Microgrids represent an emerging paradigm of future electric power systems that can utilize both distributed and centralized generations. Two recent trends in microgrids are the integration of local renewable energy sources (such as wind farms) and the use of co-generation (i.e., to supply both electricity and heat). However, these trends also bring unprecedented challenges to the design of intelligent control strategies for microgrids. Traditional generation scheduling paradigms rely on perfect prediction of future electricity supply and demand. They are no longer applicable to microgrids with unpredictable renewable energy supply and with co-generation (that needs to consider both electricity and heat demand). In this paper, we study online algorithms for the microgrid generation scheduling problem with intermittent renewable energy sources and co-generation, with the goal of maximizing the cost-savings with local generation. Based on the insights from the structure of the offline optimal solution, we propose a class of competitive online algorithms, called CHASE (Competitive Heuristic Algorithm for Scheduling Energy-generation), that track the offline optimal in an online fashion. Under typical settings, we show that CHASE achieves the best competitive ratio among all deterministic online algorithms, and the ratio is no larger than a small constant 3. We also extend our algorithms to intelligently leverage on limited prediction of the future, such as near-term demand or wind forecast. By extensive empirical evaluations using real-world traces, we show that our proposed algorithms can achieve near offline-optimal performance. In a representative scenario, CHASE leads to around * The first two authors are in alphabetical order.20% cost reduction with no future look-ahead, and the cost reduction increases with the future look-ahead window.

Although using look-ahead information is known to improve the competitive ratios of online convex optimization (OCO) problems with switching costs, the competitive ratios obtained from existing results often depend on the cost coefficients of the problem, and can potentially be large. In this paper, we propose new online algorithms that can utilize look-ahead to achieve much lower competitive ratios for OCO problems with switching costs and hard constraints. For the perfect look-ahead case where the algorithm is provided with the exact inputs in a future look-ahead window of size K, we design an Averaging Regularized Moving Horizon Control (ARMHC) algorithm that can achieve a competitive ratio of K +1 K. To the best of our knowledge, ARMHC is the first to attain a low competitive ratio that is independent of either the coefficients of the switching costs and service costs, or the upper and lower bounds of the inputs. Then, for the case when the future look-ahead has errors, we develop a Weighting Regularized Moving Horizon Control (WRMHC) algorithm that carefully weights the decisions inside the look-ahead window based on the accuracy of the look-ahead information. As a result, WRMHC also achieves a low competitive ratio that is independent of the cost coefficients, even with uncertain hard constraints. Finally, our analysis extends online primal-dual analysis to the case with look-ahead by introducing a novel "re-stitching" idea, which is of independent interest. CCS Concepts: • Theory of computation → Online algorithms; • Mathematics of computing → Convex optimization; • Networks → Network algorithms.

This paper studies seeded graph matching for power-law graphs. Assume that two edge-correlated graphs are independently edge-sampled from a common parent graph with a power-law degree distribution. A set of correctly matched vertex-pairs is chosen at random and revealed as initial seeds. Our goal is to use the seeds to recover the remaining latent vertex correspondence between the two graphs. Departing from the existing approaches that focus on the use of high-degree seeds in $1$-hop neighborhoods, we develop an efficient algorithm that exploits the low-degree seeds in suitably-defined D-hop neighborhoods. Specifically, we first match a set of vertex-pairs with appropriate degrees (which we refer to as the first slice) based on the number of low-degree seeds in their D-hop neighborhoods. This approach significantly reduces the number of initial seeds needed to trigger a cascading process to match the rest of graphs. Under the Chung-Lu random graph model with n vertices, max degree Θ(√n), and the power-law exponent 2<β<3, we show that as soon as D> 4-β/3-β, by optimally choosing the first slice, with high probability our algorithm can correctly match a constant fraction of the true pairs without any error, provided with only Ω((log n)4-β) initial seeds. Our result achieves an exponential reduction in the seed size requirement, as the best previously known result requires n1/2+ε seeds (for any small constant ε>0). Performance evaluation with synthetic and real data further corroborates the improved performance of our algorithm.

In this talk we study the issue of cross-layer design for rate control in multihop wireless networks. We have developed an optimal cross-layered rate control scheme that jointly computes both the rate allocation and the stabilizing schedule that controls the resources at the underlying layers. However, the scheduling component in this optimal cross-layered rate control scheme has to solve a complex global optimization problem at each time, and is hence too computationally expensive for online implementation. Thus, we study the impact on the performance of cross-layer rate control if the network can only use an imperfect (and potentially distributed) scheduling component that is easier to implement. We study scenarios with both fixed number of users as well as when the number of users change due to arrivals and departures in the system. In each case, we establish desirable results on the performance bounds of cross-layered rate control with imperfect scheduling. Our cross-layered approach provides provably better performance bounds when compared with a layered approach (that does not design rate control and scheduling together). The insights drawn from our analyses also enable us to design a fully distributed cross-layered rate control and scheduling algorithm under a restrictive interference model.