Optimal Allocation of Spinning Reserves in Interconnected Energy Systems with Demand Response Using a Bivariate Wind Prediction Model

https://ideas.repec.org/a/gam/jeners/v12y2019i20p3816-d274620.htmlThe proposed study presents a novel probabilistic method for optimal allocation of spinning reserves taking into consideration load, wind and solar forecast errors, inter-zonal spinning reserve trading, and demand response provided by consumers as a single framework. The model considers the system contingencies due to random generator outages as well as the uncertainties caused by load and renewable energy forecast errors. The study utilizes a novel approach to model wind speed and its direction using the bivariate parametric model. The proposed model is applied to the IEEE two-area reliability test system (RTS) to analyze the influence of inter-zonal power generation and demand response (DR) on the adequacy and economic efficiency of energy systems. In addition, the study analyzed the effect of the bivariate wind prediction model on obtained results. The results demonstrate that the presence of inter-zonal capacity in ancillary service markets reduce the total expected energy not supplied (EENS) by 81.66%, while inclusion of a DR program results in an additional 1.76% reduction of EENS. Finally, the proposed bivariate wind prediction model showed a 0.27% reduction in spinning reserve requirements, compared to the univariate model

The behavior of manganese in oxygen steelmaking

Manganese serves as an important alloying element in commercial grades of steel and high levels of Mn can improve the mechanical properties of steel. [1] The chemical behavior of Mn in Oxygen Steelmaking is complex because the element is readily oxidised in conditions found in steelmaking but the stability of its oxide is a strong function of temperature and slag chemistry, and the oxide can readily revert back to elemental Mn in steelmaking conditions. In many steel plants, manganese ore has been added to achieve high Mn at the end blow. This approach means that the use of relatively expensive ferromanganese (FeMn) can be reduced in the subsequent secondary steelmaking process. [1] Steel plants can also face the problem of high Mn (>1 wt pct) in the hot metal due to the use of lean iron ores with high MnO in the blast furnace, and this can cause operational issues in the steelmaking process.[2

Dynamic modelling of BOF process : comparison of model performance with the plant data

In a top blowing steel making process (BOF), the refining reactions of C, Si, Mn and P are related to several physicochemical phenomena occurring in different zones (e.g. slag-metal emulsion, jet impact zone, slag-bulk metal zone) inside the converter. In earlier publications, the authors have demonstrated a dynamic model, based on the fundamental approach of multiple zone reaction kinetics to simulate the refining of elements (C, Si, Mn and P) in a top blowing steelmaking process (BOF).[1,2] After successful model validation with the literature data from a 200 ton converter[3], simulations have been carried out to assess the model performance with the plant data. Off-line heat data obtained from a 330 ton converter at Tata Steel, Netherlands was used for the model validation. The BOF shop in Tata Steel operates with a wide range of operating and process conditions such as (i) different scrap mix, (ii) dynamic flux addition strategy, (iii) dynamic change in lance position and (iv) top and bottom flow rate. The model predictions of hot metal impurities were validated with the two sub-lance measurements and the simulated slag compositions were compared with the end blow measurements. The possible effect of the uncertainties associated with the measured (or reported) input variables in industrial conditions on the accuracy of the model calculations has been investigated. Further, the role of dynamic change in lance height and flow rate on the slag formation and the hot metal refining has been studied. The present study identifies the critical input variables required for the accuracy in the prediction of the dynamic model in plant conditions and provides a fundamental understanding to control the dynamic process variables in a BOF operation

Modeling of droplet generation in a top blowing steelmaking process

Quantification of metal droplets ejected due to impinging gas jet on the surface of liquid metal is an important parameter for the understanding and for the modeling of the refining kinetics of reactions in slag-metal emulsion zone. In the present work, a numerical study has been carried out to critically examine the applicability of droplet generation rate correlation previously proposed by Subagyo et al. on the basis of dimensionless blowing number (N B). The blowing number was re-evaluated at the impingement point of jet with taking into account the temperature effect of change in density and velocity of the gas jet. The result obtained from the work shows that the modified blowing number N B,T at the furnace temperature of 1873 K (1600 °C) is approximately double in magnitude compared to N B calculated by Subagyo and co-workers. When N B,T has been employed to the Subagyo’s empirical correlation for droplet generation, a wide mismatch is observed between the experimental data obtained from cold model and hot model experiments. The reason for this large deviation has been investigated in the current study, and a theoretical approach to estimate the droplet generation rate has been proposed. The suitability of the proposed model has been tested by numerically calculating the amount of metals in slag. The study shows that the weight of metals in emulsion falls in the range of 0 to 21 wt pct of hot metal weight when droplet generation rate has been calculated at ambient furnace temperature of 1873 K (1600 °C)

Dynamic model of basic oxygen steelmaking process based on multi-zone reaction kinetics : modelling of manganese removal

In the earlier work, a dynamic model for the BOF process based on the multi-zone reaction kinetics has been developed. In the preceding part, the mechanism of manganese transfer in three reactive zones of the converter has been analyzed. This study identifies that temperature at the slag-metal reaction interface plays a major role in the Mn reaction kinetics and thus a mathematical treatment to evaluate temperature at each reaction interface has been successfully employed in the rate calculation. The Mn removal rate obtained from different zones of the converter predicts that the first stage of the blow is dominated by the oxidation of Mn at the jet impact zone, albeit some additional Mn refining has been observed as a result of the oxidation of metal droplets in emulsion phase. The mathematical model predicts that the reversion of Mn from slag to metal primarily takes place at the metal droplet in the emulsion due to an excessive increase in slag-metal interface temperature during the middle stage of blowing. In the final stage of the blow, the competition between simultaneous reactions in jet impact and emulsion zone controls the direction of mass flow of manganese. Further, the model prediction shows that the Mn refining in the emulsion is a strong function of droplet diameter and the residence time. Smaller sized droplets approach equilibrium quickly and thus contribute to a significant Mn conversion between slag and metal compared to the larger sized ones. The overall model prediction for Mn in the hot metal has been found to be in good agreement with two sets of different size top blowing converter data reported in the literature

Dynamic model of basic oxygen steelmaking process based on multi-zone reaction kinetics : model derivation and validation

A multi-zone kinetic model coupled with a dynamic slag generation model was developed for the simulation of hot metal and slag composition during the BOF operation. The three reaction zones, (i) jet impact zone (ii) slag-bulk metal zone (iii) slag-metal-gas emulsion zone were considered for the calculation of overall refining kinetics. In the rate equations, the transient rate parameters were mathematically described as a function of process variables. A micro and macroscopic rate calculation methodology (micro-kinetics and macro-kinetics) were developed to estimate the total refining contributed by the recirculating metal droplets through the slag-metal emulsion zone. The micro-kinetics involves developing the rate equation for individual droplets in the emulsion. The mathematical models for the size distribution of initial droplets, kinetics of simultaneous refining of elements, the residence time in the emulsion, dynamic interfacial area change were established in the micro-kinetic model. In the macro-kinetics calculation, a droplet generation model was employed and the total amount of refining by emulsion was calculated by summing the refining from the entire population of returning droplets. A dynamic FetO generation model based on oxygen mass balance was developed and coupled with the multi-zone kinetic model. The effect of post combustion on the evolution of slag and metal composition was investigated. The model was applied to a 200-ton top blowing converter and the simulated value of metal and slag was found to be in good agreement with the measured data. The post-combustion ratio was found to be an important factor in controlling FetO content in the slag and the kinetics of Mn and P in a BOF process

APPLICATION OF PROBABILISTIC METHODS FOR EFFECTIVE AND RELIABLE OPERATION OF ELECTRICAL AND ELECTROMECHANICAL SYSTEMS

This PhD thesis presents novel system control methods that can be utilized for effective and reliable operation of electric grids and passenger elevators. First of all, this study introduces a new spinning reserve allocation optimization technique that takes into account load and renewable power generation, inter-zonal conventional power generating capacity and demand response. Using the bivariate Farlie-Gumbel-Morgenstern probability density function, the framework presented in this thesis utilizes a new method to simulate the power generation of wind farms. In addition, the presented framework uses a Bayesian Network (BN) algorithm to fine-tune the spinning reserve allocation based on previous hours' actual unit commitment, as well as the hour and day types. The model proposed in this study has been tested on the IEEE Two-Area Reliability Test System (RTS) to quantify the effect of the bivariate wind prediction model and the Bayesian network-based Reserve Allocation Adjustment Algorithm (RAAA) on reliability and cost-effectiveness of the power grid. The findings show that combining a bivariate wind forecast model with RAAA improves power grid stability by 2.66 percent while lowering overall system running costs by 1.12 percent. Secondly, the present work introduces an algorithm with an objective of optimal dispatching control of passenger lifts. The algorithm utilizes the data received from video cameras and dispatches the elevator cars based on the passenger count. The proposed algorithm utilizes the information on the number of people and dispatches the lifts with an objective to move the maximum number of passengers to the desired building levels within the minimum amount of time. In addition, the algorithm considers each person's size and whether or not they have luggage. To account for uncertainty in image acquisition, the algorithm assigns the probability weights to the number of people who are waiting for a lift and riding the lifts. The main purpose of the algorithm is to minimize the following performance metrics: average travel time (ATT), average journey time (AJT) and average waiting time (AWT). The suggested algorithm works well in situations of limited traffic sizes, according to a test case scenario conducted on a ten-story office building having four elevator cars (less than 200 people). In a scenario with large up-peak high intensity traffic, the proposed algorithm primarily underperforms. The proposed algorithm's best output was seen in situations with random inter-floor passenger movement. In scenarios of changing traffic intensity and size ATT increased by 39.94 percent and 19.53 percent, respectively

Probabilistic method for estimation of spinning reserves in multi-connected power systems with Bayesian network-based rescheduling algorithm

This study proposes a new stochastic spinning reserve estimation model applicable to multi-connected energy systems with reserve rescheduling algorithm based on Bayesian Networks. The general structure of the model is developed based on the probabilistic reserve estimation model that considers random generator outages as well as load and renewable energy forecast errors. The novelty of the present work concerns the additional Bayesian layer which is linked to the general model. It conducts reserve rescheduling based on the actual net demand realization and other reserve requirements. The results show that the proposed model improves estimation of reserve requirements by reducing the total cost of the system associated with reserve schedule. Copyright © 2019 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserve

Retraitement combiné par interféron alpha-2b pégylé et ribavirine des non-réponses après traitement par interféron seul ou interféron et ribavirine dans les hépatites chroniques virales C (étude pilote comparant deux schémas thérapeutiques)

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