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Multi-Timescale Model Predictive Control of Battery Energy Storage System Using Conic Relaxation in Smart Distribution Grids

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IEEE TRANSACTIONS ON POWER SYSTEMS 2018年第6期33卷 7152-7161页

作者： Zafar, Raheel Ravishankar, Jayashri Fletcher, John E. Pota, Hemanshu R. Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia Univ New South Wales Sch Engn & Informat Technol Canberra ACT 2600 Australia

This paper proposes a multi-timescale volt/var optimization for the optimal dispatch of battery energy storage system in smart distribution grids. It aims to coordinate the substation on-load tap changer operation on slow-timescale (hourly basis) with the photovoltaic inverters and battery storage operations on fast-timescale (15 min basis). This coordination is achieved by using two-stage stochastic programming and implemented via model predictive control. The power loss and energy purchase cost are reduced while maintaining voltages within limits. The forecasting uncertainties are modeled by generating a large number of random scenarios and then subsequently reducing scenario numbers to establish a tradeoff between computational burden and accuracy of the solution. The mixed-integer second-order cone program (MISOCP) is formulated with reduced scenarios, which achieves global optimum. Simulation results demonstrate the effectiveness of proposed MISOCP model in keeping the voltages within limits under forecasting uncertainties.

关键词： Battery storage system mixed-integer second-order cone program model predictive control smart distribution grids two-stage stochastic programming volt/VAR optimization

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Algal biofuel supply chain network design with variable demand under alternative fuel price uncertainty: A case study

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COMPUTERS & CHEMICAL ENGINEERING 2019年 130卷 106528-000页

作者： Arabi, Mahsa Yaghoubi, Saeed Tajik, Javad Iran Univ Sci & Technol Sch Ind Engn Tehran *** Iran

According to the high sugar and oil content of algae, the algal biofuel have been taken into consideration in recent years. This paper develops a multi-objective mixed integer quadratic programming (MMIQP) model to optimize the design and planning of an algae-based biofuel supply chain network under environmental and economic objectives. The goals of this paper are set to maximize the total profit and carbon dioxide absorption of the supply chain. The proposed mathematical model considers the deterioration issue in the inventory and during the transportation, in a multi-period time horizon. Since the alternative fuel price is not certain in the real world, a two-stage stochastic programming is utilized to address this uncertainty. Moreover, data envelopment analysis (DEA) approach is proposed to rank the candidate cities based on efficiency for microalgal harvesting. The applicability and efficiency of the proposed model are finally demonstrated through a real case study in Iran. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Algal biofuel supply chain Multi-objective optimization Deterioration Uncertainty two-stage stochastic programming Data envelopment analysis (DEA) approach

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Optimal System Design and Sequential Preventive Maintenance Under Uncertain Aperiodic-Changing Stresses

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IEEE TRANSACTIONS ON RELIABILITY 2018年第3期67卷 907-919页

作者： Zhu, Xiaoyan Bei, Xiaoqiang Chatwattanasiri, Nida Coit, David W. Univ Chinese Acad Sci Sch Econ & Management Beijing 100049 Peoples R China Natl Elect & Comp Technol Ctr Pathum Thani 12120 Thailand Rutgers State Univ Dept Ind & Syst Engn Piscataway NJ 08901 USA

This paper presents a two-stage stochastic programming model with recourse for integrating the design and sequential preventive maintenance schedule of a system, which is subject to uncertain aperiodic-changing future usage stresses. Specifically, the usage stresses change as the system operates and preventive maintenance is conducted. The system undergoes imperfect repair according to the sequential preventive maintenance policy and minimal repair in response to emergency failures. The system is replaced when the maintenance is uneconomical due to the deterioration of the components. Under such future usage stresses and maintenance, this paper formulates the failure rates and lifetime distributions of components and the system, the failure rates increase with the usage stresses, and both the failure rates and usage stresses have an instantaneous incremental decrease at each preventive maintenance action. In the two-stage stochastic optimization model, the first-stage decision variables are the numbers of components to be used in the subsystems, and these variables affect the second-stage variables, which define the number of imperfect preventive maintenance actions before the replacement of the system and the aperiodic preventive maintenance time intervals for various future usage scenarios. Analytical properties about the failure rates of components and subsystems and the solution for minimizing expected system maintenance cost rate are derived. A decomposition method for solving the proposed two-stage stochastic model is designed based on the analytical results. Numerical examples and sensitivity analysis are provided for deep understanding of the proposed method.

关键词： Imperfect repair redundancy allocation sequential preventive maintenance two-stage stochastic programming uncertain aperiodic-changing stresses.

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A two-stage stochastic optimization model for port cold storage capacity allocation considering pelagic fishery yield uncertainties

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ENGINEERING OPTIMIZATION 2018年第11期50卷 1926-1940页

作者： Ma, Qianli Wang, Wenyuan Peng, Yun Song, Xiangqun Dalian Univ Technol State Key Lab Coastal & Offshore Engn Fac Infrastruct Engn Dalian Peoples R China

In the distant-water fishing industry, the catching of fish is extremely unpredictable, which leads to great uncertainties regarding timely amounts of fish in port cold storage. Therefore, research dedicated to capacity optimization of port cold storage is conducted under the scenario of distant-water fishery yield uncertainties. A two-stage stochastic programming model is established to optimize the cold storage capacity allocation considering pelagic fishery yield uncertainties. The first-stage decisions of the proposed model are related to the determination of port cold storage capacity, and the second-stage decisions are related to the daily amount of total catch in cold storage. First-stage decisions are made using the expected value of the penalty function, the objective of which is to minimize the cost due to the unsatisfied constraint condition. A case study in northern China is then presented to investigate the specific influence of uncertainties on the port cold storage capacity allocation.

关键词： Capacity allocation optimization uncertainty port cold storage pelagic fishery two-stage stochastic programming

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Shelter Location and Evacuation Route Assignment Under Uncertainty: A Benders Decomposition Approach

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TRANSPORTATION SCIENCE 2018年第2期52卷 416-436页

作者： Bayram, Vedat Yaman, Hande TED Univ Dept Ind Engn TR-06420 Ankara Turkey Bilkent Univ Dept Ind Engn TR-06800 Bilkent Turkey

Shelters are safe facilities that protect a population from possible damaging effects of a disaster. For that reason, shelter location and traffic assignment decisions should be considered simultaneously for an efficient evacuation plan. In addition, as it is very difficult to anticipate the exact place, time, and scale of a disaster, one needs to take into account the uncertainty in evacuation demand, the disruption/degradation of evacuation road network structure, and the disruption in shelters. In this study, we propose an exact algorithm based on Benders decomposition to solve a scenario-based two-stage stochastic evacuation planning model that optimally locates shelters and that assigns evacuees to shelters and routes in an efficient and fair way to minimize the expected total evacuation time. The second stage of the model is a second-order cone programming problem, and we use duality results for second-order cone programming in a Benders decomposition setting. We solve practical-size problems with up to 1,000 scenarios in moderate CPU times. We investigate methods such as employing a multicut strategy, deriving Pareto-optimal cuts, and using a preemptive priority multiobjective program to enhance the proposed algorithm. We also use a cutting plane algorithm to solve the dual subproblem since it contains a constraint for each possible path. Computational results confirm the efficiency of our algorithms.

关键词： disaster management shelter location two-stage stochastic programming second-order cone programming cutting plane algorithm evacuation traffic management constrained system optimal Benders decomposition

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A stochastic programming approach for the traveling purchaser problem

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IMA JOURNAL OF MANAGEMENT MATHEMATICS 2017年第1期28卷 41-63页

作者： Beraldi, Patrizia Bruni, Maria Elena Manerba, Daniele Mansini, Renata Univ Calabria Dept Mech Energy & Management Engn Arcavacata Di Rende Italy Univ Brescia Dept Informat Engn Brescia Italy

The deterministic traveling purchaser problem (TPP) aims to select a subset of suppliers, offering products at different prices and quantities, to satisfy demand while minimizing travelling and purchasing costs. In this paper, we study a variant of the TPP in which both the available quantities and the purchasing prices are uncertain. This more challenging version of the problem, TPP under uncertainty, allows a purchaser to protect himself against risks of insufficient demand fulfilment and to exploit the benefits of procurement at lower prices. We introduce a two-stage stochastic programming formulation of the problem and we present a tailored solution approach based on a Branch-and-Cut method and on a heuristic approach to find good initial solutions. Extensive computational experiments show the efficiency of the proposed approach in finding the optimal solution of the deterministic equivalent problem for instances with up to 75 suppliers, 50 products and 200 scenarios in less than 2 h.

关键词： traveling purchaser problem two-stage stochastic programming Branch-and-Cut beam search

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The multi-sourcing location inventory problem with stochastic demand

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2018年第1期266卷 72-87页

作者： Amiri-Aref, Mehdi Klibi, Walid Babai, M. Zied KEDGE Business Sch Ctr Excellence Supply Chain CESIT Talence France Interuniv Res Ctr Enterprise Networks Logist & Tr Montreal PQ Canada

This paper deals with a multi-period location-inventory optimization problem in a multi-echelon supply chain network characterized by an uncertain demand and a multi-sourcing feature. The aim of the paper is to propose a generic modeling approach to integrate key features of the inventory planning decisions, made under a reorder point order-up-to-level (s, S) policy, with the location-allocation design decisions to cope with demand uncertainty. Given the hierarchical structure of the problem, a two-stage stochastic mathematical model that maximizes the total expected supply chain network profit is proposed. This optimization model is intractable due to its non-linearity. Therefore, a linear approximation is proposed and a sample average approximation approach is used to produce near-optimal solutions. Numerical experiments are conducted to validate the proposed modeling and solution approaches. The results show the efficiency of the linear approximation of the (s, S) policy at the strategic level to produce robust design solutions under uncertainty. They underline the sensitivity of the design solution to the demand type and the impact of the inventory holding costs and backorder costs, especially under non-stationary processes. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Supply chain management Network design Location-inventory two-stage stochastic programming

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A Deep Peak Regulation Auxiliary Service Bidding Strategy for CHP Units Based on a Risk-Averse Model and District Heating Network Energy Storage

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ENERGIES 2019年第17期12卷 3314页

作者： Tian, Liang Xie, Yunlei Hu, Bo Liu, Xinping Deng, Tuoyu Luo, Huanhuan Li, Fengqiang North China Elect Power Univ Dept Automat Baoding 071003 Peoples R China State Grid Liaoning Elect Power Co Ltd Shenyang 110006 Liaoning Peoples R China

With the advance of China's power system reform, combined heat and power (CHP) units can participate in multi-energy market. In order to maximize CHP profit in a multi-energy market, a bidding strategy for deep peak regulation auxiliary service of a CHP based on a two-stage stochastic programming risk-averse model and district heating network (DHN) energy storage was proposed. The quotation set of competitors and load uncertainty was modeled with a Latin hypercube sampling (LHS) method. A dynamic queuing method was used to clear the market for the deep peak regulation auxiliary service to determine the bidding capacities of CHPs in the electricity market and the deep peak regulation auxiliary service market, respectively. Finally, the conditional value-at-risk (CVaR) indicator is used to measure the risk brought by the system uncertainty to the CHP, and the quotation coefficient is determined after considering the expected profit and risk profit comprehensively. The results of the example show that the profits produced by simultaneous participation in both electricity market and the deep peak regulation auxiliary service market are increased by approximately 9.5% compared with the profits produced by only participation in a single market. In addition, the use of DHN energy storage led to a profit increase of approximately 4.6%. As the risk aversion coefficient increases, the expected profit will be further reduced.

关键词： deep peak regulation auxiliary service conditional value-at-risk two-stage stochastic programming power and heat decoupling energy storage

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A novel stochastic energy management of a microgrid with various types of distributed energy resources in presence of demand response programs

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ENERGY 2018年 160卷 257-274页

作者： Farsangi, Alireza SoltaniNejad Hadayeghparast, Shahrzad Mehdinejad, Mehdi Shayanfar, Heidarali Iran Univ Sci & Technol Sch Elect Engn Ctr Excellence Power Syst Automat & Operat Tehran Iran

In this paper, the energy management of a microgrid including wind turbine, PhotoVoltaic (PV) modules, Combined Heat and Power (CHP) systems, fuel cells, power only units, heat only unit, Plug-in Electric Vehicles (PEVs), and thermal energy storage resources for supplying electrical and thermal loads is presented. For achieving a better management on demand side, both price-based and incentive-based Demand Response Programs (DRPs) have been used and their impacts on reducing the operational cost of microgrid in both grid-connected and island modes have been investigated. Also, the uncertainty of price, load, wind speed and solar radiation are taken into account in order to obtain more realistic results. By discretization of Probability Distribution Function (PDF) of each uncertain parameter, a set of scenarios is generated. Then, using a scenario reduction method based on mixed-integer linear optimization, the set of reduced scenarios is obtained. two-stage stochastic programming approach is used to minimize the operational cost in microgrid energy management. The proposed method for microgrid energy management has been evaluated in three modes: grid-connected, grid-connected with DRPs, and island mode with DRPs. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Microgrid energy management Distributed energy resource (DER) Demand response programs (DRPs) Uncertainty two-stage stochastic programming Plug-in electric vehicles (PEVs)

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Integrated blood supply chain planning for disaster relief

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INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION 2018年 27卷 168-188页

作者： Samani, Mohammad Reza Ghatreh Torabi, S. Ali Hosseini-Motlagh, Seyyed-Mandi Iran Univ Sci & Technol Sch Ind Engn Univ Ave Tehran 16846 Iran Univ Tehran Sch Ind Engn Coll Engn Tehran Iran

This paper proposes a multi-objective mixed integer linear programming model for the design of an integrated blood supply chain network for disaster relief. The developed model accounts for all the special aspects of blood supply chains involving uncertain demand of blood products and their irregular supply, perishability of blood products and shortage avoidance. It also provides a trade-off analysis between the cost efficiency (via minimizing the total costs), responsiveness (through minimizing the maximum unsatisfied demand) and effectiveness of the designed network (by minimizing the time span between blood production in regional blood centers and consumption in demand zones so that their freshness is preserved). A hybrid framework based on the two-stage stochastic programming and possibilistic programming approaches is devised to deal with a mixture of random and epistemic uncertainties. Some numerical experiments are conducted to validate the proposed model and its solution approach. Also, a real case study is presented to demonstrate the practicality of the proposed model. Helpful managerial insights are also provided through conducting a number of sensitivity analyses.

关键词： Blood supply chain Disaster relief Multi-objective programming two-stage stochastic programming Possibilistic programming

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