two-stage stochastic programming might be better suited to such settings, where first-stage decisions do not adapt to the uncertainty realized. In this paper, we propose a novel alternative approach, named as adaptive two-stage stochastic programming, where each component of the decision policy requiring limited flexibility has its own revision point, a period prior to which the decisions are determined at the beginning of the planning until this revision point, and after which they are revised for adjusting to the uncertainty realized thus far until the end of the planning. We then analyze this approach over the capacity expansion planning problem, that may require limited flexibility over expansion decisions. Methodology/results: We provide a generic mixed-integer programming formulation for the adaptive two-stage stochastic programming problem with finite support, in particular for scenario trees, and show that this problem is NP-hard in general. Next, we focus on the capacity expansion planning problem, and derive bounds on the value of adaptive two-stage programming in comparison to the two-stage and multi-stage approaches in terms of revision points. We propose several heuristic solution algorithms based on this bound analysis. These algorithms either provide approximation guarantees or computational advantages in solving the resulting adaptive two-stage stochastic problem. Managerial implications: We provide insights on the choice of the revision times based on our analytical analysis. We further present an extensive computational study on a generation capacity expans

关键词： mixed-integer linear programming

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Emergency Medical System Design Using Kernel Search

Emergency Medical System Design Using Kernel Search

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IEEE Workshop on Complexity in Engineering (COMPENG)

作者： Janosikova, L'udmila Jankovic, Peter Univ Zilina Fac Management Sci & Informat Univ 1 Zilina 01026 Slovakia

ISBN: (纸本)9781538653388

The paper deals with location of the standby sites of emergency vehicles in a large-scale area. We propose a mathematical programming model that preserves the current number of vehicles and looks for better station location. The optimization criterion is to minimize the average response time. Large problem instances are intractable in a reasonable amount of time and that is why a modification of the kernel search heuristic is proposed to get an approximate solution. A computer simulation model is used to evaluate the current and the proposed system in the Slovak Republic. The model was calibrated using real data on emergency trips.

关键词： facility location multiple-source capacitated p-median problem kernel search mixed-integer linear programming agent-based computer simulation

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Applications of train routing selection methods for real-time railway traffic management 21

Applications of train routing selection methods for real-tim...

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21st IEEE International Conference on Intelligent Transportation Systems (ITSC)

作者： Sama, Marcella D'Ariano, Andrea Pacciarelli, Dario Pellegrini, Paola Rodriguez, Joaquin Roma Tre Univ Dept Engn Rome Italy Univ Lille Nord France IFSTTAR COSYS LEOSTESTAS Villeneuve Dascq France

ISBN: (纸本)9781728103235

The real-time railway traffic management problem (rtRTMP) aims to solve time-overlapping conflicting track requests due to traffic disturbances. The size of the problem and the time required to solve it are affected by the number of routing alternatives available to each train. The real-time train routing selection problem (rtTRSP) chooses a feasible routing subset for each train to use as input for the rtRTMP. Recently, a computational analysis has been performed via Ant Colony Optimization and the RECIFE-MILP solver. This paper generalizes such analysis by considering a different rtRTMP model, objective function and solution approach. We adopt the AGLIBRARY solver, which is based an alternative graph model of the problem and minimizes the maximum consecutive delay. The aim is to develop real-time disturbance response strategies and to quantify the advantages of the selection of a subset of routings when using different solvers. We analyze how changes in the rtRTMP model are reflected in the rtTRSP and which modifications are required. The computational analysis is performed on two French infrastructures: the line around the city of Rouen and the Lille terminal station area. The analysis shows that solving the rtTRSP helps both solvers significantly, even if they are based on different models, objectives and algorithms.

关键词： Public Transport Train Rescheduling Ant Colony Optimization mixed-integer linear programming

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Operational optimization of energy systems, 25 years - established and promising use cases 16

Operational optimization of energy systems, 25 years - estab...

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16th International Symposium on District Heating and Cooling (DHC)

作者： Stange, Peter Matthees, Anja Sander, Thomas Tech Univ Dresden Inst Power Engn Chair Bldg Energy Syst & Heat Supply Helmholtzstr 14 D-01069 Dresden Germany

The operation of complex energy systems for the supply of heat and electricity leads to several questions regarding their optimal control, e.g. when to use which generator, when to load or unload energy storages or when to buy or sell energy. Usually it is a complex task to answer these questions with the aim of optimizing a specific objective and respecting all arising physical, technical and economic constraints. Since 25 years we are solving this problem for an energy provider of a medium-sized city with the aim of minimizing the operational costs. For this purpose, an own modelled mixed-integer linear optimization problem (MILP) has to be solved in association to the continuous operation of the energy system. The model includes but is not limited to several combined heat and power generators, heat accumulators, steam generators and auxiliary coolers. In this presentation we will give an outline about the wide range of given conditions that are successfully implemented for this application. Further we show our approach to generate realistic heat demand and power consumption forecasts which are both essential preconditions for obtaining reliable optimization results. In addition to the well established MILP model in this specific use case we will outline some further promising applications of mathematical optimization in the context of energy systems. This includes the more precise modelling of energy storages, the computation of the optimal design of energy systems and the consideration of different or multiple targets in optimization. Moreover, we outline the problem of uncertain boundary conditions due to the growing amount of temporally hard to predict energy production and demand. (C) 2018 The Authors. Published by Elsevier Ltd.

关键词： optimization of operation mixed-integer linear programming energy supply district heating demand forecast

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Battery Optimal Approach to Demand Charge Reduction in Behind-The-Meter Energy Management Systems

Battery Optimal Approach to Demand Charge Reduction in Behin...

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IEEE-Power-and-Energy-Society General Meeting (PESGM)

作者： Vatanparvar, Korosh Sharma, Ratnesh Univ Calif Irvine EECS Dept Irvine CA 92697 USA NEC Labs Amer Inc Energy Dept Irvine CA 92617 USA NEC Labs Amer Inc Energy Management Dept Cupertino CA 95014 USA

ISBN: (纸本)9781538677032

Large monthly demand charge of commercial and industrial entities is a major problem for their economical business. Utilizing a battery by behind-the-meter Energy Management Systems (EMS) has been seen as a solution to demand charge reduction. In state-of-the-art approaches, the EMS maintains sufficient energy for the unexpected large demands and uses the battery to meet them. However, large amount of energy stored in the battery may increase the average battery State of-Charge (SoC) and cause degradation in battery capacity. Therefore, the current approaches of demand charge reduction significantly-shortens the battery lifetime which is not economical. In this paper, we propose a novel battery optimal approach to reduce the monthly demand charges. In our approach, load profile of the previous month is used by daily optimizations to shave daily power demands while considering the battery lifetime model. Evaluated daily demand thresholds and load profile are statistically analyzed to cluster different types of day. Hence, it helps the EMS to find the typical daily load profile and appropriate monthly demand threshold for the entity. The performance of our approach has been analyzed and compared to the state-of-the-arts by experimenting on multiple real-life load profiles and battery configurations. The results show significant reduction of 16% in annual average battery SoC that increases the battery lifetime from 4.1 to 5.6 years while achieving up to 13.4% demand charge reduction.

关键词： Behind-The-Meter Energy Management Battery Demand Charge Optimization Machine Learning mixed-integer linear programming Battery Lifetime

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