This work studies electricity markets between power grids and microgrids, an emerging paradigm of electric power generation and supply. It is among the first that addresses the economic challenges arising from such grid integration, and represents the first power auction mechanism design that explicitly handles the Unit Commitment Problem (UCP), a key challenge in power grid optimization previously investigated only for centralized cooperative algorithms. The proposed solution leverages a recent result in theoretical computer science that can decompose an optimal fractional (infeasible) solution to NP-hard problems into a convex combination of integral (feasible) solutions. The end result includes randomized power auctions that are (approximately) truthful and computationally efficient, and achieve small approximation ratios for grid-wide social welfare under UCP constraints and temporal demand correlations. Both power markets with grid-to-microgrid and microgrid-to-grid energy sales are studied, with an auction designed for each, under the same randomized power auction framework. Trace driven simulations are conducted to verify the efficacy of the two proposed inter-grid power auctions.

This work studies the online electricity cost minimization problem at a co-location data center. A co-location data center serves multiple tenants who rent the physical infrastructure within the data center to run their respective cloud computing services. Consequently, the co-location operator has no direct control over power consumption of its tenants, and an efficient mechanism is desired for eliciting desirable consumption patterns from the co-location tenants. Electricity billing faced by a data center is nowadays based on both the total volume consumed and the peak consumption rate. This leads to an interesting new combinatorial optimization structure on the electricity cost optimization problem, which also exhibits an online nature due to the definition of peak consumption. We model and solve the problem through two approaches: the pricing approach and the auction approach. For the former, we design an offline 2-approximation algorithm as well as an online algorithm with a small competitive ratio in most practical settings. For the latter, we design an efficient (2+ c )-competitive online algorithm, where c is a system dependent parameter close to 1.49, and then convert it into an efficient mechanism that executes in an online fashion, runs in polynomial time, and guarantees truthful bidding and (2+2 c )-competitive in social cost.