An algorithm and modeling are developed to make precise planning of year-round solar energy (SE) collection, storage, and redistribution to meet a decided demand of electrical power fully relying on solar energy. The model takes the past 10 years’ data of average and worst-case sky coverage (clouds fraction) condition of a location at a time interval (window) of per 6 min in every day to predict solar energy and electrical energy harvest. The electrical energy obtained from solar energy in sunny times must meet the instantaneous energy demand and also the need for energy storage for nighttime and overcast days, so that no single day will have a shortage of energy supply in the entire year and yearly cycles. The analysis can eventually determine a best starting date of operation, a least solar collection area, and a least energy storage capacity for cost-effectiveness of the system. The algorithm provides a fundamental tool for the design of a general renewable energy harvest and storage system for non-interrupted year-round power supply. As an example, the algorithm was applied for the authors’ local city, Tucson, Arizona of the U.S. for a steady power supply of 1 MW.