Future Operating Conditions Research Articles

Pan-European meteorological and snow indicators of climate change impact on ski tourism

Ski tourism plays a major socio-economic role in the snowy and mountainous areas of Europe such as the Alps, the Pyrenees, Nordic Europe, Eastern Europe, Anatolia, etc. Past and future climate change has an impact on the operating conditions of ski resorts, due to their reliance on natural snowfall and favorable conditions for snowmaking. However, there is currently a lack of assessment of past and future operating conditions of ski resorts at the pan-European scale in the context of climate change. The presented work aims at filling this gap, as part of the ”European Tourism” Sectoral Information System (SIS) of the Copernicus Climate Change Services (C3S). The Mountain Tourism Meteorological and Snow Indicators (MTMSI) were co-designed with representatives of the ski tourism industry, including consulting companies. They were derived from statistically adjusted EURO-CORDEX climate projections (multiple GCM/RCM pairs for RCP2.6, RCP4.5 and RCP8.5) using the UERRA 5.5 km resolution surface reanalysis as a reference, used as input to the snow cover model Crocus, with and without accounting for snow management (grooming, snowmaking). Results are generated for 100 m elevation bands for NUTS-3 geographical areas spanning all areas relevant to ski tourism in Europe. This article introduces the underpinning elements for the generation of this product, and illustrates results at the pan-European scale as well as for smaller scale case studies. A dedicated visualization app allows for easy navigation into the multiple dimensions of this dataset, thereby fulfilling the needs of a broad range of users.

Development of a Mobile Robot Test Platform and Methods for Validation of Prognostics-Enabled Decision Making Algorithms

As fault diagnosis and prognosis systems in aerospace applications become more capable, the ability to utilize information supplied by them becomes increasingly important. While certain types of vehicle health data can be effectively processed and acted upon by crew or support personnel, others, due to their complexity or time constraints, require either automated or semi-automated reasoning. Prognostics-enabled Decision Making (PDM) is an emerging research area that aims to integrate prognostic health information and knowledge about the future operating conditions into the process of selecting subsequent actions for the system. The newly developed PDM algorithms require suitable software and hardware platforms for testing under realistic fault scenarios. The paper describes the development of such a platform, based on the K11 planetary rover prototype. A variety of injectable fault modes are being investigated for electrical, mechanical, and power subsystems of the testbed, along with methods for data collection and processing. In addition to the hardware platform, a software simulator with matching capabilities has been developed. The simulator allows for prototyping and initial validation of the algorithms prior to their deployment on the K11. The simulator is also available to the PDM algorithms to assist with the reasoning process. A reference set of diagnostic, prognostic, and decision making algorithms is also described, followed by an overview of the current test scenarios and the results of their execution on the simulator.

Physics Based Degradation Models for Electrolytic Capacitor Prognostics under Thermal Overstress Conditions

Electronics subsystems play an increasingly important role in safety critical systems for monitoring, control, and enhanced functionality. Electrolytic capacitors are an important component in ,amy subsystems that range from power supplies on safety critical avionics equipment to power drivers for electro-mechanical actuators. These capacitors are ideal forpassing or bypassing low-frequency signals in power supplies but are known to have lower reliability compared to ceramic and tantalum capacitors, and given their criticality in electronicssubsystems they are a good candidate for component level monitoring and prognostics. Prognostics provides a way to assess remaining useful life of components and systems based on their current state of health and their anticipated future use and operating conditions. Past experiences have shown that capacitor degradation and failures are quite prevalent under high electrical and thermal stress conditions that they experience during operations. Our focus in this work is on deriving a physics-based degradation model for electrolytic capacitors under thermal stress conditions. As part of our methodology, we study the effects of accelerated aging due to thermal stress on a batch of capacitors stored at high ambient temperature conditions. This provides a framework for supplementing theoretical modeling with data collected from simultaneous experiments, which is then used to validate the derived models. This work represents a first step toward combining data driven and physics-based approaches for modeling capacitor degradation. The case study shows how the proposed technique is applied to a batch of identically manufactured capacitors.

Towards A Model-based Prognostics Methodology for Electrolytic Capacitors: A Case Study Based on Electrical Overstress Accelerated Aging

This paper presents a model-driven methodology for predicting the remaining useful life of electrolytic capacitors. This methodology adopts a Kalman filter approach in conjunction with an empirical state-based degradation model to predict the degradation of capacitor parameters through the life of the capacitor. Electrolytic capacitors are important components of systems that range from power supplies on critical avionics equipment to power drivers for electro-mechanical actuators. These devices are known for their comparatively low reliability and given their critical role in the system, they are good candidates for component level prognostics and health management. Prognostics provides a way to assess remaining useful life of a capacitor based on its current state of health and its anticipated future usage and operational conditions. This paper proposes and empirical degradation model and discusses experimental results for an accelerated aging test performed on a set of identical capacitors subjected to electrical stress. The data forms the basis for developing the Kalman-filter based remaining life prediction algorithm.

Embedded Digital Twins in future energy management systems: paving the way for automated grid control

Abstract Emerging real-time applications in information technology, and operational technology enable new innovative concepts to design and operate cyber-physical systems. A promising approach, which has been discovered recently as key technology by several industries is the Digital Twin (DT) concept. A DT connects the virtual representation of a physical object, system or process by available information and sensor data streams, which allows to gather new information about the system it mirrors by applying analytic functions. Thereby the DT technology can help to fill sensor data gaps, e. g., to support anomaly detection, and to predict future operating conditions and system states. This paper discusses a dynamic power system DT as a cornerstone instance of a new generation of EMS, and a prospective new EMS architecture, to support the increasingly complex operation of electric power systems. Unlike in traditional offline power system models, the parameters are updated dynamically using measurement information from the supervisory control and data acquisition (SCADA) and a wide area monitoring system (WAMS) to tune the model. This allows to derive a highly accurate virtual representation of the mirrored physical objects. A simulation engine, the Digital Dynamic Mirror (DDM) is introduced, in order to be able to reproduce the state of a reference network in real-time. The validation of the approach is carried out by a case study. In a closed loop within EMS applications, the DDM can help to assess contingency mitigation strategies, thus it can support the decision-making process under variable system conditions. The next generation of control centre Energy Management System (EMS) can benefit from this development by augmentation of the dynamic observability, and the rise of operator situation awareness.

Study on the simulation and optimization of the heat transfer scheme in a buried-pipe ground-source heat pump

To guarantee the sustainable development and utilization of shallow geothermal energy and avoid heat accumulation problems during the operating period of ground-source heat pump, a three-dimensional coupled numerical model of unsteady groundwater seepage and heat transfer was established. This model was applied to the demonstration project of the buried-pipe heat pump system in Danyang, Jiangsu Province. The model was based on the principles of groundwater seepage and heat transport and was combined with the future operating conditions of the ground-source heat pump system, to forecast the thermal equilibrium development trend of underground temperature field. A design scheme was determined on this basis. The district was divided into five development and utilization zones (I–V), and we changed the minimum heat transfer hole spacing from 5 to 14, 17, 18, 21, and 19 m. From the results, according to the actual situation we adopted, the simulation optimization scheme effectively slowed the heat transfer and resolved the heat accumulation problem, which can easily occur during the operation of a ground-source heat pump system. The three-dimensional coupled numerical model of unsteady groundwater seepage and heat transfer is an effective way to optimize and determine the sustainable development and utilization plan of a ground-source heat pump for shallow geothermal energy.

Polder pumping-station for the future: designing and retrofitting infrastructure systems under structural uncertainty

ABSTRACT In designing and retrofitting infrastructure systems, engineers are increasingly confronted by uncertainties about the future operating conditions of these systems, stemming from climate change or rapid socio-economic development. Particularly for long-lived capital-intensive infrastructure components like pumping stations, current engineering practices need to be complemented by structured approaches for designing infrastructures whose performance is robust to a wide range of possible future operating conditions. This paper presents multi-objective robust simulation as a viable approach. We investigate its applicability in a case study of the re-design of a polder pumping-station in The Netherlands. The research demonstrates the added value of multi-objective robust simulation in establishing robust design alternatives. The approach generates additional decision relevant insights into the performance of the pumping-station under uncertain future conditions while supplying decision-makers with the information required to make informed trade-offs amongst key design choices.

On-line optimization of four-zone simulated moving bed chromatography using an Equilibrium-Dispersion Model: II. Experimental validation

In the first theoretical part of this work, an on-line optimization method using the equilibrium-dispersion model of chromatography was developed for the dynamic optimization of periodically operated four-zone simulated moving bed (SMB) processes. In this second part, we present the experimental validation of the proposed concept. To describe the behaviors of real SMB systems, the on-line control unit was modified. Unavoidable system void volumes, which can be described using delay functions corresponding to standard mass balance models for pipes and CSTs, were considered in addition to the chromatographic columns. Furthermore, a new sample collection method that does not interrupt product streams was introduced for process monitoring. The equipment including an external concentration analysis unit was automated via a tailor-made interface program. The on-line control unit estimated the parameters of the process model and optimized the future operating conditions ‘switch-by-switch’. The case study investigated in this work was the separation of racemic mixtures of two bicalutamide enantiomers using a chiral stationary phase applying the conventional and two advanced SMB operating modes.

Assessment of potential service-life performance for MarBN steel power plant header under flexible thermomechanical operations

This paper is concerned with the service-life assessment of 9Cr steels superheater outlet steam header subjected to realistic subcritical and future ultra-super critical flexible operating conditions. The proposed methodology is achieved via a combined program of high temperature strain-controlled fatigue tests, temperature- and time-dependent unified viscoplastic model for thermomechanical fatigue analysis, and Smith-Watson-Topper critical plane criterion for multi-axial life prediction. Samples of idealised operational transients with particular attention on the starting-up cycle is fully coupled with the computational modelling of header component for the high temperature performance assessment. The predicted results indicate that: (i) the header shell inner-bore saddles at weld regions are the critical locations that lead to earlier potential fatigue crack initiation, and (ii) the predicted lifetime under subcritical conditions correlates reasonably with the industrial experience. A steam header manufactured from MarBN operating under ultra-super critical condition is shown to have comparable life performance with the P91 header operating under subcritical condition.

Power Management by LSTM Network for Nanogrids

Nanogrids can be considered smart grids that are implemented for small-scale buildings, houses, and apartments. A typical power management framework for nanogrids determines the scheduling of operations of electric appliances for each time interval with objectives related to total power consumption and total delay due to scheduling. Such a framework of power management has limitations in accommodating future operating conditions of nanogrids. Taking future outdoor temperature as a future operating condition, a proactive power management for nanogrids is presented in this paper. The goal of proactive power management for nanogrids is to achieve the proper level of indoor temperature in a cost-efficient way, sooner rather than later, by taking into account future outdoor temperature. To achieve this goal, a long short-term memory (LSTM) network is used as the controller. Simulations have been performed to verify the performance of the proposed power management. The results of the simulations demonstrate that living comfort measured in terms of room temperature is enhanced while the overall electricity cost is reduced, mainly due to the ability of the LSTM network to predict the trend of outdoor temperature.

Comparison of different models of future operating condition in Particle-Filter-based Prognostic Algorithms

Abstract In literature, a major part of the prognostic studies considers the mission profile as a static parameter when evaluating the system Remaining Useful Life (RUL). However, in practice, the way in which a system operates significantly impacts the future evolution of its degradation. Therefore, this paper aims at evaluating the impact associated with the utilization of three different methods to characterize future operating conditions within the implementation of probability-based prognostic algorithms, namely Long-short term memory (LSTM), Markov Chain and Constant (or time-invariant) usage. These three methods are compared together in terms of both prognostic accuracy and essential update times when investigating the Time-of-Discharge (ToD) of an electric bicycle Lithium-Ion (Li-Ion) battery.

Domain Adaptation for One-Class Classification: Monitoring the Health of Critical Systems Under Limited Information

The failure of a complex and safety critical industrial asset can have extremely high consequences. Close monitoring for early detection of abnormal system conditions is therefore required. Data-driven solutions to this problem have been limited for two reasons: First, safety critical assets aredesigned and maintained to be highly reliable and faults are rare. Fault detection can thus not be solved with supervised learning. Second, complex industrial systems usually have long lifetime during which they face very different operating conditions. In the early life of the system, the collected data is probably not representative of future operating conditions, making it challenging to train a robust model. In this paper, we propose a methodology to monitor the systems in their early life. To do so, we enhance the training dataset with other units from a fleet, for which longer observations are available. Since each unit has its own specificity, we propose to extract features made independent of their origin by three unsupervised feature alignment techniques. First, using a variational encoder, we impose a shared probabilistic encoder/decoder for both units. Second, we introduce a new loss designed to conserve inter-point spacial relationships between the input and the learned features. Last, wepropose to train in an adversarial manner a discriminator on the origin of the features. Once aligned, the features are fed to a one-class classifier to monitor the health of the system. By exploring the different combinations of the proposed alignment strategies, and by testing them on a real case study, a fleet composed of 112 power plants operated in different geographical locations and under very different operating regimes, we demonstrate that this alignment is necessary and beneficial.

Battery remaining discharge energy estimation based on prediction of future operating conditions

Abstract The battery remaining discharge energy has a significant influence on the remaining driving range of pure electric vehicles; therefore, it requires precise estimation. This paper introduces a novel approach for battery remaining discharge energy estimation based on the accurate prediction of future operating conditions. In the proposed battery remaining discharge energy estimation algorithm, the battery future power output and temperature change rate are firstly predicted according to the historical data. An equivalent circuit model, with the parameters identified using the recursive least-squares algorithm, is applied to simulate the battery voltage response. Battery remaining discharge energy is then estimated based on the iterative prediction of battery states (including the state of charge, temperature, battery model parameter variations and voltage response) in the future discharge process. The performance of the prediction-based remaining discharge energy estimation approach is validated under dynamic current and temperature conditions. The results demonstrate that the proposed method can guarantee desirable accuracy and robustness in battery remaining discharge energy estimation under dynamic operating conditions, exhibiting its potential for practical applications.

Risk‐based security assessment of transmission line overloading considering spatio‐temporal dependence of load and wind power using vine copula

Location of wind power plants and demand centres are not always close by; hence, the transmission of energy puts a burden on existing grid infrastructure. This unwanted burden necessitates transmission lines to operate more and more frequently close to their operating limits. To alleviate such situations, this research addresses the advantages of modelling spatio-temporal dependence of load and wind power using vine copula. Probabilistic AC optimal power flow is performed on a modified IEEE 39-bus system with significant wind penetration. Real load and wind power data from a U.S. utility is mapped onto the test-case to achieve realistic results. Load flow calculation can help in performing steady-state voltage and overload evaluations for post-disturbance system conditions. Because the security level of a power system is determined by the likelihood and severity of security violation. In this research, the probability of line overload is calculated from load flow and the severity function describes the risk of line overloading. Two case studies depicting future operating conditions of massive wind power penetration with reduced fossil fuel and nuclear power generation are considered. Simulation results prove the advantage of addressing spatio-temporal dependency to quantify the overload risk index, which is treated as a security indicator.

Application of Discrete-Interval Moving Seasonalities to Spanish Electricity Demand Forecasting during Easter

Forecasting electricity demand through time series is a tool used by transmission system operators to establish future operating conditions. The accuracy of these forecasts is essential for the precise development of activity. However, the accuracy of the forecasts is enormously subject to the calendar effect. The multiple seasonal Holt–Winters models are widely used due to the great precision and simplicity that they offer. Usually, these models relate this calendar effect to external variables that contribute to modification of their forecasts a posteriori. In this work, a new point of view is presented, where the calendar effect constitutes a built-in part of the Holt–Winters model. In particular, the proposed model incorporates discrete-interval moving seasonalities. Moreover, a clear example of the application of this methodology to situations that are difficult to treat, such as the days of Easter, is presented. The results show that the proposed model performs well, outperforming the regular Holt–Winters model and other methods such as artificial neural networks and Exponential Smoothing State Space Model with Box-Cox Transformation, ARMA Errors, Trend and Seasonal Components (TBATS) methods.

A Decision Support System for Proactive-Robust Traffic Network Management

Real-time traffic network management systems are envisioned to provide network operators with decision support capabilities to alleviate recurrent and non-recurrent congestion. These capabilities involve predicting the network congestion dynamics and facilitating the development of proactive traffic management schemes that integrate traffic control and demand management strategies. However, traffic networks are subject to numerous sources of stochasticity that make it difficult to accurately predict their operational conditions and generate effective traffic management schemes to cope with these conditions. This paper presents a decision support system for proactive-robust traffic network management, which accounts for uncertainty in the network operational conditions. The objective is to develop robust traffic management schemes such that the network overall performance remains close to optimality under all possible future operational conditions. The modeling framework of the system is presented, which adopts a rolling horizon framework that integrates a meta-heuristic search algorithm and a dynamic traffic assignment simulation-based methodology. The system performance is examined through application to the traffic network of the US-75 corridor in Dallas, TX, USA.

China's Industrial Economy 2018 Q1 Report

In Q1, the Business Sentiment Index stood at 51, indicating an expansion, albeit small, for the first time in four years. This expansion, mainly driven by state-owned and foreign firms, has been largely due to the optimistic expectations of future operating conditions. Private firms – the vast majority of industrial firms – stayed flat (50). In addition, production, electricity consumption and both foreign and domestic orders, with the diffusion indices ranging between 47 and 49, showed signs of slight contraction. Investments were still weak. Overcapacity remains the biggest challenge, suggesting a continued need to curtail production capacity. In addition, persistent rises in raw material costs and the resulting price inflation since 2016 Q4 may hinder the recovery of the industrial economy. Overall, supply-side reform has made positive progress in solving the structural problems of China’s industrial economy. The industrial economy continues an L-shaped long-term trend, stable and with noticeable signs of improvement. Given the government’s strong commitment to economic development, we remain optimistic about the long-term outlook of the Chinese economy.

Probabilistic evaluation of the long-term power system resource adequacy: The Greek case

This paper deals with the probabilistic evaluation of the long-term resource adequacy of an interconnected power system. An integrated software tool that solves the long-term scheduling problem using a sophisticated unit commitment model is employed, while both traditional capacity and flexibility adequacy concepts are addressed. A novel methodology to assess the flexibility adequacy using the operational power system schedule is presented. Multi-year simulations of the Greek interconnected power system on an hour-by-hour basis for the forthcoming 10-year study horizon (period 2018–2027) under a set of scenarios regarding the future operating conditions have been performed, providing the key operational data for the calculation of all probabilistic indicators. Test results indicate that the existence of all currently available thermal generating units as well as the timely realization of all scheduled construction plans is of utmost importance for the long-term secure and reliable operation of the Greek interconnected power system. In a more generalized framework, the ambitious EU goals regarding decarbonization and increasing RES shares in electricity generation should be accompanied by detailed studies in order to ensure that no resource adequacy problems will arise in the near future due to the massive withdrawal of conventional base-load and, possibly, flexible generating units.

Estudio por Microáreas de la Demanda Actual y Futura del Área de Cobertura de la Subestación 57 Pomasqui del Distrito Metropolitano de Quito

El trabajo analiza la demanda actual de la zona de cobertura de la subestación 57 Pomasqui de propiedad de la Empresa Eléctrica Quito (EEQ), y establece la proyección de la demanda al año 2018 usando el método por microáreas. Para ello, el estudio considera dos grandes etapas: 1) determinación de la demanda máxima de cada microárea, y 2) estimación del área habilitada de construcción de cada zona, basándose en el Plan de Uso y Ocupación del Suelo (PUOS) promulgado en el Plan Metropolitano de Ordenamiento Territorial (PMOT) de la ciudad de Quito. Los resultados obtenidos en estas dos etapas se utilizan, en combinación con la curva logística, para realizar la proyección de la demanda al año 2018. Es decir, se basa en el hecho que el crecimiento de la demanda eléctrica sigue el comportamiento de la curva logística y considera los resultados de la proyección de la demanda. Esto establece un procedimiento que permite estimar la demanda para el año 2018. Finalmente, con los resultados de la demanda proyectada al año 2018, se realizan estudios de flujos de potencia usando el software CYMDIST con el fin de estimar las condiciones operativas futuras. Esto permite detectar posibles problemas y comentar alternativas de solución en la planificación de distribución a corto y mediano plazo.

Forecasting operational strategic positioning in Taiwan's aerospace industry

Business managers and investors have an interest in knowing the future operational conditions of business in an industry. Thus, this study proposes a new model for forecasting and evaluating operational strategic positioning. The grey theory GM (1,1) prediction model is used for the forecasting of business operational conditions. The Boston Consulting Group (BCG) matrix is used as a prototype to develop a positioning matrix of operational efficiency for measuring business operational changes and assessing the operational strategic positioning. This study applies the earnings per share (EPS) of six listed aerospace industrial component suppliers in Taiwan, for the period from 2010 to 2015 to construct the forecasting model. The data are used to verify the reliability of the operational strategic positioning model and forecast the future operations for the period from 2016 to 2017. Finally, the operational efficiency matrix is used to position the operational strategies of these six enterprises. The results of this study can be used as a reference by managers and investors to develop operational and investment strategies.