Saloranta T.M.,Ice Energy
Cryosphere | Year: 2012
Daily maps of snow conditions have been produced in Norway with the seNorge snow model since 2004. The seNorge snow model operates with 1 × 1 km resolution, uses gridded observations of daily temperature and precipitation as its input forcing, and simulates, among others, snow water equivalent (SWE), snow depth (SD), and the snow bulk density (ρ). In this paper the set of equations contained in the seNorge model code is described and a thorough spatiotemporal statistical evaluation of the model performance from 1957-2011 is made using the two major sets of extensive in situ snow measurements that exist for Norway. The evaluation results show that the seNorge model generally overestimates both SWE and ρ, and that the overestimation of SWE increases with elevation throughout the snow season. However, the R2-values for model fit are 0.60 for (log-transformed) SWE and 0.45 for ρ, indicating that after removal of the detected systematic model biases (e.g. by recalibrating the model or expressing snow conditions in relative units) the model performs rather well. The seNorge model provides a relatively simple, not very data-demanding, yet nonetheless process-based method to construct snow maps of high spatiotemporal resolution. It is an especially well suited alternative for operational snow mapping in regions with rugged topography and large spatiotemporal variability in snow conditions, as is the case in the mountainous Norway. © Author(s) 2012. CC Attribution 3.0 License. Source
Ice Energy | Date: 2012-03-29
Disclosed is a method and device for a refrigerant-based thermal energy storage and cooling system with integrated multi-mode refrigerant loops. The disclosed embodiments provide a refrigerant-based thermal storage system with increased versatility, reliability, lower cost components, reduced power consumption and ease of installation.
Ice Energy | Date: 2013-09-09
Disclosed is a system and method for providing power generation and distribution with on-site energy storage and power input controlled by a utility or a third party manager. The system allows a utility manager to decide and direct how energy is delivered to a customer on both sides of the power meter, while the customer directs and controls when and how much energy is needed. In the disclosed embodiments, the utility controls the supply (either transmitted or stored) and makes power decisions on a system that acts as a virtual power plant, while the end-user retains control of the on-site aggregated power consumption assets. The disclosed systems act to broker the needs of the utility and end-user by creating, managing and controlling the interface between these two entities.
Ice Energy | Date: 2012-05-25
Disclosed is a system and method for improving grid efficiency, reliability, security and capacity, utilizing energy storage over a plurality of on-site energy storing appliances, and also utilizing on-site demand reduction devices lacking storage, all controlled via configuration settings with a local means to act independently, yet in statistical coordination, to provide a desired effect. The appliance and controller are located on the downstream side of the end-users power meter, and facilitates the utilization of the stored energy and manages the optimal timing for producing and delivering the stored energy to the end-user. This model demonstrates a utility driven, disaggregated, distributed energy system, where the distributed energy resource is designed to behave as an offset to the predictable daily electrical demand profile.
Ice Energy | Date: 2010-11-19
Disclosed are a method and device for a refrigerant-based thermal storage system wherein a condensing unit and an ice-tank heat exchanger can be isolated through a second heat exchanger. The disclosed embodiments provide a refrigerant-based ice storage system with increased reliability, lower cost components, and reduced power consumption compared to non-isolated systems.