Juan Luis Bosch

A solar cooling facility with two forms of energy storage, electrochemical (batteries) and thermal (tanks containing the heat transfer fluid, HTF), is simulated. The technical specifications used in the simulation are taken from a recently installed system in an institutional building at the University of Almería (Spain). Electricity generated by photovoltaic (PV) panels is either stored in a battery bank, or supplied directly to the chiller, which is also connected to the grid as a backup. The HTF circulates through a storage tank and is driven to a heat exchanger to cover the refrigeration demand. The whole system is modeled in TRNSYS with the corresponding meteorological data: the PV array has a peak power of 23.4 kW, the compression chiller power is 70 kWt, and the building demands of refrigeration from high and low season amount to 413.5 kWh/day and 62.8 kWh/day, respectively. The battery bank capacity is 40.8 kWh and the tank has a volume of 4000 L. Different configurations of energy storage (only electrical, only thermal, and hybrid) are tested for both demands. The results show that the refrigeration needs can be covered with solar energy and storage in the low season, with a surplus that can be driven to the building. In the high demand season, an extra input from the grid is needed as the current facility covers 89.3% of the total electricity requirements per day (54.5% if no storage is used). Finally, the system performance is evaluated over an intermediate-demand month. Overall, it is found that electrical consumption from the grid is minimal when batteries are used, either alone or in conjunction with thermal storage.

Large ramps and ramp rates in photovoltaic (PV) power output are of concern and sometimes even explicitly restricted by grid operators. Battery energy storage systems can smooth the power output and maintain ramp rates within permissible limits. To enable PV plant and energy storage system design and planning, a method to estimate the largest expected ramps for a given location is proposed. Because clouds are the dominant source of PV power output variability, an analytical relationship between the worst expected ramp rate, cloud motion vector, and the geometrical layout of the PV plant is developed. The ability of the proposed method to bracket actual ramp rates is assessed over 10 months under different meteorological conditions, demonstrating an average compliance rate of 98.9% for a 2 min evaluation time window. The largest observed ramp of 29.7% s⁻¹ is contained with the worst case estimate of 34.3% s⁻¹. This method provides a convenient yet economical approach to worst-case PV ramp rate modeling and is compatible with solar irradiance measured at coarse temporal resolution.

Large PV power ramp rates are of concern and sometimes even explicitly restricted by grid operators. Battery energy storage systems can smooth the power output and maintain ramp rates within permissible limits. To enable PV plant and energy storage systems design and planning, a method to estimate the largest expected ramps for a given location is proposed. Because clouds are the dominant source of PV power output variability, an analytical relationship between the worst expected ramp rates, cloud motion vectors, and the geometrical layout of the PV plant is developed. The ability of the proposed method to bracket actual ramp rates is assessed over 8 months under different meteorological conditions, demonstrating an average compliance rate of 96.9% for a 2 min evaluation time window.

Optical losses in central receiver systems may be enhanced by the atmospheric attenuation taking place along the optical path between the heliostat mirrors and the receiver. Ray-tracing and performance codes usually estimate the atmospheric attenuation by a third order polynomial whose coefficients can be input by the user in the model. A sensitivity study on the time-resolution for modelling the atmospheric attenuation is presented in this work by modelling two reference solar tower plants (Ivanpah 1 and Crescent Dunes) with the System Advisor Model (SAM). The input for the atmospheric attenuation has been computed from daily, monthly means and annual mean aerosol optical depth for the Tamanrasset site. The impact of considering one unique polynomial for the whole year to one polynomial every day is higher in the case of Crescent Dunes due to its larger solar field, and relative averages differences in the daily output power may be from around 2% to 4.5% due to the daily peaks of AOD that result in eventual high attenuating conditions for the solar field.

Visibility is traditionally needed for air quality monitoring or air traffic control, and has become a key input to determine the transmission losses...