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Demand response of grid-connected microgrid based on metaheuristic optimization algorithm

ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS 2021年第1期47卷 11765-11786页

作者： Singh, Arvind R. Ding, Lei Raju, D. Koteswara Kumar, R. Seshu Raghav, L. Phani Shandong Univ Sch Elect Engn Key Lab Power Syst Intelligent Dispatch & Control Minist Educ Jinan Peoples R China Natl Inst Technol Dept Elect Engn Silchar India

Demand Response Programs (DRPs) have gained the microgrid (MG) operators phenomenal attention for mobilizing the end-users in alleviating the uncertainties associated with renewable energy sources. In this research work, the effective MG energy management system (EMS) in conjunction with price-driven DRPs is proposed to achieve synergistic coordination between the energy providers and consumers and reduce operational costs. The flexible price elasticity model is implemented instead of using the price elasticity models with a preordained constant value like most existing literature. This results in a more realistic characterization of customer responsiveness to energy price changes and promotes the DRPs under a grid-connected MG environment. With this regard, a stochastic day-ahead energy management strategy is proposed to incorporate four distinct DRPs and schedule the MG distributed energy resources. The proposed strategy is verified on a practical 3-feeder MG test system with a majority of 50% industrial load considered. The short-term scheduling period of 15 min ahead solar and wind power forecast is considered to optimize the microgrid dispatch costs accurately. The intermittent nature of renewable energy sources is addressed by employing a stochastic-based scenario generation and reduction approach. A novel and nature-inspired Black Widow Optimization (BWO) is applied to determine the optimal scheduling configuration. The effectiveness of the BWO is validated in terms of rate of convergence, computational time, and solution efficacy. Finally, the best DRP has been chosen based on its technical and economic performance indices by employing a multi-criteria decision-making approach.

关键词： Black widow optimization demand response program energy management system flexible price response model microgrid multi-criteria decision making stochastic programming

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Sizing and Siting of Distributed Cloud Energy Storage Systems for a Shipboard Power System

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2021年第3期57卷 1935-1944页

作者： Lai, Kexing Illindala, Mahesh S. Kansas State Univ Dept Elect & Comp Engn Manhattan KS 66506 USA Ohio State Univ Dept Elect & Comp Engn Columbus OH 43210 USA

The research on ship electrification is gaining renewed interest to take advantage of the numerous benefits offered by microgrids in the development of an all-electric ship (AES). A key aspect of the AES is in planning of cost effective and resilient shipboard power system (SPS). The integration of energy storage system (ESS) in SPS is found to help achieve this goal. An ESS can improve the fuel economy of diesel generators by regulating their load demand and provide emergency power promptly whenever necessary. This article proposes a planning strategy for sizing and siting of a special ESS integration configuration, distributed cloud ESS (DCESS), in the SPS. While the aim of DCESS sizing is minimizing the total cost of SPS, the objective of DCESS siting is to enhance the resilience of SPS. By stochastic programming, the optimal solution with respect to representative operating conditions and fault scenarios is obtained. The proposed DCESS configuration would facilitate easy power exchange among the electrical zones. A comparison with the conventional ESS configuration is presented and the benefit of DCESS configuration on resilience enhancement is demonstrated.

关键词： Generators Resilience Microgrids Energy storage Planning Power systems Fuels Energy storage system microgrid planning resilience shipbuilding industry stochastic programming

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Direct plastic structural design under lognormally distributed strength by chance constrained programming

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OPTIMIZATION AND ENGINEERING 2020年第1期21卷 131-157页

作者： Ngoc Trinh Tran Staat, Manfred FH Aachen Univ Appl Sci Heinrich Mussmann Str 1 D-52428 Julich Germany

We propose the so-called chance constrained programming model of stochastic programming theory to analyze limit and shakedown loads of structures under random strength with a lognormal distribution. A dual chance constrained programming algorithm is developed to calculate simultaneously both the upper and lower bounds of the plastic collapse limit and the shakedown limit. The edge-based smoothed finite element method (ES-FEM) is used with three-node linear triangular elements.

关键词： Limit analysis Shakedown analysis Primal dual programming stochastic programming Chance constrained programming Reliability

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Optimal blending strategies for coking coal using chance constraints

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JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2021年第12期72卷 2690-2703页

作者： Jeuken, Rick Forbes, Michael Kearney, Michael Univ Queensland Brisbane Qld Australia

Coking coal is essential for the production of steel, and the quality of this coal significantly contributes to the quality of the produced steel. High quality coking coal has low ash content and a range of properties including volatile matter content and predicted coke strength. The coal is improved by processing after it has been mined. This processing varies and coal from multiple sources is blended. This paper introduces an original mixed integer programming model to maximise the profit of coal blending and processing. The model is computationally efficient and can be implemented at any coal mining and processing operation. The multi-period blending model incorporates stockpiling of raw material, and explicitly captures the geological variability of coal using chance constraints. A case study is evaluated and demonstrates that explicitly modelling geological variability can reduce the risk of breaching product specifications without any revenue loss. The improvement is achievable, without additional cost, by selecting the order that coal is fed into a processing plant.

关键词： stochastic programming chance constraints mining optimisation coal blending

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Network-Constrained Joint Energy and Flexible Ramping Reserve Market Clearing of Power- and Heat-Based Energy Systems: A Two-Stage Hybrid IGDT-stochastic Framework

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IEEE SYSTEMS JOURNAL 2021年第2期15卷 1547-1556页

作者： Mirzaei, Mohammad Amin Nazari-Heris, Morteza Mohammadi-Ivatloo, Behnam Zare, Kazem Marzband, Mousa Shafie-Khah, Miadreza Anvari-Moghaddam, Amjad Catalao, Joo P. S. Univ Tabriz Fac Elect & Comp Engn Tabriz *** Iran Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Northumbria Univ Fac Engn & Environm Newcastle Upon Tyne NE1 8ST Tyne & Wear England King Abdulaziz Univ Ctr Res Excellence Renewable Energy & Power Syst Jeddah 21589 Saudi Arabia Univ Vaasa Sch Technol & Innovat Vaasa 65200 Finland Aalborg Univ Dept Energy Technol DK-9920 Aalborg Denmark Univ Porto Fac Engn P-4200465 Porto Portugal INESC TEC P-4200465 Porto Portugal

This article proposes a new two-stage hybrid stochastic-information gap-decision theory (IGDT) based on the network-constrained unit commitment framework. The model is applied for the market clearing of joint energy and flexible ramping reserve in integrated heat- and power-based energy systems. The uncertainties of load demands and wind power generation are studied using the Monte Carlo simulation method and IGDT, respectively. The proposed model considers both risk-averse and risk-seeker strategies, which enables the independent system operator to provide flexible decisions in meeting system uncertainties in real-time dispatch. Moreover, the effect of feasible operating regions of the combined heat and power (CHP) plants on energy and flexible ramping reserve market and operation cost of the system is investigated. The proposed model is implemented on a test system to verify the effectiveness of the introduced two-stage hybrid framework. The analysis of the obtained results demonstrates that the variation of heat demand is effective on power and flexible ramping reserve supplied by CHP units.

关键词： Cogeneration Uncertainty Load modeling Wind power generation Wind turbines Hybrid power systems Combined heat and power (CHP) flexible ramping reserve hybrid IGDT-stochastic information gap-decision theory (IGDT) market clearing stochastic programming

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Multiperiod transshipment location-allocation problem with flow synchronization under stochastic handling operations

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NETWORKS 2021年第1期78卷 88-104页

作者： Giusti, Riccardo Manerba, Daniele Tadei, Roberto Politecn Torino Dept Control & Comp Engn I-10129 Turin Italy Univ Brescia Dept Informat Engn Brescia Italy

The transshipment location-allocation problem consists of locating transshipment facilities (e.g., intermodal hubs) of a transportation network and allocating freight flows through them, from several origins to several destinations, to satisfy demand and supply constraints. The objective is to maximize the total net transportation utility given by the total shipping utility minus the total cost to locate the facilities. Moreover, flow synchronization at the facilities must also be ensured. Unfortunately, the flow synchronization depends on a broad set of unknown events, which could cause both unexpected reductions of the facility capacity and uncertain utility of handling operations. In this paper, we first want to evaluate how uncertainty on facility capacity and handling operations utility affects the transshipment location-allocation problem in terms of complexity, net gain, and optimal solutions. Moreover, we extend the problem from a single to a multi-period setting to have a wider view of future scenarios realizations and consequently synchronize the flows by using different facilities on different periods. We propose a two-stage stochastic programming formulation with recourse and analyze, over a ground set of instances, some well-known economic indicators to derive managerial insights on the importance of addressing uncertainty for the problem. Finally, given the computational burden of solving the deterministic equivalent problem, we propose several heuristics based on progressive hedging and test their performance.

关键词： progressive hedging stochastic programming synchromodality transshipment location– allocation problem

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Mixed Integer programming models for planning maintenance at offshore wind farms under uncertainty

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2020年第0期112卷 180-202页

作者： Schrotenboer, Albert H. Ursavas, Evrim Vis, Iris F. A. Univ Groningen Fac Econ & Business Dept Operat Groningen Netherlands

We introduce the stochastic Maintenance Fleet Transportation Problem for Offshore wind farms (SMFTPO), in which a maintenance provider determines an optimal, medium-term planning for maintaining multiple wind farms while controlling for uncertainty in the maintenance tasks and weather conditions. Since the maintenance provider is typically not the owner of a wind farm, it needs to adhere minimum service requirements that specify the required service. We consider three of such settings: (1) perform all maintenance tasks, (2) allow for a fraction of unscheduled tasks, and (3) incentivize to perform maintenance rather quickly. We provide a two-stage stochastic mixed integer programming model for the three SMFTPO settings, and solve it by means of Sample Average Approximation. In addition, we provide an overview of the, what we discovered, non-aligned modeling assumptions in the literature regarding operational decisions. By providing a series of special cases of the second-stage problem resembling the different modeling assumptions, we aim to establish a common consensus regarding the key modeling decisions to be taken in maintenance planning problems for offshore wind farms. We provide newly constructed, and publicly available, benchmark sets. We extensively compare the different SMFTPO settings and its special cases on those benchmark sets, and we show that the special case reformulations are very effective for solving the second-stage problems. In addition, we find that for particular cases, established modeling techniques result in overestimations and increased running times.

关键词： Mixed Integer programming stochastic programming Offshore wind Transportation Service logistics Maintenance

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An innovative waste management system in a smart city under stochastic optimization using vehicle routing problem

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SOFT COMPUTING 2021年第8期25卷 6707-6727页

作者： Akbarpour, Navid Salehi-Amiri, Amirhossein Hajiaghaei-Keshteli, Mostafa Oliva, Diego Tecnol Monterrey Escuela Ingn & Ciencias Puebla Mexico Univ Quebec Ecole Technol Super ETS Dept Syst Engn Montreal PQ Canada Univ Oberta Catalunya Dept Comp Sci IN3 Castelldefels Spain Univ Guadalajara CUCEI Dept Ciencias Computac Guadalajara Jalisco Mexico

A smart city (SC) is a sustainable and efficient urban center that provides a high quality of life to its inhabitants through optimal management of its resources that nowadays have been wider and wider. In modern societies, municipal solid waste management (MSWM) is an important part of SCs, the main problem of MSWM is the cost that it generates and must be reduced. To solve this situation in this paper are considered two sub-models. The first sub-model uses vehicle routing problem (VRP) for routing fleet among generation waste to separation facilities. The second sub-model is designed to allocate resources from separation facilities to set of recovery plants or landfill centers. From the best of our knowledge, most of the past studies related to this topic have focused only on deterministic implementations. Also, recent studies usually focus on uncertain parameters in the area of waste generation. In addition, a few related studies have developed the uncertain parameter which has focused on facilitating separation. This study considers the uncertain parameters in the output rate of separation facilities as well as the importance of value recovery from each bin;the aim is to enhance the efficiency of operations. The purpose of this study is to minimize the total transportation cost and to maximize recycled revenue. Chance-constrained programming has been used to deal with stochastic optimization model. Four metaheuristic algorithms are employed to identify the best solution. Besides, the performance of the proposed algorithms is evaluated. Finally, sensitivity analyses along with number of scenarios have developed to measure the tightness of the proposed problem. The results of the study illustrate the optimized number of vehicles that can help the managers and decision-makers in various tightness conditions.

关键词： Municipal solid waste management Waste collection Smart city Vehicle routing problem stochastic programming

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Sustainable closed-loop supply chain network under uncertainty: a response to the COVID-19 pandemic

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2021年 1-17页

作者： Rafigh, Parisa Akbari, Ali Akbar Bidhandi, Hadi Mohammadi Kashan, Ali Husseinzadeh Islamic Azad Univ South Tehran Branch Dept Ind Engn Tehran Iran Tarbiat Modares Univ Fac Ind & Syst Engn Tehran Iran

This study proposes a sustainable closed-loop supply chain under uncertainty to create a response to the COVID-19 pandemic. In this paper, a novel stochastic optimization model integrating strategic and tactical decision-making is presented for the sustainable closed-loop supply chain network design problem. This paper for the first time implements the concept of sustainable closed-loop supply chain for the application of ventilators using a stochastic optimization model. To make the problem more realistic, most of the parameters are considered to be uncertain along with the normal probability distribution. Since the proposed model is more complex than majority of previous studies, a hybrid whale optimization algorithm as an enhanced metaheuristic is proposed to solve the proposed model. The efficiency of the proposed model is tested in an Iranian medical ventilator production and distribution network in the case of the COVID-19 pandemic. The results confirm the performance of the proposed algorithm in comparison with two other similar algorithms based on different multi-objective criteria. To show the impact of sustainability dimensions and COVID-19 pandemic for our proposed model, some sensitivity analyses are done. Generally, the findings confirm the performance of the proposed sustainable closed-loop supply chain for the pandemic cases like COVID-19.

关键词： Sustainable supply chain stochastic programming Multi-objective optimization Whale optimization algorithm COVID-19 pandemic

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Hybrid strategies using linear and piecewise-linear decision rules for multistage adaptive linear optimization

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2021年第3期290卷 1014-1030页

作者： Rahal, Said Papageorgiou, Dimitri J. Li, Zukui Univ Alberta Dept Chem & Mat Engn 9211-116 St Edmonton AB T6G 1H9 Canada ExxonMobil Res & Engn Co Corp Strateg Res 1545 Route 22 East Annandale NJ 08801 USA

Decision rules offer a rich and tractable framework for solving certain classes of multistage adaptive optimization problems. Recent literature has shown the promise of using linear and nonlinear decision rules in which wait-and-see decisions are represented as functions, whose parameters are decision variables to be optimized, of the underlying uncertain parameters. Despite this growing success, solving real-world stochastic optimization problems can become computationally prohibitive when using nonlinear decision rules, and in some cases, linear ones. Consequently, decision rules that offer a competitive trade-off between solution quality and computational time become more attractive. Whereas the extant research has always used homogeneous (i.e., either linear or piecewise-linear) decision rules, the major contribution of this paper is a computational exploration of hybrid decision rules combining the benefits of the two classes of decision rules. We first verify empirically that having higher uncertainty resolution or more linear pieces in early stages is more significant than having it in late stages in terms of solution quality. Then, we compare non-increasing and non-decreasing (i.e., higher uncertainty resolution in early and late stages, respectively) hybrid decision rules in a computational study to illustrate the trade-off between solution quality and computational cost. We also demonstrate a case where, unexpectedly, a linear decision rule is superior to a more complex piecewise-linear decision rule within a simulator. This observation bolsters the need to assess the quality of decision rules obtained from a look-ahead model within a simulator rather than just using the optimal look-ahead objective function value. (C) 2020 Elsevier B.V. All rights reserved.

关键词： stochastic programming Decision rule Hybrid decision rule Lifting strategy

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