Review of virtual power plant applications for the power system management and V2G market development

Utilization of renewable energy sources and energy storage systems is increasing with fostering new policies on energy industries. However, the increase of distributed generation hinders the reliability of power systems. In order to stabilize them, a virtual power plant emerges as a novel power grid management system. The VPP has a role to make a participation of different distributed energy resources and energy storage systems. This paper defines core technology of the VPP which are demand response and ancillary service concerning about Korea, America and Europe cases. It also suggests application solutions of the VPP to V2G market for restructuring national power industries in Korea.

Radiological and material characterization of high volume fly ash concrete

The main goal of research presented in this paper was the material and radiological characterization of high volume fly ash concrete (HVFAC) in terms of determination of natural radionuclide content and radon emanation and exhalation coefficients. All concrete samples were made with a fly ash content between 50% and 70% of the total amount of cementitious materials from one coal burning power plant in Serbia. Physical (fresh and hardened concrete density) and mechanical properties (compressive strength, splitting tensile strength and modulus of elasticity) of concrete were tested. The radionuclide content (226Ra, 232Th and 40K) and radon massic exhalation of HVFAC samples were determined using gamma spectrometry. Determination of massic exhalation rates of HVFAC and its components using radon accumulation chamber techniques combined with a radon monitor was performed. The results show a beneficial effect of pozzolanic activity since the increase in fly ash content resulted in an increase in compressive strength of HVFAC by approximately 20% for the same mass of cement used in the mixtures. On the basis of the obtained radionuclide content of concrete components the I -indices of different HVFAC samples were calculated and compared with measured values (0.27e0.32), which were significantly below the recommended 1.0 index value. The prediction was relatively close to the measured values as the ratio between the calculated and measured I-index ranged between 0.89 and 1.14. Collected results of mechanical and radiological properties and performed calculations clearly prove that all 10 designed concretes with a certain type of fly ash are suitable for structural and non-structural applications both from a material and radiological point of view.

A Generalized Fuzzy Linguistic Model for Predicting Component Concentrations in an Optical Gas Sensing System

Motivated by environmental protection concerns, monitoring the flue gas of thermal power plant is now often mandatory due to the need to ensure that emission levels stay within safe limits. Optical based gas sensing systems are increasingly employed for this purpose, with regression techniques used to relate gas optical absorption spectra to the concentrations of specific gas components of interest (NOx, SO2 etc.). Accurately predicting gas concentrations from absorption spectra remains a challenging problem due to the presence of nonlinearities in the relationships and the high-dimensional and correlated nature of the spectral data. This article proposes a generalized fuzzy linguistic model (GFLM) to address this challenge. The GFLM is made up of a series of “If-Then” fuzzy rules. The absorption spectra are input variables in the rule antecedent. The rule consequent is a general nonlinear polynomial function of the absorption spectra. Model parameters are estimated using least squares and gradient descent optimization algorithms. The performance of GFLM is compared with other traditional prediction models, such as partial least squares, support vector machines, multilayer perceptron neural networks and radial basis function networks, for two real flue gas spectral datasets: one from a coal-fired power plant and one from a gas-fired power plant. The experimental results show that the generalized fuzzy linguistic model has good predictive ability, and is competitive with alternative approaches, while having the added advantage of providing an interpretable model.

A generalized fuzzy linguistic model for predicting component concentrations in an optical gas sensing system

Motivated by environmental protection concerns, monitoring the flue gas of thermal power plant is now often mandatory due to the need to ensure that emission levels stay within safe limits. Optical based gas sensing systems are increasingly employed for this purpose, with regression techniques used to relate gas optical absorption spectra to the concentrations of specific gas components of interest (NO_x, SO₂ etc.). Accurately predicting gas concentrations from absorption spectra remains a challenging problem due to the presence of nonlinearities in the relationships and the high-dimensional and correlated nature of the spectral data. This article proposes a generalized fuzzy linguistic model (GFLM) to address this challenge. The GFLM is made up of a series of “If-Then” fuzzy rules. The absorption spectra are input variables in the rule antecedent. The rule consequent is a general nonlinear polynomial function of the absorption spectra. Model parameters are estimated using least squares and gradient descent optimization algorithms. The performance of GFLM is compared with other traditional prediction models, such as partial least squares, support vector machines, multilayer perceptron neural networks and radial basis function networks, for two real flue gas spectral datasets: one from a coal-fired power plant and one from a gas-fired power plant. The experimental results show that the generalized fuzzy linguistic model has good predictive ability, and is competitive with alternative approaches, while having the added advantage of providing an interpretable model.