Potential for virtual energy storage in a wind-PV-hydropower system in Yalong River Basin, China.

Wind and hydropower are generally more prevalent in stormy weather conditions when solar radiation is relatively lower, which is why these renewables show complimentary characteristics over time. Similarly, weather patterns show spatial covariance. This means that spatio-temporal coordination of renewable energy production can reduce significantly the variance in the system power, hence, contribute to a virtual energy storage similar as has previously been done by matching the demand response to power availability. The 130,000 km² Yalong River Basin in southwest China is used as an example in this study and for this basin we found typical climate-controlled periods in the renewable energy variations on periods of half a year, one year and 11-years. Based on historical hydro-climatic records, results for a planned combined wind-PV-hydropower system show that the maximum virtual energy storage has similar trends under different periods, i.e. it decreases with coordination distance and stabilizes on a coordination range of between 200 – 400 km. The maximum virtual energy storage gain was found to be 737 MWh . The project developed an existing spectral method for the analysis of the variance of the potential power and virtual energy storage in combined wind-PV-hydropower systems under different climate periods. Two different scenarios were analyzed, one in which all power stations were matched regardless of transmission constraints and one in which coordination of PV and wind power is fully centralized around single hydropower stations. The virtual energy storage gain obtained at decadal long periods, such as the 11-year cycle, can also be seen as an alternative to reserve power capacity that is activated only to avoid energy droughts. This study focused on the theoretical maximum potential for virtual energy storage, but the feasibility of this potential is limited by the uncertainty associated with production optimization and the meteorologic forecasts of future energy availability.