Set Of Representative Scenarios Research Articles

Tri-Level Multi-Energy System Planning Method for Zero Energy Buildings Considering Long- and Short-Term Uncertainties

To effectively supply the multi-energy loads and achieve the annual zero energy targets of zero energy buildings (ZEBs) throughout the planning horizon, this paper proposes a tri-level multi-energy system planning method for ZEBs considering both long- and short-term uncertainties. In the upper level, the optimal goal is to obtain an optimal device sizing scheme within the electric-thermal-hydrogen integrated multi-energy system (EHT-MES). The middle and lower levels tackle the long-term temperature change and short-term source-load uncertainties separately. For the former, a set of representative scenarios that include typical and extreme weather conditions are generated from the future temperature forecast dataset, and an ambiguous set is utilized to model the uncertain probability distributions of the scenario set. For the latter, to guarantee the reliable and economic operation of ZEBs under different seasonal-daily patterns, a hybrid stochastic and robust optimization (HSRO) method is applied to deal with short-term uncertainties from solar radiation, wind output, outdoor temperature, electric loads, and hot water loads. A reformulation method is proposed to transform the multi-level coupling planning model into an equivalent and tractable form, and an improved column-and-constraint generation (C&CG) algorithm is developed to solve the recast model. Simulation results verify the effectiveness of the proposed planning method in deploying ZEBs’ multi-energy devices and immunizing against multiple timescale uncertainties.

A stochastic programming approach to the cutting stock problem with usable leftovers

In cutting processes, one of the strategies to reduce raw material waste is to generate leftovers that are large enough to return to stock for future use. The length of these leftovers is important since waste is expected to be minimal when cutting these objects in the future. However, in several situations, future demand is unknown and evaluating the best length for the leftovers is challenging. Furthermore, it may not be economically feasible to manage a stock of leftovers with multiple lengths that may not result in minimal waste when cut. In this paper, we approached the cutting stock problem with the possibility of generating leftovers as a two-stage stochastic program with recourse. We approximated the demand levels for the different items by employing a finite set of scenarios. Also, we modeled different decisions made before and after uncertainties were revealed. We proposed a mathematical model to represent this problem and developed a column generation approach to solve it. We ran computational experiments with randomly generated instances, considering a representative set of scenarios with a varying probability distribution. The results validated the efficiency of the proposed approach and allowed us to derive insights on the value of modeling and tackling uncertainty in this problem. Overall, the results showed that the cutting stock problem with usable leftovers benefits from a modeling approach based on sequential decision-making points and from explicitly considering uncertainty in the model and the solution method.

To exploit the flexibility of TSO–DSO​ interaction: A coordinated transmission robust planning and distribution stochastic reinforcement solution

With the penetration of distributed energy resources into distribution systems, the power exchange between distribution and transmission systems becomes more coupled and flexible. The technical and economy performance of the expansion approach could be improved with the consideration of TSO–DSO interconnection and interaction. This paper presents a hierarchical planning framework for distribution stochastic and transmission robust optimization in a coordinated manner. Source-network-load uncertainties in transmission network are formulated using uncertain set while source-demand uncertainties in distribution networks are described with a set of representative scenarios. An integrated solution procedure, including column and constraint generation and iteration algorithm, is proposed to determine the transmission robust and distribution stochastic expansion schemes. Simulation carried out on a transmission network with three distribution networks validates the high performance of the formulated problem compared with the existing isolated expansion approach.

Multidimensional Scenario Selection for Power Systems With Stochastic Failures

Stochastic optimization can be used to model predictable but uncertain element failures, in an attempt to enhance system reliability in power system operation and planning. In practical applications, such as preventive operation during severe weather, the uncertainty set is often very large. This will lead to two challenges: (i) every possible scenario cannot be practically identified; and (ii) the computational demands of stochastic optimization with a large scenario set cannot be met. To address these challenges, this paper develops a multidimensional scenario selection method, which creates a rather small but representative set of scenarios. The developed method makes use of failure features as well as network features of the elements that may fail, to achieve a superior performance. The simulations studies on a synthetic large-scale Texas system, show the dominant performance of the method compared to existing algorithms in the literature, as well as common industry practices. Due to its effectiveness, the method presented in this paper enables computationally efficient implementation of stochastic power system operation and planning software tools. Such stochastic tools will improve system reliability and efficiency through enhanced use of the existing resources, without requiring any expensive system upgrade.

A stochastic distribution system planning method considering regulation services and energy storage degradation

With the trend of energy storage participating in ancillary service markets, it is still computationally burdensome to incorporate the rapidly changing real-time signals in the long-run distribution system planning. In this paper, a two-stage stochastic programming is proposed for the distribution system with energy storage, where the storage degradation and ancillary service revenue for frequency regulation are both considered. For this purpose, the problem is formulated as a mixed-integer linear programming optimizing the overall planning cost, including investment and maintenance cost, power transaction cost and revenue from regulation services. A degradation penalty is added in the objective to avoid excessive charge/discharge when providing regulation services, thus further benefiting the economy of the distribution system. The model also considers uncertainties of load demand and electricity prices. A Gaussian mixture model is adopted to characterize these uncertainties and a set of representative scenarios are sampled. To accelerate the optimization, a modified progressive hedging with parallel computing is proposed. It is demonstrated through a 33-bus distribution system that the proposed algorithm has a speed approximately 15 times as fast as the state-of-art commercial software Gurobi when solving the model in 100 scenarios. For this case study, considering degradation penalty has been shown to extend energy storage lifespan by one year.

Structural performance of deteriorating reinforced concrete columns under multiple earthquake events

This study investigates deteriorating reinforced concrete (RC) columns subjected to multiple earthquake events, taking into consideration how a combination of environmental stressors and extreme loads can adversely affect the performance of RC columns during their expected service life. For this purpose, nonlinear finite-element models are developed with all the necessary details, validated with a set of experimental test results, and then employed to predict the extent of structural degradation as a function of age and prior earthquake-induced damage. This is through a systematic approach to capture the consequences of corrosion-induced deterioration, in terms of rebar cross section loss, drop of steel yield and ultimate strength, and degradation of bond. The consequences of earthquake events are evaluated with not only structural damage caused by lateral loads, but also the corrosion process expedited as a result of residual damage. Such a holistic assessment strategy paves the way to obtain realistic predictions for the performance of RC columns under future earthquake events. To study this critical aspect, a set of representative scenarios that consist of two earthquake events of different intensities are defined. Considering that RC columns in service commonly experience more than one earthquake during their lifetime, the outcome of this study contributes to transforming the accuracy of predictions made on the residual capacity and seismic response of structures subjected to multiple stressors and hazards.

Source reputation assessment in an IoT-based vehicular traffic monitoring system

Vehicular traffic safety relies, to a great extent, on traffic control and traffic offence detection. While this is currently in the domain of governmental and road maintenance services, many alternatives, such as crowdsensing-based approaches have emerged. We present a traffic event reporting scheme, focusing on event detection and data source reputation mechanisms. Proposed scheme is evaluated on a set of representative scenarios. Proposal is critically discussed and is followed by guidelines for future work.

Parameterized Dynamic Routing of a Fleet of Cybercars**This work was supported by the China Scholarship Council under Grant 201207090001.

Abstract: Due to the nonlinearity of the dynamics of vehicles and the discrete nature of the route decision variables, the dynamic routing problem for a large number of vehicles is computationally very hard to solve. In this paper, two efficient parameterized control methods are proposed for the dynamic routing of a fleet of cybercars in a road network only open to cybercars. With the proposed parameterized control methods, the updates of the routes of cybercars are parameterized and then optimized over the parameters with respect to the overall performance of the cybercar system for a representative set of scenarios. After tuning the parameters, the proposed parameterized control methods are implemented online with fixed parameters. Moreover, the two proposed parameterized control methods are well-structured and scalable, and therefore can be applied to road networks with arbitrary topologies. The effectiveness of the proposed parameterized control methods is shown in a numerical simulation study.

Модель экономики России как инструмент оценки эффективности крупномасштабных железнодорожных проектов

In this article, we modify the well-known inter-regional inter-sectoral optimization model (ОМММ), which makes it possible to incorporate a large-scale railway investment project. If the development of the national economy is deterministic, the model estimates the possible impact of the project on households' consumptive use. This helps to evaluate the macroeconomic efficiency of the project in the given development scenario. The uncertainty of economic development can be reflected in a representative set of scenarios. We consider the comparison of competing projects under the scenario uncertainty as a game against nature. We argue that Wald's criterion is appropriate for investors, whereas managers would possibly prefer Savage's criterion. If the projects use public funds, the interests of society and the government employees selecting projects for implementation may differ significantly due to inconsistency between the criteria.

Computational validation of the EPR™ combustible gas control system

Abstract During a postulated severe accident in a pressurized water reactor which involves the melting of the reactor core, hydrogen is released into the containment. In order to limit the pressure and temperature loads in case of a possible combustion of hydrogen and to prevent a challenge to the containment integrity, the EPR™ containment is equipped with a combustible gas control system. The multi-step approach for analytically validating the performance of this system is presented. The starting point for the analysis is a study of a large number of core melt accidents using a lumped-parameter code, which yields the release characteristics from the primary circuit into the containment. Based thereupon, a set of representative scenarios and bounding scenarios is selected for numerical calculations with a lumped-parameter code, and a smaller subset is then chosen for an additional in-depth analysis with computational fluid dynamics codes. Together, these calculations yield the detailed distribution of hydrogen in the containment, the depletion of hydrogen by recombiners, and the risk of the occurrence of potentially hazardous combustion modes. For cases where flame acceleration after hydrogen ignition cannot be ruled out by simple criteria, additional calculations with a dedicated computational fluid dynamics combustion code demonstrate that a transition from deflagration to detonation in the containment can be excluded. It is also shown that the temperature and pressure loads remain within the acceptable limits. Furthermore an in-depth analysis of the individual recombiner performance and methods for optimizing hydrogen depletion by the arrangement of the recombiners in the containment is presented. The potential impact of an early activation of the spray system on the hydrogen risk is also discussed.

Applying a framework for defining emergency management scenarios

Introduction: Scenarios are used extensively to support emergency management (EM). Virtually every user within the community, from policymakers to first responders, uses scenarios in one guise or another. They provide the context to characterize a dynamic problem space, to support the rehearsal of response options, and to facilitate the evaluation of new technology. With such far-reaching implications, there needs to be a means to guide scenario selection.Objective: The Canadian Centre for Security Science sponsored the development of a framework to characterize scenarios and to assist in evaluating EM capabilities, explicitly in the area of chemical, biological, radiological, and nuclear response. The framework also complements capability-based planning and provides a means to share scenarios.Methodology: The Public Safety and Security Planning Scenario Framework assists the EM community, which ranges from the national to the community level, by selecting scenarios based on user perspectives and objectives. In developing the framework, three challenges were addressed: a taxonomy was required to frame and define what constitutes a scenario; parameters were needed to describe and characterize scenarios; and structure was called for to assist in ordering the collection and comparison of representative scenarios.The first challenge involved reviewing existing literature to define the term “scenario.” Typically, scenarios are used to consider near-term threats, to capture planning assumptions, and to provide the perspective necessary to assess concepts and capabilities. The framework proposes a set of criteria or dimensions (eg, risks, triggers, and time horizons) that can be used to characterize scenarios. To test the framework, a representative set of scenarios was cataloged using these dimensions. Analysis of the resulting set was instructive in revealing the differences in planning scenarios across the chemical, biological, radiological/nuclear, and explosive communities. As the framework matures, it is hoped that it will promote information reuse and provide a valuable forum for capturing best practices and developing standards, enhancing efficiency and effectiveness improvements both locally and nationally.

Technology interactions among low-carbon energy technologies: What can we learn from a large number of scenarios?

Advanced low-carbon energy technologies can substantially reduce the cost of stabilizing atmospheric carbon dioxide concentrations. Understanding the interactions between these technologies and their impact on the costs of stabilization can help inform energy policy decisions. Many previous studies have addressed this challenge by exploring a small number of representative scenarios that represent particular combinations of future technology developments. This paper uses a combinatorial approach in which scenarios are created for all combinations of the technology development assumptions that underlie a smaller, representative set of scenarios. We estimate stabilization costs for 768 runs of the Global Change Assessment Model (GCAM), based on 384 different combinations of assumptions about the future performance of technologies and two stabilization goals. Graphical depiction of the distribution of stabilization costs provides first-order insights about the full data set and individual technologies. We apply a formal scenario discovery method to obtain more nuanced insights about the combinations of technology assumptions most strongly associated with high-cost outcomes. Many of the fundamental insights from traditional representative scenario analysis still hold under this comprehensive combinatorial analysis. For example, the importance of carbon capture and storage (CCS) and the substitution effect among supply technologies are consistently demonstrated. The results also provide more clarity regarding insights not easily demonstrated through representative scenario analysis. For example, they show more clearly how certain supply technologies can provide a hedge against high stabilization costs, and that aggregate end-use efficiency improvements deliver relatively consistent stabilization cost reductions. Furthermore, the results indicate that a lack of CCS options combined with lower technological advances in the buildings sector or the transportation sector is the most powerful predictor of high-cost scenarios.

Effects of oxcarbazepine and carbamazepine on driving ability: a double-blind, randomized crossover trial with healthy volunteers

Carbamazepine (CBZ) is known to produce cognitive side effects being at least partly relevant for driving. In contrast to this, the cognitive effects of oxcarbazepine (OXC) are suspected to be less pronounced. This study aimed to test 900 mg/day OXC as compared to 600 mg/day CBZ with respect to driving. Driving performance of 27 healthy volunteers under subchronic treatment of OXC and CBZ was assessed in a driving simulator with a double-blind, randomized, crossover design including a baseline measurement. The test course contained a representative set of scenarios. Lane-keeping performance, driving mistakes, and eyelid closure (as a behavioral measure of sleepiness) were analyzed. In addition, subjects were asked to assess their driving performance, effort, attention, and sleepiness subjectively. Both drugs had negative effects on driving as reflected in poorer lane-keeping performance, higher rate of driving mistakes, increased sleepiness, and worse subjective ratings. These effects were most obvious in monotonous situations and could be compensated in situations challenging to cognitive and motor driving skills. With respect to all considered parameters, CBZ did more often differ significantly from baseline than OXC. Under both drugs, driving performance was worse than at baseline. Even though deterioration of driving performance was only slightly less pronounced under OXC than under CBZ, it might be recommended as more appropriate than CBZ for epileptic patients who need to drive a car.

Exploitation of Ship Scattering in Polarimetric SAR for an Improved Classification Under High Clutter Conditions

This paper evaluates the potentialities of polarimetric ship scattering for basing classification methods that provide reasonable performance within cluttered scenes. Both simulated and airborne polarimetric synthetic aperture radar (SAR) images have been used to validate the conclusions of a previous phenomenological study. Numerical simulations have been carried out with GRECOSAR, a polarimetric interferometric SAR simulation tool that is able to process highly complex targets with a fast and accurate radar-cross-section prediction module. A representative set of scenarios has been defined, which includes various realistic ship models, sea states, and imaging geometries. In all of them, a two-scale sea surface model precisely accounting for sea-ship interaction and sea clutter has been added. The analysis of different images has shown that, with an adequate spatial resolution, ships may be characterized by a particular spatial arrangement and polarization state distribution of dominant scattering centers. This feature has allowed one to propose a new classification algorithm, which shows a promising behavior after various preliminary tests. In this paper, the performance of this technique is further evaluated with realistic clutter. The results show that robust classification is possible even in highly cluttered scenes if quad-pol imagery is available. On the contrary, in low clutter conditions, the usage of less restrictive solutions, like circular dual-pol schemes, is feasible and may still get an acceptable performance.

Multistage scenario-based interval-stochastic programming for planning water resources allocation

In this study, a multistage scenario-based interval-stochastic programming (MSISP) method is developed for water-resources allocation under uncertainty. MSISP improves upon the existing multistage optimization methods with advantages in uncertainty reflection, dynamics facilitation, and risk analysis. It can directly handle uncertainties presented as both interval numbers and probability distributions, and can support the assessment of the reliability of satisfying (or the risk of violating) system constraints within a multistage context. It can also reflect the dynamics of system uncertainties and decision processes under a representative set of scenarios. The developed MSISP method is then applied to a case of water resources management planning within a multi-reservoir system associated with joint probabilities. A range of violation levels for capacity and environment constraints are analyzed under uncertainty. Solutions associated different risk levels of constraint violation have been obtained. They can be used for generating decision alternatives and thus help water managers to identify desired policies under various economic, environmental and system-reliability conditions. Besides, sensitivity analyses demonstrate that the violation of the environmental constraint has a significant effect on the system benefit.

Short-Term Nurse Scheduling in Response to Daily Fluctuations in Supply and Demand

Hourly changes in patient census and acuity require hospitals to update their staffling needs on a continuing basis. This paper discusses the problem that management faces several times a day as the demand for nursing services departs from the planned schedule. Prior to the start of each shift, the number of nurses who are scheduled to be on duty over the next 24 hours is compared with the number actually available, and if shortages exist a series of decisions have to be made to ensure that each unit in the hospital has sufficient coverage. These decisions involve the use of overtime, outside nurses, and floaters. To address this problem, we have developed an integer programming model that takes the current set of rosters for regular and pool nurses and the expected demand for the upcoming 24 hours as input, and produces a revised schedule that makes the most efficient use of the available resources. The model is formulated and solved at a hospital-wide level rather than for each unit separately. To determine its applicability, a representative set of scenarios was investigated using data obtained from a medium-size facility in the U.S. with 14 units. The results indicate that problem instances with up to 120 nurses can be solved in a negligible amount of time.

Generating Scenarios for the Towers Perrin Investment System

The Towers Perrin company applies integrative asset-liability planning to the problem of pension management. The planning system depends upon a stochastic economic projection model—called CAP:Link—for generating economic factors and asset returns via a set of representative scenarios. Each scenario depicts a coherent set of outcomes. The projections span a long-run horizon—10 to 40 years. Risks and rewards for alternative investment strategies are determined via dynamic asset and liability allocation over the scenarios. The approach has been implemented in 12 countries in Europe, North America, and Asia.