Watershed rainfall forecasting using neuro-fuzzy networks with the assimilation of multi-sensor information

The complex temporal heterogeneity of rainfall coupled with mountainous physiographic context makes a great challenge in the development of accurate short-term rainfall forecasts. This study aims to explore the effectiveness of multiple rainfall sources (gauge measurement, and radar and satellite products) for assimilation-based multi-sensor precipitation estimates and make multi-step-ahead rainfall forecasts based on the assimilated precipitation. Bias correction procedures for both radar and satellite precipitation products were first built, and the radar and satellite precipitation products were generated through the Quantitative Precipitation Estimation and Segregation Using Multiple Sensors (QPESUMS) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS), respectively. Next, the synthesized assimilated precipitation was obtained by merging three precipitation sources (gauges, radars and satellites) according to their individual weighting factors optimized by nonlinear search methods. Finally, the multi-step-ahead rainfall forecasting was carried out by using the adaptive network-based fuzzy inference system (ANFIS). The Shihmen Reservoir watershed in northern Taiwan was the study area, where 641 hourly data sets of thirteen historical typhoon events were collected. Results revealed that the bias adjustments in QPESUMS and PERSIANN-CCS products did improve the accuracy of these precipitation products (in particular, 30-60% improvement rates for the QPESUMS, in terms of RMSE), and the adjusted PERSIANN-CCS and QPESUMS individually provided about 10% and 24% contribution accordingly to the assimilated precipitation. As far as rainfall forecasting is concerned, the results demonstrated that the ANFIS fed with the assimilated precipitation provided reliable and stable forecasts with the correlation coefficients higher than 0.85 and 0.72 for one- and two-hour-ahead rainfall forecasting, respectively. The obtained forecasting results are very valuable information for the flood warning in the study watershed during typhoon periods. © 2013 Elsevier B.V

Models for Flexible Supply Chain Network Design

Arguably Supply Chain Management (SCM) is one of the central problems in Operations Research and Management Science (OR/MS). Supply Chain Network Design (SCND) is one of the most crucial strategic problems in the context of SCM. SCND involves decisions on the number, location, and capacity, of production/distribution facilities of a manufacturing company and/or its suppliers operating in an uncertain environment. Specifically, in the automotive industry, manufacturing companies constantly need to examine and improve their supply chain strategies due to uncertainty in the parameters that impact the design of supply chains. The rise of the Asian markets, introduction of new technologies (hybrid and electric cars), fluctuations in exchange rates, and volatile fuel costs are a few examples of these uncertainties. Therefore, our goal in this dissertation is to investigate the need for accurate quantitative decision support methods for decision makers and to show different applications of OR/MS models in the SCND realm. In the first technical chapter of the dissertation, we proposed a framework that enables the decision makers to systematically incorporate uncertainty in their designs, plan for many plausible future scenarios, and assess the quality of service and robustness of their decisions. Further, we discuss the details of the implementation of our framework for a case study in the automotive industry. Our analysis related to the uncertainty quantification, and network's design performance illustrates the benefits of using our framework in different settings of uncertainty. Although this chapter is focused on our case study in the automotive industry, it can be generalized to the SCND problem in any industry. We have outline the shortcomings of the current literature in incorporating the correlation among design parameters of the supply chains in the second technical chapter. In this chapter, we relax the traditional assumption of knowing the distribution of the uncertain parameters. We develop a methodology based on Distributionally Robust Optimization (DRO) with marginal uncertainty sets to incorporate the correlation among uncertain parameters into the designing process. Further, we propose a delayed generation constraint algorithm to solve the NP-hard correlated model in significantly less time than that required by commercial solvers. Further, we show that the price of ignoring this correlation in the parameters increases when we have less information about the uncertain parameters and that the correlated model gives higher profit when exchange rates are high compared to the stochastic model (with the independence assumption). We extended our models in previous chapters by presenting capacity options as a mechanism to hedge against uncertainty in the input parameters. The concept of capacity options similar to financial options constitute the right, but not the obligation, to buy more commodities from suppliers with a predetermined price, if necessary. In capital-intensive industries like the automotive industry, the lost capital investment for excess capacity and the opportunity costs of underutilized capacity have been important drivers for improving flexibility in supply contracts. Our proposed mechanism for high tooling cost parts decreases the total costs of the SCND and creates flexibility within the structure of the designed SCNs. Moreover, we draw several insights from our numerical analyses and discuss the possibility of price negotiations between suppliers and manufacturers over the hedging fixed costs and variable costs. Overall, the findings from this dissertation contribute to improve the flexibility, reliability, and robustness of the SCNs for a wide-ranging set of industries.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145819/1/nsalehi_1.pd

International Conference on Continuous Optimization (ICCOPT) 2019 Conference Book

The Sixth International Conference on Continuous Optimization took place on the campus of the Technical University of Berlin, August 3-8, 2019. The ICCOPT is a flagship conference of the Mathematical Optimization Society (MOS), organized every three years. ICCOPT 2019 was hosted by the Weierstrass Institute for Applied Analysis and Stochastics (WIAS) Berlin. It included a Summer School and a Conference with a series of plenary and semi-plenary talks, organized and contributed sessions, and poster sessions. This book comprises the full conference program. It contains, in particular, the scientific program in survey style as well as with all details, and information on the social program, the venue, special meetings, and more

Inferring efficient operating rules in multireservoir water resource systems: A review

[EN] Coordinated and efficient operation of water resource systems becomes essential to deal with growing demands and uncertain resources in water-stressed regions. System analysis models and tools help address the complexities of multireservoir systems when defining operating rules. This paper reviews the state of the art in developing operating rules for multireservoir water resource systems, focusing on efficient system operation. This review focuses on how optimal operating rules can be derived and represented. Advantages and drawbacks of each approach are discussed. Major approaches to derive optimal operating rules include direct optimization of reservoir operation, embedding conditional operating rules in simulation-optimization frameworks, and inferring rules from optimization results. Suggestions on which approach to use depend on context. Parametrization-simulation-optimization or rule inference using heuristics are promising approaches. Increased forecasting capabilities will further benefit the use of model predictive control algorithms to improve system operation. This article is categorized under: Engineering Water > Water, Health, and Sanitation Engineering Water > MethodsThe study has been partially funded by the ADAPTAMED project (RTI2018-101483-B-I00) from the Ministerio de Ciencia, Innovacion Universidades (MICINN) of Spain, and by the postdoctoral program (PAID-10-18) of the Universitat Politecnica de Valencia (UPV).Macian-Sorribes, H.; Pulido-Velazquez, M. (2019). Inferring efficient operating rules in multireservoir water resource systems: A review. Wiley Interdisciplinary Reviews Water. 7(1):1-24. https://doi.org/10.1002/wat2.1400S12471Aboutalebi, M., Bozorg Haddad, O., & Loáiciga, H. A. (2015). Optimal Monthly Reservoir Operation Rules for Hydropower Generation Derived with SVR-NSGAII. Journal of Water Resources Planning and Management, 141(11), 04015029. doi:10.1061/(asce)wr.1943-5452.0000553Ahmad, A., El-Shafie, A., Razali, S. F. M., & Mohamad, Z. S. (2014). Reservoir Optimization in Water Resources: a Review. Water Resources Management, 28(11), 3391-3405. doi:10.1007/s11269-014-0700-5Ahmadi, M., Bozorg Haddad, O., & Mariño, M. A. (2013). Extraction of Flexible Multi-Objective Real-Time Reservoir Operation Rules. Water Resources Management, 28(1), 131-147. doi:10.1007/s11269-013-0476-zAndreu, J., Capilla, J., & Sanchís, E. (1996). AQUATOOL, a generalized decision-support system for water-resources planning and operational management. Journal of Hydrology, 177(3-4), 269-291. doi:10.1016/0022-1694(95)02963-xAndreu, J., & Sahuquillo, A. (1987). Efficient Aquifer Simulation in Complex Systems. Journal of Water Resources Planning and Management, 113(1), 110-129. doi:10.1061/(asce)0733-9496(1987)113:1(110)Ashbolt, S. C., Maheepala, S., & Perera, B. J. C. (2016). 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Applied Dynamic Programming. doi:10.1515/9781400874651Ben-Tal, A., El Ghaoui, L., & Nemirovski, A. (2009). Robust Optimization. doi:10.1515/9781400831050Bessler, F. T., Savic, D. A., & Walters, G. A. (2003). Water Reservoir Control with Data Mining. Journal of Water Resources Planning and Management, 129(1), 26-34. doi:10.1061/(asce)0733-9496(2003)129:1(26)Bhaskar, N. R., & Whitlatch, E. E. (1980). Derivation of monthly reservoir release policies. Water Resources Research, 16(6), 987-993. doi:10.1029/wr016i006p00987Bianucci, P., Sordo-Ward, Á., Moralo, J., & Garrote, L. (2015). Probabilistic-Multiobjective Comparison of User-Defined Operating Rules. Case Study: Hydropower Dam in Spain. Water, 7(12), 956-974. doi:10.3390/w7030956Biglarbeigi, P., Giuliani, M., & Castelletti, A. (2018). Partitioning the Impacts of Streamflow and Evaporation Uncertainty on the Operations of Multipurpose Reservoirs in Arid Regions. Journal of Water Resources Planning and Management, 144(7), 05018008. doi:10.1061/(asce)wr.1943-5452.0000945Bolouri-Yazdeli, Y., Bozorg Haddad, O., Fallah-Mehdipour, E., & Mariño, M. A. (2014). Evaluation of Real-Time Operation Rules in Reservoir Systems Operation. Water Resources Management, 28(3), 715-729. doi:10.1007/s11269-013-0510-1Borgomeo, E., Mortazavi-Naeini, M., Hall, J. W., O’Sullivan, M. J., & Watson, T. (2016). Trading-off tolerable risk with climate change adaptation costs in water supply systems. Water Resources Research, 52(2), 622-643. doi:10.1002/2015wr018164Bozorg-Haddad, O., Azarnivand, A., Hosseini-Moghari, S.-M., & Loáiciga, H. A. (2017). WASPAS Application and Evolutionary Algorithm Benchmarking in Optimal Reservoir Optimization Problems. Journal of Water Resources Planning and Management, 143(1), 04016070. doi:10.1061/(asce)wr.1943-5452.0000716Bozorg-Haddad, O., Karimirad, I., Seifollahi-Aghmiuni, S., & Loáiciga, H. A. (2015). 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Journal of Water Resources Planning and Management, 127(6), 363-368. doi:10.1061/(asce)0733-9496(2001)127:6(363)Cai, X., Vogel, R., & Ranjithan, R. (2013). Special Issue on the Role of Systems Analysis in Watershed Management. Journal of Water Resources Planning and Management, 139(5), 461-463. doi:10.1061/(asce)wr.1943-5452.0000341Cancelliere, A., Giuliano, G., Ancarani, A., & Rossi, G. (2002). Water Resources Management, 16(1), 71-88. doi:10.1023/a:1015563820136Caseri, A., Javelle, P., Ramos, M. H., & Leblois, E. (2015). Generating precipitation ensembles for flood alert and risk management. Journal of Flood Risk Management, 9(4), 402-415. doi:10.1111/jfr3.12203Castelletti, A., Galelli, S., Restelli, M., & Soncini-Sessa, R. (2010). Tree-based reinforcement learning for optimal water reservoir operation. Water Resources Research, 46(9). doi:10.1029/2009wr008898Castelletti, A., Pianosi, F., & Restelli, M. (2013). A multiobjective reinforcement learning approach to water resources systems operation: Pareto frontier approximation in a single run. Water Resources Research, 49(6), 3476-3486. doi:10.1002/wrcr.20295Castelletti, A., Pianosi, F., & Soncini-Sessa, R. (2008). Water reservoir control under economic, social and environmental constraints. Automatica, 44(6), 1595-1607. doi:10.1016/j.automatica.2008.03.003Castelletti, A., & Soncini-Sessa, R. (2007). Bayesian networks in water resource modelling and management. Environmental Modelling & Software, 22(8), 1073-1074. doi:10.1016/j.envsoft.2006.06.001Castelletti, A., & Soncini-Sessa, R. (2007). Bayesian Networks and participatory modelling in water resource management. Environmental Modelling & Software, 22(8), 1075-1088. doi:10.1016/j.envsoft.2006.06.003Celeste, A. B., & Billib, M. (2009). Evaluation of stochastic reservoir operation optimization models. Advances in Water Resources, 32(9), 1429-1443. doi:10.1016/j.advwatres.2009.06.008Celeste, A. B., Curi, W. F., & Curi, R. C. (2009). Implicit Stochastic Optimization for deriving reservoir operating rules in semiarid Brazil. Pesquisa Operacional, 29(1), 223-234. doi:10.1590/s0101-74382009000100011Chandramouli, V., & Raman, H. (2001). Multireservoir Modeling with Dynamic Programming and Neural Networks. Journal of Water Resources Planning and Management, 127(2), 89-98. doi:10.1061/(asce)0733-9496(2001)127:2(89)Chang, L.-C., & Chang, F.-J. (2001). Intelligent control for modelling of real-time reservoir operation. Hydrological Processes, 15(9), 1621-1634. doi:10.1002/hyp.226Chazarra, M., García-González, J., Pérez-Díaz, J. I., & Arteseros, M. (2016). Stochastic optimization model for the weekly scheduling of a hydropower system in day-ahead and secondary regulation reserve markets. Electric Power Systems Research, 130, 67-77. doi:10.1016/j.epsr.2015.08.014Chen, D., Leon, A. S., Fuentes, C., Gibson, N. L., & Qin, H. (2018). Incorporating Filters in Random Search Algorithms for the Hourly Operation of a Multireservoir System. Journal of Water Resources Planning and Management, 144(2), 04017088. doi:10.1061/(asce)wr.1943-5452.0000876Coerver, H. M., Rutten, M. M., & van de Giesen, N. C. (2018). Deduction of reservoir operating rules for application in global hydrological models. Hydrology and Earth System Sciences, 22(1), 831-851. doi:10.5194/hess-22-831-2018Côté, P., & Leconte, R. (2016). Comparison of Stochastic Optimization Algorithms for Hydropower Reservoir Operation with Ensemble Streamflow Prediction. Journal of Water Resources Planning and Management, 142(2), 04015046. doi:10.1061/(asce)wr.1943-5452.0000575Cui, L., & Kuczera, G. (2005). Optimizing water supply headworks operating rules under stochastic inputs: Assessment of genetic algorithm performance. Water Resources Research, 41(5). doi:10.1029/2004wr003517Culley, S., Noble, S., Yates, A., Timbs, M., Westra, S., Maier, H. R., … Castelletti, A. (2016). A bottom-up approach to identifying the maximum operational adaptive capacity of water resource systems to a changing climate. Water Resources Research, 52(9), 6751-6768. doi:10.1002/2015wr018253Cunha, M. C., & Antunes, A. (2012). Simulated annealing algorithms for water systems optimization. WIT Transactions on State of the Art in Science and Engineering, 57-73. doi:10.2495/978-1-84564-664-6/04Dariane, A. B., & Momtahen, S. (2009). Optimization of Multireservoir Systems Operation Using Modified Direct Search Genetic Algorithm. Journal of Water Resources Planning and Management, 135(3), 141-148. doi:10.1061/(asce)0733-9496(2009)135:3(141)Das, B., Singh, A., Panda, S. N., & Yasuda, H. (2015). Optimal land and water resources allocation policies for sustainable irrigated agriculture. Land Use Policy, 42, 527-537. doi:10.1016/j.landusepol.2014.09.012Davidsen, C., Liu, S., Mo, X., Rosbjerg, D., & Bauer-Gottwein, P. (2016). The cost of ending groundwater overdraft on the North China Plain. Hydrology and Earth System Sciences, 20(2), 771-785. doi:10.5194/hess-20-771-2016Ehteram, M., Karami, H., & Farzin, S. (2018). Reservoir Optimization for Energy Production Using a New Evolutionary Algorithm Based on Multi-Criteria Decision-Making Models. Water Resources Management, 32(7), 2539-2560. doi:10.1007/s11269-018-1945-1Eisel, L. M. (1972). Chance constrained reservoir model. Water Resources Research, 8(2), 339-347. doi:10.1029/wr008i002p00339European Commission(2007). Communication from the Commission to the European Parliament and the Council: Addressing the challenge of water scarcity and droughts in the European Union COM(2007) 414 final. Brussels Belgium.European Commission. (2012a). Communication from the Commission to the European Parliament the Council the European Economic and Social Committee and the Committee of the Regions: A Blueprint to Safeguard Europe's Water Resources COM(2012) 673 final. Brussels Belgium.European Commission. (2012b). Communication from the Commission to the European Parliament the Council the European Economic and Social Committee and the Committee of the Regions: Report on the Review of the European Water Scarcity and Droughts Policy COM(2012) 672 final. Brussels Belgium.Fallah-Mehdipour, E., Bozorg Haddad, O., & Mariño, M. A. (2012). Real-Time Operation of Reservoir System by Genetic Programming. Water Resources Management, 26(14), 4091-4103. doi:10.1007/s11269-012-0132-zFazlali, A., & Shourian, M. (2017). A Demand Management Based Crop and Irrigation Planning Using the Simulation-Optimization Approach. Water Resources Management, 32(1), 67-81. doi:10.1007/s11269-017-1791-6Ficchì, A., Raso, L., Dorchies, D., Pianosi, F., Malaterre, P.-O., Van Overloop, P.-J., & Jay-Allemand, M. (2016). Optimal Operation of the Multireservoir System in the Seine River Basin Using Deterministic and Ensemble Forecasts. Journal of Water Resources Planning and Management, 142(1), 05015005. doi:10.1061/(asce)wr.1943-5452.0000571Fu, Q., Li, T., Cui, S., Liu, D., & Lu, X. (2017). Agricultural Multi-Water Source Allocation Model Based on Interval Two-Stage Stochastic Robust Programming under Uncertainty. Water Resources Management, 32(4), 1261-1274. doi:10.1007/s11269-017-1868-2Galelli, S., Goedbloed, A., Schwanenberg, D., & van Overloop, P.-J. (2014). Optimal Real-Time Operation of Multipurpose Urban Reservoirs: Case Study in Singapore. Journal of Water Resources Planning and Management, 140(4), 511-523. doi:10.1061/(asce)wr.1943-5452.0000342Giuliani, M., Castelletti, A., Pianosi, F., Mason, E., & Reed, P. M. (2016). Curses, Tradeoffs, and Scalable Management: Advancing Evolutionary Multiobjective Direct Policy Search to Improve Water Reservoir Operations. Journal of Water Resources Planning and Management, 142(2), 04015050. doi:10.1061/(asce)wr.1943-5452.0000570Giuliani, M., Herman, J. D., Castelletti, A., & Reed, P. (2014). Many-objective reservoir policy identification and refinement to reduce policy inertia and myopia in water management. Water Resources Research, 50(4), 3355-3377. doi:10.1002/2013wr014700Giuliani, M., Li, Y., Castelletti, A., & Gandolfi, C. (2016). A coupled human-natural systems analysis of irrigated agriculture under changing climate. Water Resources Research, 52(9), 6928-6947. doi:10.1002/2016wr019363Giuliani, M., Quinn, J. D., Herman, J. D., Castelletti, A., & Reed, P. M. (2018). Scalable Multiobjective Control for Large-Scale Water Resources Systems Under Uncertainty. IEEE Transactions on Control Systems Technology, 26(4), 1492-1499. doi:10.1109/tcst.2017.2705162Grüne, L., & Semmler, W. (2004). Using dynamic programming with adaptive grid scheme for optimal control problems in economics. Journal of Economic Dynamics and Control, 28(12), 2427-2456. doi:10.1016/j.jedc.2003.11.002Guariso, G., Rinaldi, S., & Soncini-Sessa, R. (1986). The Management of Lake Como: A Multiobjective Analysis. Water Resources Research, 22(2), 109-120. doi:10.1029/wr022i002p00109Gundelach, J., & ReVelle, C. (1975). Linear decision rule in reservoir management and design: 5. A general algorithm. Water Resources Research, 11(2), 204-207. doi:10.1029/wr011i002p00204Guo, X., Hu, T., Zeng, X., & Li, X. (2013). Extension of Parametric Rule with the Hedging Rule for Managing Multireservoir System during Droughts. Journal of Water Resources Planning and Management, 139(2), 139-148. doi:10.1061/(asce)wr.1943-5452.0000241Haddad, O. B., Afshar, A., & Mariño, M. A. (2006). Honey-Bees Mating Optimization (HBMO) Algorithm: A New Heuristic Approach for Water Resources Optimization. Water Resources Management, 20(5), 661-680. doi:10.1007/s11269-005-9001-3Hadka, D., Herman, J., Reed, P., & Keller, K. (2015). An open source framework for many-objective robust decision making. Environmental Modelling & Software, 74, 114-129. doi:10.1016/j.envsoft.2015.07.014Haguma, D., & Leconte, R. (2018). Long-Term Planning of Water Systems in the Context of Climate Non-Stationarity with Deterministic and Stochastic Optimization. Water Resources Management, 32(5), 1725-1739. doi:10.1007/s11269-017-1900-6Haguma, D., Leconte, R., & Côté, P. (2018). Evaluating Transition Probabilities for a Stochastic Dynamic Programming Model Used in Water System Optimization. Journal of Water Resources Planning and Management, 144(2), 04017090. doi:10.1061/(asce)wr.1943-5452.0000883Houck, M. H. (1979). A Chance Constrained Optimization Model for reservoir design and operation. 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Many objective robust decision making for complex environmental systems undergoing change. Environmental Modelling & Software, 42, 55-71. doi:10.1016/j.envsoft.2012.12.007Kelman, J., Stedinger, J. R., Cooper, L. A., Hsu, E., & Yuan, S.-Q. (1990). Sampling stochastic dynamic programming applied to reservoir operation. Water Resources Research, 26(3), 447-454. doi:10.1029/wr026i003p00447Keshtkar, A. R., Salajegheh, A., Sadoddin, A., & Allan, M. G. (2013). Application of Bayesian networks for sustainability assessment in catchment modeling and management (Case study: The Hablehrood river catchment). Ecological Modelling, 268, 48-54. doi:10.1016/j.ecolmodel.2013.08.003Kim, T., Heo, J.-H., Bae, D.-H., & Kim, J.-H. (2008). Single-reservoir operating rules for a year using multiobjective genetic algorithm. Journal of Hydroinformatics, 10(2), 163-179. doi:10.2166/hydro.2008.019Koutsoyiannis, D., & Economou, A. (2003). Evaluation of the parameterization-simulation-optimization approach for the control of reservoir systems. Water Resources Research, 39(6). doi:10.1029/2003wr002148Kumar, D. N., & Reddy, M. J. (2006). Ant Colony Optimization for Multi-Purpose Reservoir Operation. Water Resources Management, 20(6), 879-898. doi:10.1007/s11269-005-9012-0Nagesh Kumar, D., & Janga Reddy, M. (2007). Multipurpose Reservoir Operation Using Particle Swarm Optimization. Journal of Water Resources Planning and Management, 133(3), 192-201. doi:10.1061/(asce)0733-9496(2007)133:3(192)Kumar, K., & Kasthurirengan, S. (2018). Generalized Linear Two-Point Hedging Rule for Water Supply Reservoir Operation. Journal of Water Resources Planning and Management, 144(9), 04018051. doi:10.1061/(asce)wr.1943-5452.0000964Kwakkel, J. H., Haasnoot, M., & Walker, W. E. (2016). Comparing Robust Decision-Making and Dynamic Adaptive Policy Pathways for model-based decision support under deep uncertainty. 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Some Comments on Linear Decision Rules and Chance Constraints. Water Resources Research, 6(2), 668-671. doi:10.1029/wr006i002p00668Loucks

Large-scale unit commitment under uncertainty: an updated literature survey

The Unit Commitment problem in energy management aims at finding the optimal production schedule of a set of generation units, while meeting various system-wide constraints. It has always been a large-scale, non-convex, difficult problem, especially in view of the fact that, due to operational requirements, it has to be solved in an unreasonably small time for its size. Recently, growing renewable energy shares have strongly increased the level of uncertainty in the system, making the (ideal) Unit Commitment model a large-scale, non-convex and uncertain (stochastic, robust, chance-constrained) program. We provide a survey of the literature on methods for the Uncertain Unit Commitment problem, in all its variants. We start with a review of the main contributions on solution methods for the deterministic versions of the problem, focussing on those based on mathematical programming techniques that are more relevant for the uncertain versions of the problem. We then present and categorize the approaches to the latter, while providing entry points to the relevant literature on optimization under uncertainty. This is an updated version of the paper "Large-scale Unit Commitment under uncertainty: a literature survey" that appeared in 4OR 13(2), 115--171 (2015); this version has over 170 more citations, most of which appeared in the last three years, proving how fast the literature on uncertain Unit Commitment evolves, and therefore the interest in this subject

A Comprehensive Survey on Rare Event Prediction

Rare event prediction involves identifying and forecasting events with a low probability using machine learning and data analysis. Due to the imbalanced data distributions, where the frequency of common events vastly outweighs that of rare events, it requires using specialized methods within each step of the machine learning pipeline, i.e., from data processing to algorithms to evaluation protocols. Predicting the occurrences of rare events is important for real-world applications, such as Industry 4.0, and is an active research area in statistical and machine learning. This paper comprehensively reviews the current approaches for rare event prediction along four dimensions: rare event data, data processing, algorithmic approaches, and evaluation approaches. Specifically, we consider 73 datasets from different modalities (i.e., numerical, image, text, and audio), four major categories of data processing, five major algorithmic groupings, and two broader evaluation approaches. This paper aims to identify gaps in the current literature and highlight the challenges of predicting rare events. It also suggests potential research directions, which can help guide practitioners and researchers.Comment: 44 page

Uncertainty evaluation of reservoir simulation models using particle swarms and hierarchical clustering

History matching production data in finite difference reservoir simulation models has been and always will be a challenge for the industry. The principal hurdles that need to be overcome are finding a match in the first place and more importantly a set of matches that can capture the uncertainty range of the simulation model and to do this in as short a time as possible since the bottleneck in this process is the length of time taken to run the model. This study looks at the implementation of Particle Swarm Optimisation (PSO) in history matching finite difference simulation models. Particle Swarms are a class of evolutionary algorithms that have shown much promise over the last decade. This method draws parallels from the social interaction of swarms of bees, flocks of birds and shoals of fish. Essentially a swarm of agents are allowed to search the solution hyperspace keeping in memory each individual’s historical best position and iteratively improving the optimisation by the emergent interaction of the swarm. An intrinsic feature of PSO is its local search capability. A sequential niching variation of the PSO has been developed viz. Flexi-PSO that enhances the exploration and exploitation of the hyperspace and is capable of finding multiple minima. This new variation has been applied to history matching synthetic reservoir simulation models to find multiple distinct history 3 matches to try to capture the uncertainty range. Hierarchical clustering is then used to post-process the history match runs to reduce the size of the ensemble carried forward for prediction. The success of the uncertainty modelling exercise is then assessed by checking whether the production profile forecasts generated by the ensemble covers the truth case

Uncertainty Quantification And Economic Dispatch Models For The Power Grid

The modern power grid is constrained by several challenges, such as increased penetration of Distributed Energy Resources (DER), rising demand for Electric Vehicle (EV) integration, and the need to schedule resources in real-time accurately. To address the above challenges, this dissertation offers solutions through data-driven forecasting models, topology-aware economic dispatch models, and efficient optional power flow calculations for large scale grids. Particularly, in chapter 2, a novel microgrid decomposition scheme is proposed to divide the large scale power grids into smaller microgrids. Here, a two-stage Nearest-Generator Girvan-Newman (NGGN) algorithm, a graphicalclustering-based approach, followed by a distributed economic dispatch model, is deployed to yield a 12.64% cost savings. In chapter 3, a deep-learning-based scheduling scheme is intended for the EVs in a household community that uses forecasted demand, consumer preferences and Time-of-use (TOU) pricing scheme to reduce electricity costs for the consumers and peak shaving for the utilities. In chapter 4, a hybrid machine learning model using GLM with other methods was designed to forecast wind generation data. Finally, in chapter 5, multiple formulations for Alternating Current Optimal Power Flow (ACOPF) were designed for large scale grids in a high-performance computing environment. The ACOPF formulations, namely, power balance polar, power balance Cartesian, and current balance Cartesian, are tested on bus systems ranging from a 9-bus to 25,000. The current balance Cartesian formulation had an average of 23% faster computational time than two other formulations on a 25,000 bus system