Air conditioning loads (ACLs) are potential flexible resources that can provide various grid services to the power system. Recent studies have attempted to represent their flexibility using a virtual battery (VB) model for quantification, but the modeling process requires information on thermal parameters and heat disturbances (e.g., solar irradiation and internal heat load) that are difficult to measure. In this paper, we present a new method that models a VB without prior knowledge of such information. First, we construct a thermal dynamic model of an individual ACL using historical input-output data. The linear regression model parameters are identified without using the measurements of disturbances. Second, we derive a VB model from the linear regression parameters using a change of variable technique. We show that the VB can be directly modeled from the regression model of thermal dynamics without estimating the exact thermal parameters and heat disturbances. Third, aggregation of the VB models is implemented. The energy limits of aggregate VB models are designed considering the baseline load prediction error caused by disturbance uncertainty. Finally, simulation results verify the accuracy and effectiveness of the proposed VB modeling strategy.
This paper develops a new analytical model to estimate real-time variations in grid frequency and voltages resulting from dynamic network reconfiguration (DNR). In the proposed model, switching operations are considered as discrete variations in an admittance matrix, leading to step variations in node injection currents. The network model with discrete admittance variations is then integrated with dynamic models of synchronous generators and voltage-dependent loads, enabling analysis of the dynamic grid operations initiated by the DNR. Case studies are performed to validate the proposed model via comparison with a conventional model and a comprehensive MATLAB/SIMULINK model.
The American Petroleum Institute guideline provides a methodology for calculating the risks of equipments installed in refineries or petrochemical plants. However, especially in connection with consequence analysis, there are limitations of its direct application to petrochemical plant. As a principal cause, only representative material is recommended for the risk evaluation while the equipment contains numerous materials. The objectives of this paper are to propose an enhanced risk-based inspection (RBI) method to resolve above shortcomings and to assess the risks of typical petrochemical equipments. In this respect, a RBI program including a material management database is developed to fully incorporate the characteristics of different materials. The proposed program consists of qualitative, semi-quantitative, and quantitative evaluation modules in which representative material, as well as toxic materials, is selected automatically for comparison to those getting from the current guideline. It has been applied to assess the risks of major equipments in ethylene facility such as vessel and column. Thereby, realistic evaluation results were obtained and applicability of the proposed RBI program was proven.
Abstract An active balun using current steering topology is presented for phase and amplitude corrections. The proposed active balun is constructed with two different unit balun structures based on resistive feedback to reduce phase and amplitude errors. The first unit balun composed of the common source configuration provides a main transconductance stage for the proposed active balun. The second unit balun using common source and common gate configurations works for phase and gain error reduction. The resistive feedback proposed here leads to mitigation of phase error and then amplitude error, and an equation for deciding the feedback resistance is successfully deduced. The value of a feedback resistor is determined to provide the least phase and amplitude errors by optimizing the derived design equation. Designed and fabricated active balun in 65 nm CMOS process operates over 1.0–1.2 GHz band, showing input and output reflection coefficients under –10 dB, phase error under 3.5° and gain error under 1.4 dB over the frequency of interest. Moreover, the gain is measured to be 4 dB maximum and power consumption of 2.6 mW is measured.
This paper addresses the problem of scheduling the optimal power outputs and moving paths of mobile energy storage devices (MESDs) in a distribution network with the aim of minimizing the total cost for energy loss. The MESDs can be loaded on electric trucks and timely connected to charging stations for plug-in electric vehicles. The moving distances and transit time periods of the MESDs are modeled using a set of linear equations with consideration of road traffic congestion for commuting time periods. The optimization problem can then be effectively solved using a mixed-integer linear programming algorithm. Simulation case studies are performed using a modified IEEE 34-Node Test Feeder. The case study results verify that the proposed scheduling method leads to the effective use of the MESDs to minimize the total cost for the energy loss in the power and traffic networks, compared to the conventional method using stationary batteries.
Fluidized bed incinerators are more efficient and safe for treating explosive waste than previous methods because they can emit nitrogen oxide (NOx) concentrations below the standard value (90 ppm). However, a limitation is that they have only focused on optimizing the operating conditions to minimize NOx emission concentrations till now. In this situation, it is crucial to balance NOx and process costs. Therefore, this study designed an explosive waste incineration process and performed multi-objective optimization. An artificial neural network surrogate modeling method is vital to reduce optimization time. Therefore, surrogate models with 95% and 99% accuracies were obtained, reducing the calculation time by 90%. Furthermore, an index combining NOx emission concentrations and process costs was proposed to obtain an optimal balanced operating condition of the process. By optimizing the process index, a new operating condition was obtained that could reduce 20% of the process costs while maintaining NOx emission concentrations within the standard limit. The proposed operating condition and data, such as from sensitivity analysis, would provide a valuable guideline for operating the abovementioned process associated with NOx emission standards.
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