The Cloud System Evolution in the Trades (CSET) study was designed to describe and explain the evolution of the boundary layer aerosol, cloud, and thermodynamic structures along trajectories within the North Pacific trade winds. The study centered on seven round trips of the National Science Foundation–National Center for Atmospheric Research (NSF–NCAR) Gulfstream V (GV) between Sacramento, California, and Kona, Hawaii, between 7 July and 9 August 2015. The CSET observing strategy was to sample aerosol, cloud, and boundary layer properties upwind from the transition zone over the North Pacific and to resample these areas two days later. Global Forecast System forecast trajectories were used to plan the outbound flight to Hawaii with updated forecast trajectories setting the return flight plan two days later. Two key elements of the CSET observing system were the newly developed High-Performance Instrumented Airborne Platform for Environmental Research (HIAPER) Cloud Radar (HCR) and the high-spectral-resolution lidar (HSRL). Together they provided unprecedented characterizations of aerosol, cloud, and precipitation structures that were combined with in situ measurements of aerosol, cloud, precipitation, and turbulence properties. The cloud systems sampled included solid stratocumulus infused with smoke from Canadian wildfires, mesoscale cloud–precipitation complexes, and patches of shallow cumuli in very clean environments. Ultraclean layers observed frequently near the top of the boundary layer were often associated with shallow, optically thin, layered veil clouds. The extensive aerosol, cloud, drizzle, and boundary layer sampling made over open areas of the northeast Pacific along 2-day trajectories during CSET will be an invaluable resource for modeling studies of boundary layer cloud system evolution and its governing physical processes.
Microgeneration technologies are positioned to address future building energy efficiency requirements and facilitate the integration of renewables into buildings to ensure a sustainable, energy-secure future. This paper explores the development of a robust multi-input multi-output (MIMO) controller applicable to the control of hybrid renewable microgeneration systems with the objective of minimising the electrical grid utilisation of a building while fulfilling the thermal demands. The controller employs the inverse dynamics of the building, servicing systems, and energy storage with a robust control methodology. These inverse dynamics provides the control system with knowledge of the complex cause and effect relationships between the system, the controlled inputs, and the external disturbances, while an outer-loop control ensures robust, stable control in the presence of modelling deficiencies/uncertainty and unknown disturbances. Variable structure control compensates for the physical limitations of the systems whereby the control strategy employed switches depending on the current utilisation and availability of the energy supplies. Preliminary results presented for a system consisting of a micro-CHP unit, solar PV, and battery storage indicate that the control strategy is effective in minimising the interaction with the local electrical network and maximising the utilisation of the available renewable energy
A predictive load shifting controller has been developed and deployed in a low-carbon house near Glasgow, UK. The house features an under floor heating system, fed by an air-source heat pump. Based on forecast air temperatures and solar radiation levels, the controller 1) predicts the following day’s heating requirements to achieve thermal comfort 2) runs heat pump during off peak periods to deliver the required heat by pre-charging the under floor heating. Prior to its installation in the building, the controller’s operating characteristics were identified using a calibrated building simulation model. The performance of the controller in the house was monitored over four weeks in 2015. The monitored data indicated that the actual thermal performance of the predictive controller was better than that projected using simulation, with better levels of thermal comfort achieved. Indoor air temperatures were between 18°C to 23°C for around 87% of the time between 07:00-22:00. However, the performance of the heat pump under load shift control was extremely poor, with the heat being delivered primarily by the unit’s auxiliary immersion coil. The paper concludes with a refined version of the controller that should improve the day-ahead energy predictions and offer greater flexibility in heat pump operation for future field trials
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