9 research outputs found
Distributional Analysis for Model Predictive Deferrable Load Control
Deferrable load control is essential for handling the uncertainties
associated with the increasing penetration of renewable generation. Model
predictive control has emerged as an effective approach for deferrable load
control, and has received considerable attention. In particular, previous work
has analyzed the average-case performance of model predictive deferrable load
control. However, to this point, distributional analysis of model predictive
deferrable load control has been elusive. In this paper, we prove strong
concentration results on the distribution of the load variance obtained by
model predictive deferrable load control. These concentration results highlight
that the typical performance of model predictive deferrable load control is
tightly concentrated around the average-case performance.Comment: 12 pages, technical report for CDC 201
Distributed Optimal Vehicle Grid Integration Strategy with User Behavior Prediction
With the increasing of electric vehicle (EV) adoption in recent years, the
impact of EV charging activities to the power grid becomes more and more
significant. In this article, an optimal scheduling algorithm which combines
smart EV charging and V2G gird service is developed to integrate EVs into power
grid as distributed energy resources, with improved system cost performance.
Specifically, an optimization problem is formulated and solved at each EV
charging station according to control signal from aggregated control center and
user charging behavior prediction by mean estimation and linear regression. The
control center collects distributed optimization results and updates the
control signal, periodically. The iteration continues until it converges to
optimal scheduling. Experimental result shows this algorithm helps fill the
valley and shave the peak in electric load profiles within a microgrid, while
the energy demand of individual driver can be satisfied.Comment: IEEE PES General Meeting 201
Minimal-Variance Distributed Deadline Scheduling in a Stationary Environment
Many modern schedulers can dynamically adjust their service capacity to match the incoming workload. At the same time, however, variability in service capacity often incurs operational and infrastructure costs. In this paper, we propose distributed algorithms that minimize service capacity variability when scheduling jobs with deadlines. Specifically, we show that Exact Scheduling minimizes service capacity variance subject to strict demand and deadline requirements under stationary Poisson arrivals. We also characterize the optimal distributed policies for more general settings with soft demand requirements, soft deadline requirements, or both. Additionally, we show how close the performance of the optimal distributed policy is to that of the optimal centralized policy by deriving a competitive-ratio-like bound
Minimal-Variance Distributed Deadline Scheduling in a Stationary Environment
Many modern schedulers can dynamically adjust their service capacity to match the incoming workload. At the same time, however, variability in service capacity often incurs operational and infrastructure costs. In this paper, we propose distributed algorithms that minimize service capacity variability when scheduling jobs with deadlines. Specifically, we show that Exact Scheduling minimizes service capacity variance subject to strict demand and deadline requirements under stationary Poisson arrivals. We also characterize the optimal distributed policies for more general settings with soft demand requirements, soft deadline requirements, or both. Additionally, we show how close the performance of the optimal distributed policy is to that of the optimal centralized policy by deriving a competitive-ratio-like bound
Generalized Exact Scheduling: a Minimal-Variance Distributed Deadline Scheduler
Many modern schedulers can dynamically adjust their service capacity to match the incoming workload. At the same time, however, unpredictability and instability in service capacity often incur operational and infrastructure costs. In this paper, we seek to characterize optimal distributed algorithms that maximize the predictability, stability, or both when scheduling jobs with deadlines. Specifically, we show that Exact Scheduling minimizes both the stationary mean and variance of the service capacity subject to strict demand and deadline requirements. For more general settings, we characterize the minimal-variance distributed policies with soft demand requirements, soft deadline requirements, or both. The performance of the optimal distributed policies is compared to that of the optimal centralized policy by deriving closed-form bounds and by testing centralized and distributed algorithms using real data from the Caltech electrical vehicle charging facility and many pieces of synthetic data from different arrival distribution. Moreover, we derive the Pareto-optimality condition for distributed policies that balance the variance and mean square of the service capacity. Finally, we discuss a scalable partially-centralized algorithm that uses centralized information to boost performance and a method to deal with missing information on service requirements