Variability of Photovoltaic Power in the State of Gujarat Using High Resolution Solar Data [report]

M. Hummon, J. Cochran, A. Weekley, A. Lopez, J. Zhang, B. Stoltenberg, B. Parsons, P. Batra, B. Mehta, D. Patel
2014 unpublished
India has ambitious goals for high utilization of variable renewable power from wind and solar, and deployment has been proceeding at a rapid pace. The western state of Gujarat currently has the largest amount of solar capacity of any Indian state, with over 855 megawatts direct current (MW DC ) 1 among plants above 1 MW DC in size. Combined with over 3,240 megawatts (MW) of wind, variable generation renewables comprise nearly 18% of the electric-generating capacity in the state. The Central
more » ... ctricity Authority has projected these wind and solar capacities will more than double by 2017. 2 With high penetration levels of wind and solar, system operators must have access to additional resources that can help balance the net-load variability (load minus wind and solar output) and carry adequate reserves to respond to the combination of load and variable generation forecast errors. To assess the adequacy of balancing resources, and to evaluate operational practices to access these resources, system operators and planners typically perform grid integration studies. Key to informative analysis is accurate representationspatially and temporallyof the power variability and uncertainty of solar and wind generation. This report focuses on the solar characteristics needed for a grid integration analysis, which would also include information on load, wind, and conventional generation. A new historic 10-kilometer (km) gridded solar radiation data set capturing hourly insolation values for 2002-2011 is available for India. 3 The authors apply an established method for downscaling hourly irradiance data to one-minute irradiance data at each photovoltaic (PV) power production location for one year-2006. 4 Using this data, the authors quantify solar production at locations reflecting six scenarios: baseline, totaling 1.9 gigawatts direct current [GW DC ]) and five possible expansion scenarios, each adding 500-1,000 MW of solar capacity, to yield a total installed solar capacity of 2.4 GW DC and 2.9 GW DC . The scenarios are: 1. Baseline: existing and planned solar generation, totaling 1.9 GW DC 2. Charanka: expansion at an existing, single solar park; represents the most geographically concentrated scenario, 1.0 GW DC 3. Seven utility PV locations: expansion at the seven best, developable sites, distributed throughout the state, 1.0 GW DC 4. Kutchh: expansion in the Kutchh region of Gujarat, distributed across the region, 1.0 GW DC 1 Direct current (DC) ratings refer to the capacity of the photovoltaic panels under standard conditions: 1,000 watts/square meter, 25°C. Alternate current (AC) ratings refer to the peak power output of the inverter, which includes system losses and DC to AC conversion losses. 4 A single year of sub-hour data captures the seasonal and time of day variability; however multiple study years would be needed to understand the long-term economics of a PV plant at a particular location. vi This report is available at no cost from the National Renewable Energy Laboratory (NREL) at 5. Narmada Canal: expansion above the canal, evenly distributed across its length 0.5 GW DC 6. Sixteen Cities rooftop PV: expansion across rooftops in the 16 largest cities, evenly distributed across the cities; represents the most geographically diverse scenario, 1.0 GW DC . Three of the expansion scenarios (Charanka, Kutchh, and Narmada Canal) were suggested by the Gujarat Energy Transmission Corporation (GETCO). The other two scenarios were selected to reflect broader geographic diversity. The objective of this report is to characterize and contrast the intra-hour variability of these six PV generation scenarios. The report statistically analyzes one year's worth of power variability data, applied to both the baseline and expansion scenarios, to evaluate diurnal and seasonal power fluctuations, different timescales of variability (e.g., from one to 15 minutes), the magnitude of variability (both total megawatts and relative to installed solar capacity), and the extent to which the variability can be anticipated in advance. The paper also examines how GETCO and the Gujarat State Load Dispatch Centre (SLDC) could make use of the solar variability profiles in operations and planning. One factor inherent to grid balancing challenges associated with increased solar deployment is the ramp rate, which is the sustained rate of power increase or decrease over time. 5 Solar power ramps result from both the daily solar path and cloud patterns that decrease the incident solar radiation on the PV panels. Quantifying total MW per minute ramp rates allows system operators and planners to assess balancing options. This analysis quantifies the relatively simple concept that the total magnitude of solar power ramping goes up with increased solar capacity. Simply put, total ramping in the baseline scenario of 1.9 GW DC is less than total ramping in the baseline plus expansion scenario of 2.4 GW DC , which is less than the four other baseline plus expansion scenarios totaling 2.9 GW DC . The dominant cause of this correlation is ramping due to sunrise and sunset, which occurs over a short, predictable period of time. During the monsoon season, clouds reduce the sunrise and sunset ramp rates, and decrease the peak solar output by 20-35%. Figure ES -1, the aggregate power output from the baseline plus each expansion scenario in monsoon (left) and dry (right) seasons, illustrates these results.
doi:10.2172/1126817 fatcat:mo3yktczkjd7tnvlz3awcdjzem