A medium and long-term carbon emission forecasting method for provincial power grid
The development of low-carbon electric power industry is a key way to deal with the greenhouse effect and lower the PM2.5. When integrated into power grid, renewable energy can efficiently cut down the output of the thermal power and reduce the carbon emission of the grid significantly. However, it lacks a more comprehensive and systematic approach to forecast a medium and long-term carbon emission for provincial power grid. Thus, in this paper, life cycle assessment (LCA) method and gray prediction model are used to forecast the medium and long-term carbon emission. In addition, the carbon intensity of provincial power grid is evaluated based on historical data such as electricity consumption, gross domestic product (GDP), carbon emission coefficient. Taking Jiangxi power grid as the example, the results indicate that the proposed method is of good accuracy and the integration of renewable energy can effectively reduce the carbon emission and the carbon intensity.
Day-ahead forecasting of grid carbon intensity in support of HVAC plant demand response decision-making to reduce carbon emissions
Electrical HVAC loads in buildings are suitable candidates for use in demand response activity. This paper demonstrates a method to support planned demand response actions intended explicitly to reduce carbon emissions. Demand response is conventionally adopted to aid the operation of electricity grids and can lead to greater efficiency; here it is planned to target times of day when electricity is generated with high carbon intensity. Operators of HVAC plant and occupants of conditioned spaces can plan when to arrange shutdown of plant once they can foresee the opportune time of day for carbon saving. It is shown that the carbon intensity of the mainland UK electricity grid varies markedly throughout the day, but that this tends to follow daily and weekly seasonal patterns. To enable planning of demand response, 24-hour ahead forecast models of grid carbon intensity are developed that are not dependent on collecting multiple exogenous data sets. In forecasting half-hour periods of high carbon intensity either linear autoregressive or non-linear ANN models can be used, but a daily seasonal autoregressive model is shown to provide a 20% improvement in carbon reduction. Practical application The forecast method demonstrated in the paper would enable building operators to plan demand response activity to target times of high carbon intensity on the UK electricity grid. The method would be easy to implement as the only data required is publicly available.
Numerical tools applied to power reactor noise analysis
In order to be able to calculate the space- and frequency-dependent neutron noise in real inhomogeneous systems in two-group theory, a code was developed for the calculation of the Green's function (dynamic transfer function) of such systems. This paper reports on the development as well as the test and application of the numerical tools employed. The code that was developed yields the space-dependence of the fluctuations of the neutron flux induced by fluctuating properties of the medium in the two-group diffusion approximation and in a two-dimensional representation of heterogeneous systems, for both critical systems and non-critical systems with an external source. Some applications of these tools to power reactor noise analysis are then described, including the unfolding of the parameters of the noise source from the induced neutron noise, measured at a few discrete locations throughout the core. Other concrete applications concern the study of the space-dependence of the Decay Ratio in Boiling Water Reactors, the noise-based estimation of the Moderator Temperature Coefficient of reactivity in Pressurized Water Reactors, the modeling of the beam- and shell-mode core-barrel vibrations in Pressurized Water Reactors, and the investigation of the validity of the point-kinetic approximation in subcritical systems driven by an external source. In most of these applications, calculations performed using the code are compared with at-power plant measurements. Power reactor noise analysis applications of the above type, i.e. core monitoring without disturbing plant operation, is of particular interest in the framework of the extensive program of power uprates worldwide.