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Coordinated wind-thermal-energy storage offering strategy in energy and spinning reserve markets using a multi-stage model

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APPLIED ENERGY 2020年 259卷

作者： Khaloie, Hooman Abdollahi, Amir Shafie-khah, Miadreza Anvari-Moghaddam, Amjad Nojavan, Sayyad Siano, Pierluigi Catalao, Joao P. S. Shahid Bahonar Univ Kerman Dept Elect Engn Kerman Iran Irans Natl Elites Fdn Tehran Iran Univ Vaasa Sch Technol & Innovat Vaasa 65200 Finland Aalborg Univ Dept Energy Technol DK-9220 Aalborg Denmark Univ Bonab Dept Elect Engn Bonab Iran Univ Salerno Dept Management & Innovat Syst Fisciano Italy Univ Porto Fac Engn P-4200465 Porto Portugal INESC TEC P-4200465 Porto Portugal

Renewable energy resources such as wind, either individually or integrated with other resources, are widely considered in different power system studies, especially self-scheduling and offering strategy problems. In the current paper, a three-stage stochastic multi-objective offering framework based on mixed-integer programming formulation for a wind-thermal-energy storage generation company in the energy and spinning reserve markets is proposed. The commitment decisions of dispatchable energy sources, the offering curves of the generation company in the energy and spinning reserve markets, and dealing with energy deviations in the balancing market are the decisions of the proposed three-stage offering strategy problem, respectively. In the suggested methodology, the participation model of the energy storage system in the spinning reserve market extends to both charging and discharging modes. The proposed framework concurrently maximizes generation company's expected profit and minimizes the expected emission of thermal units applying lexicographic optimization and hybrid augmented-weighted is an element of-constraint method. In this regard, the uncertainties associated with imbalance prices and wind power output as well as day-ahead energy and spinning reserve market prices are modeled via a set of scenarios. Eventually, two different strategies, i.e., a preference-based approach and emission trading pattern, are utilized to select the most favored solution among Pareto optimal solutions. Numerical results reveal that taking advantage of spinning reserve market alongside with energy market will substantially increase the profitability of the generation company. Also, the results disclose that spinning reserve market is more lucrative than the energy market for the energy storage system in the offering strategy structure.

关键词： Offering strategy Electricity markets Environmental-economic Energy storage system multi-stage stochastic programming is an element of-constraint method

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multi-stage stochastic optimization of islanded utility-microgrids design after natural disasters

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OPERATIONS RESEARCH PERSPECTIVES 2022年 9卷

作者： Kizito, Rodney Liu, Zeyu Li, Xueping Sun, Kai Univ Tennessee Knoxville Dept Ind & Syst Engn Knoxville TN 37996 USA Solar Energy Technol Off SETO DOE Washington DC USA Univ Tennessee Knoxville Dept Elect Engn & Comp Sci Knoxville TN 37996 USA

Natural disasters (e.g., hurricanes) can cause widespread power outages within distribution networks and interrupted power supply to critical loads (e.g., grocery stores, hospitals, gas, fire, and police stations) that provide utility services. Microgrids are localized power grids that can incorporate solar/photovoltaic (PV) distributed generators (PV-DGs) and energy storage systems (ESSs) for stand-alone system operations independent of the main grid, known as the island mode. This study investigates a microgrid design problem using PV-DGs and ESSs when facing prolonged power outages in the main grid. We propose a multi-stage stochastic program that holistically considers the techno-economics of microgrid investment and daily operations by optimizing the reliability and resilience of the microgrid during a week-long power outage. The model is designed from a utility perspective that includes budget constraints for investment. Due to the large model size, we develop a nested L-shaped algorithm that solves the problem exactly and analyzes the microgrid's reliability across different weather scenarios in the entire decision-making horizon. Results from a case study using real-world data show that an islanded utility-scale microgrid can effectively provide uninterrupted power supply to a network of 5 and 10 critical loads, covering 100% and 97% of the demand in all possible future scenarios, with potential investments of $8 million and $15 million, respectively.

关键词： Disaster relief power Critical load restoration Microgrid reliability multi-stage stochastic programming Solar power Energy storage system

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Maximizing the Net Present Value in Project Scheduling Under Periodic Inflation

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ASIA-PACIFIC JOURNAL OF OPERATIONAL RESEARCH 2021年第4期38卷

作者： Peymankar, Mahboobe Ranjbar, Mohammad Ferdowsi Univ Mashhad Mashhad Fac Engn Dept Ind Engn Mashhad Razavi Khorasan Iran

We investigate a project scheduling problem in which cash flows are periodically variable, and we aim to maximize the net present value (NPV). For each activity, a set of cash flows is considered where each one pertains to a particular period. This setting is compatible with inflation rates that may well occur in some countries with unstable economic situations where the occurrence times of inflation are sometimes known in advance. In this case, the project can be scheduled more suitably to abate probable pitfalls. In this paper, we investigate the problem in two settings: deterministic and stochastic cash flows. In the deterministic case, in each period we consider the expected value of cash flows of each activity as a constant and develop an integer linear programming model in conjunction with a branch-and-bound algorithm to solve the problem optimally. Moreover, we develop a multi-stage stochastic programming (MSSP) model to formulate the stochastic version of the problem. Using a set of randomly generated test instances and extensive computational results, we analyze the performance of our developed solution approaches. In addition, we compare the deterministic and stochastic models and analyze the sensitivity of the most important parameters.

关键词： Project scheduling net present value branch-and-bound algorithm multi-stage stochastic programming

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A Probabilistic and Value-Based Planning Approach to Assess the Competitiveness between Gas-Fired and Renewables in Hydro-Dominated Systems: A Brazilian Case Study

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ENERGIES 2021年第21期14卷

作者： Nazare, Felipe Barroso, Luiz Bezerra, Bernardo Pontificia Univ Catolica Rio de Janeiro PUC RJ Elect Engn Dept BR-22541041 Rio De Janeiro Brazil Univ Pontificia Comillas Inst Invest Tecnol Escuela Tecn Super Ingn ICAI Madrid 28015 Spain Omega Geracao SA R Elvira Ferraz 68 BR-04552040 Sao Paulo Brazil

The main challenge with the penetration of variable renewable energy (VRE) in thermal-dominated systems has been the increase in the need for operating reserves, relying on dispatchable and flexible resources. In the case of hydro-dominated systems, the cost-effective flexibility provided by hydro-plants facilitates the penetration of VRE, but the compounded production variability of these resources challenges the integration of baseload gas-fired plants. The Brazilian power system illustrates this situation, in which the development of large associated gas fields economically depends on the operation of gas-fired plants. Given the current competitiveness of VRE, a natural question is the economic value and tradeoffs for expanding the system opting between baseload gas-fired generation and VRE in an already flexible hydropower system. This paper presents a methodology based on a multi-stage and stochastic capacity expansion model to estimate the optimal mix of baseload thermal power plants and VRE additions considering their contributions for security of supply, which includes peak, energy, and operating reserves, which are endogenously defined in a time-varying and sized in a dynamic way as well as adequacy constraints. The presented model calculates the optimal decision plan, allowing for the estimation of the economical tradeoffs between baseload gas and VRE supply considering their value for the required services to the system. This allows for a comparison between the integration costs of these technologies on the same basis, thus helping policymakers and system planners to better decide on the best way to integrate the gas resources in an electricity industry increasingly renewable. A case study based on a real industrial application is presented for the Brazilian power system.

关键词： power system expansion co-optimization of energy and reserve associated natural gas multi-stage stochastic programming electricity-gas integration

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Designing a testing kit supply network for suspected COVID-19 cases under mixed uncertainty approach

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APPLIED SOFT COMPUTING 2021年 111卷 107696页

作者： Hosseini-Motlagh, Seyyed-Mahdi Samani, Mohammad Reza Ghatreh Farokhnejad, Parnian Iran Univ Sci & Technol Sch Ind Engn Univ AveNarmak Tehran 16846 Iran

Motivated by the COVID-19 (C-19) pandemic and the challenges it poses to global health and the medical communities, this research aims to investigate the factors affecting of reduction health inequalities related to the C-19 to tackle the increasing number of outbreaks and their social consequences in such a pandemic. Hence, we design a COVID-19 testing kit supply network (C-19TKSN) to allocate various C-19 test kits to the suspected C-19 cases depending on the time between the emergence of their first symptoms and the time they are tested. In particular, this model aims to minimize the total network cost and decrease false results C-19 test by considering the fundamental characteristics of a diagnostic C-19 test (i.e., specificity and sensitivity). In the sensitivity characteristic, a gamma formula is presented to estimate the error rate of false-negative results. The nature of the C-19TKSN problem is dynamic over time due to difficult predictions and changes in the number of C-19 patients. For this reason, we consider the potential demands relating to different regions of the suspected C-19 cases for various C-19 test kits and the rate of prevalence of C-19 as stochastic parameters. Accordingly, a multi-stage stochastic programming (MSSP) method with a combined scenario tree is proposed to deal with the stochastic data in a dynamic environment. Then, a fuzzy approach is employed based on Me measure to cope with the epistemic uncertainty of input data. Eventually, the practicality and capability of the proposed model are shown in a real-life case in Iran. The results demonstrate that the performance of the MSSP model is significantly better in comparison with the two-stage stochastic programming (TSSP) model regarding the false results and the total cost of the network. (C) 2021 Elsevier B.V. All rights reserved.

关键词： COVID-19 Test kits Specificity Sensitivity multi-stage stochastic programming Me measure

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A fractional multi-stage simulation-optimization energy model for carbon emission management of urban agglomeration

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SCIENCE OF THE TOTAL ENVIRONMENT 2021年 774卷

作者： Cao, R. Huang, G. H. Chen, J. P. Li, Y. P. Beijing Normal Univ Sch Environm State Key Joint Lab Environm Simulat & Pollut Con Beijing 100875 Peoples R China Beijing Normal Univ Sch Environm Ctr Energy Environm & Ecol Res UR BNU Beijing 100875 Peoples R China Univ Regina Fac Engn & Appl Sci Regina SK S4S 0A2 Canada

Carbon emission reduction and carbon sink growth are essential to realize climate change mitigation. In this study, a fractional multi-stage simulation-optimization energy model is developed to tackle multiple uncertainties in regional energy systems and reflect system efficiency under conflicting objectives. Specially, simulation method is used for projecting energy demand and associated carbon emissions through integrating support-vector-regression, Monte Carlo simulation and stochastic impacts by regression on population, affluence, and technology tool into a general framework. Meanwhile, multiple complexities in terms of multi region, multi-stage, and conflicting objectives are addressed through optimization techniques of fractional programming and multi-stage stochastic programming. To illustrate the applicability and superiority of the developed model, it is employed to the energy system and carbon emission management in the Pearl River Delta urban agglomeration. The major findings in the research include: electricity demand would grow by 26.6% from 2020 to 2035. The rate of renewable energy generation per unit cost under economic-environmental objectives would be 18.7% higher than that under single economic objective. Carbon emissions can be reduced under scenarios of climate change mitigation and socioeconomic development pathway. Meanwhile, forest carbon sink can be an effective alternative to mitigate carbon emissions. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Energy systems planning Simulation-optimization model multi-stage stochastic programming Dual objectives Emission mitigation Carbon sink

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multi-stage stochastic multi-Region Planning Model for China's Electricity Sector Considering Future Uncertainties 5

Multi-Stage Stochastic Multi-Region Planning Model for China...

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5th Asia Conference on Power and Electrical Engineering (ACPEE)

作者： Wang, Zhicheng Wang, Xiuli Tang, Lei Qi, Shixiong Yan, Sheng Wang, Shuai Xi An Jiao Tong Univ Sch Elect Engn Xian Peoples R China State Grid Corp China Beijing Peoples R China State Grid Econ & Technol Res Inst Co LTD Beijing Peoples R China

ISBN: (纸本)9781728152813

A novel multi-stage stochastic multi-region planning model for China's electricity sector is proposed in this paper. The model integrates multi-stage stochastic programming methodology on the basis of multi-region planning model to optimize power generation structure and inter-regional transmission capacity of China in the long term considering uncertainties. And multiple power generation types and inverse peak regulation characteristic of renewable energy are considered in the model, which may be more reasonable for the long-term planning under the background of low carbonization. The planning results considering the uncertainty of load demand and electricity consumption demonstrate that the proposed model is essentially a gradual decision process and guarantees decision makers to choose the optimal planning scheme with the uncertainty revealed sequentially, which may obtain more flexible results and perform better facing future uncertainties. Through scenario analysis, the impact of load demand and electricity consumption uncertainty on the electricity sector development is also revealed. Meanwhile the developing pathway of China's electricity sector can be obtained through the planning results.

关键词： multi-stage stochastic programming multi-region model China's electricity sector power generation structure inter-regional transmission capacity gradual decision process scenario analysis developing pathway

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A stochastic optimization model for staged hospital evacuation during hurricanes

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2021年 151卷

作者： Rambha, Tarun Nozick, Linda K. Davidson, Rachel Yi, Wenqi Yang, Kun Indian Inst Sci Dept Civil Engn Bangalore Karnataka India Indian Inst Sci Ctr infrastruct Sustainable Transportat & Urba Bangalore Karnataka India Cornell Univ Sch Civil & Environm Engn Ithaca NY 14853 USA Univ Delaware Dept Civil & Environm Engn Newark DE 19716 USA Cornell Univ Syst Engn Ithaca NY 14853 USA

Hurricanes result in large scale evacuations almost every year. Of particular concern and difficulty is the decision of whether or not to evacuate hospitals in these emergencies. During an emergency, a hospital is a source of refuge, and evacuating its patients is often viewed as a last resort since it is difficult to provide quality care while transporting them. At the same time, flooding and loss of power and communications put patients and caregivers at very high risk. Most emergency response plans do not have clear guidelines for evacuating or sheltering-in-place. Hurricanes are particularly complicated because there is often considerable uncertainty surrounding their eventual trajectory and intensity. These factors have contributed to, what is in hindsight, poor decisions that have cost lives. The current paper addresses this problem by developing a stochastic optimization formulation, taking into account evolving conditions and, therefore a hopefully robust collection of future flood, wind, and roadway traffic predictions. The model determines the order in which patients should be evacuated over time based on the evolution of the storm by trading off cost and risk. A holistic case study focused on North Carolina and the evolution of Hurricane Isabel is presented by fusing data and model outputs from different sources. The results highlight the advantages of using a recourse formulation that adapts to new information and illustrates the proposed decision-support model's long-term applications.

关键词： Hurricanes Hospital evacuation Disaster management Scenario trees multi-stage stochastic programming

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Resilient procurement planning for supply chains: A case study for sourcing a critical mineral material

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RESOURCES POLICY 2021年 74卷

作者： Fattahi, Mohammad Shahrood Univ Technol Dept Ind Engn & Management Shahrood Iran

This study develops a multi-stage stochastic program to determine optimal selection of suppliers, sourcing strategy, and order allocation in a multi-period supply chain planning under operational and disruption risks. Option contract and spot market are considered as two alternatives for sourcing a critical raw material under an uncertain decision-making environment. Final products' demand and the material's market price are stochastic, and capacity of unreliable suppliers varies randomly because of possible disruptions. For uncertainty modeling, discrete scenarios are generated via a simulation approach and then, a scenario reduction technique is used to construct a suitable scenario tree. To obtain a resilient planning, fortification of suppliers and option contract are exploited as mitigation strategies. Further, the level of supply chain's risk is quantified through a risk measure and limited by risk constraints. Computational results are presented on a real-life supply chain including multiple mines as suppliers and zinc-smelting plants that produce zinc ingots and powder. Using the computational studies and sensitivity analysis, the applicability of the stochastic model, the performance of risk-measurement policies, and the importance of mitigation strategies are investigated to drive some managerial insights.

关键词： Supplier selection Risk management multi-stage stochastic programming Option contract Mineral raw material Mitigation strategies

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Sustainable planning in mining supply chains with renewable energy integration: A real-life case study

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RESOURCES POLICY 2021年 74卷

作者： Fattahi, Mohammad Mosadegh, Hadi Hasani, Aliakbar Shahrood Univ Technol Dept Ind Engn & Management Shahrood Iran Amirkabir Univ Technol Dept Ind Engn Tehran Iran

This paper focuses on sustainable planning of a mining supply chain network from mining sites to demand centers. In the network, wind and solar generators can be installed in mining sites to attain low carbon mining operations. The uncertainty related to the renewable energy resources is taken into account, and to find an effective and reliable planning strategy, we present a novel cost-efficient multi-stage stochastic program in which strategic and tactical planning decisions are integrated. In the stochastic model, beside the economic goal, the greenhouse gas emissions from transportation through the network are mitigated and the social impact of the supply chain is considered using a social life cycle assessment methodology. A real case study in Iran related to the production of zinc concentrates from mines is implemented to demonstrate the applicability of the proposed optimization model in evaluating the economic objective, the sustainability issues, and the infrastructure needs for the renewable energy integration. Furthermore, a sensitivity analysis on main parameters of the problem is performed to drive some managerial insights that help decision makers in sustainable planning of mining supply chains.

关键词： Mining supply chain Sustainable development multi-stage stochastic programming Onsite renewable generation

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