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As solar electricity has become cheaper than the retail electricity price, residential consumers are trying to reduce costs by meeting more demand using solar energy. One way to achieve this is to invest in the solar infrastructure collaboratively. When houses form a coalition, houses with high solar potential or surplus roof capacity can install more panels and share the generated solar energy with others, lowering the total cost. Fair sharing of the resulting cost savings across the houses is crucial to prevent the coalition from breaking. However, estimating the fair share of each house is complex as houses contribute different amounts of generation and demand in the coalition, and rooftop solar generation across houses with similar roof capacities can vary widely. In this paper, we present HeliosFair, a system that minimizes the total electricity costs of a community that shares solar energy and then uses Shapley values to fairly distribute the cost savings thus obtained. Using real-world data, we show that the joint CapEx and OpEx electricity costs of a community sharing solar can be reduced by 12.7% on average (11.3% on average with roof capacity constraints) over houses installing solar energy individually. Our Shapley-value-based approach can fairly distribute these savings across houses based on their contributions towards cost reduction, while commonly used ad hoc approaches are unfair under many scenarios. HeliosFair is also the first work to consider practical constraints such as the difference in solar potential across houses, rooftop capacity and weight of solar panels, making it deployable in practice. 

The widespread adoption and popularity of Internet-scale Distributed Networks (IDNs) has led to an explosive growth in the infrastructure of these networks. Unfortunately, this growth has also led to a rapid increase in energy consumption with its accompanying environmental impact. Therefore, energy efficiency is a key consideration in operating and designing these power-hungry networks. In this paper, we study the greening potential of combining two contrasting sources of renewable energy, namely solar energy and Open Air Cooling (OAC). OAC involves the use of outside air to cool data centers if the weather outside is cold and dry enough. Therefore OAC is likely to be abundant in colder weather and at night-time. In contrast, solar energy is correlated with sunny weather and day-time. Given their contrasting natures, we study whether synthesizing these two renewable sources of energy can yield complementary benefits. Given the intermittent nature of renewable energy, we use batteries and load shifting to facilitate the use of green energy and study trade-offs in brown energy reduction based on key parameters like battery size, number of solar panels, and radius of load movement. We do a detailed cost analysis, including amortized cost savings as well as a break-even analysis for different energy prices. Our results look encouraging and we find that we can significantly reduce brown energy consumption by about 55% to 59% just by combining the two technologies. We can increase our savings further to between 60% to 65% by adding load movement within a radius of 5000kms, and to between 73% to 89% by adding batteries.