A truthful incentive mechanism for emergency demand response in colocation data centers

Data centers are key participants in demand response programs, including emergency demand response (EDR), where the grid coordinates large electricity consumers for demand reduction in emergency situations to prevent major economic losses. While existing literature concentrates on owner-operated data centers, this work studies EDR in multi-tenant colocation data centers where servers are owned and managed by individual tenants. EDR in colocation data centers is significantly more challenging, due to lack of incentives to reduce energy consumption by tenants who control their servers and are typically on fixed power contracts with the colocation operator. Consequently, to achieve demand reduction goals set by the EDR program, the operator has to rely on the highly expensive and/or environmentally-unfriendly on-site energy backup/generation. To reduce cost and environmental impact, an efficient incentive mechanism is therefore in need, motivating tenants’ voluntary energy reduction in case of EDR. This work proposes a novel incentive mechanism, Truth-DR, which leverages a reverse auction to provide monetary remuneration to tenants according to their agreed energy reduction. Truth-DR is computationally efficient, truthful, and achieves 2-approximation in colocation-wide social cost. Trace-driven simulations verify the efficacy of the proposed auction mechanism.published_or_final_versio

Greening Multi-Tenant Data Center Demand Response

Data centers have emerged as promising resources for demand response, particularly for emergency demand response (EDR), which saves the power grid from incurring blackouts during emergency situations. However, currently, data centers typically participate in EDR by turning on backup (diesel) generators, which is both expensive and environmentally unfriendly. In this paper, we focus on "greening" demand response in multi-tenant data centers, i.e., colocation data centers, by designing a pricing mechanism through which the data center operator can efficiently extract load reductions from tenants during emergency periods to fulfill energy reduction requirement for EDR. In particular, we propose a pricing mechanism for both mandatory and voluntary EDR programs, ColoEDR, that is based on parameterized supply function bidding and provides provably near-optimal efficiency guarantees, both when tenants are price-taking and when they are price-anticipating. In addition to analytic results, we extend the literature on supply function mechanism design, and evaluate ColoEDR using trace-based simulation studies. These validate the efficiency analysis and conclude that the pricing mechanism is both beneficial to the environment and to the data center operator (by decreasing the need for backup diesel generation), while also aiding tenants (by providing payments for load reductions).Comment: 34 pages, 6 figure

SLA Impact Modeling for Service Engagement

Abstract-During the customer engagement phase it is critical for the service providers to estimate the impact of service level constraints on service personnel needs. However, it is often difficult due to the implication from customer workload. In this paper we propose an SLA impact evaluation methodology that uses queueing models to quantitatively evaluate the impact of SLAs to the engagement cost model

A Novel Energy Model for Renewable Energy-Enabled Cellular Networks Providing Ancillary Services to the Smart Grid

In this paper, we consider cellular networks powered by the smart grid (SG) and by local renewable energy (RE) sources. While this configuration promises energy savings, usage of cleaner energy, and cost reduction, it has some intrinsic complexity due to the interaction between the network operators and the SG. Motivated by the significant advancement in the SG, we consider the case where cellular networks provide the SG with ancillary services by replying to the grid's explicit requests to increase or decrease their grid consumption. We propose a new approach for configuring and operating base stations (BSs) to provide ancillary services. Based on real data, we model the energy state of a BS as a Markov chain taking into account the proposed energy management policy, randomness of SG requests, and RE generation. We use the model to evaluate the performance of the system, and to decide proper settings of its parameters in order to minimize the energy operational cost. The performance of our proposal is then compared against those of other approaches. Results show that important cost savings, with negligible degradation in quality of service, are possible when RE generation, SG patterns, and storage sizes are properly taken into account

Energy Portfolio Optimization of Data Centers

Data centers have diverse options to procure electricity. However, the current literature on exploiting these options is very fractured. Specifically, it is still not clear how utilizing one energy option may affect selecting other energy options. To address this open problem, we propose a unified energy portfolio optimization framework that takes into consideration a broad range of energy choices for data centers. Despite the complexity and nonlinearity of the original models, the proposed analysis boils down to solving tractable linear mixed-integer stochastic programs. Using experimental electricity market and Internet workload data, various insightful numerical observations are reported. It is shown that the key to link different energy options with different short- and long-term profit characteristics is to conduct risk management at different time horizons. Also, there is a direct relationship between data centers' service-level agreement parameters and their ability to exploit certain energy options. The use of on-site storage and the deployment of geographical workload distribution can particularly help data centers in utilizing high-risk energy choices, such as offering ancillary services or participating in wholesale electricity markets

Sim2Win: How simulation can help data centers to benefit from controlling their power profile

To support the grid and integrate renewables, demand response schemes reward the power flexibility of energy consumers. Data centers can profit from this by using power management techniques on all levels of data center architecture: infrastructure, hardware, workload, applications. Even though lately, demand response with data centers has been well researched, most works focus on just one or two techniques and one or two valorization options. This leaves data centers stranded that are not represented by the specific combinations of assumptions and techniques presented in research, and thus a huge potential remains barely touched. To address this challenge, the goal of the presented work is to provide data centers with a framework that can be flexibly instantiated by each data center to assess its individual demand response potential. To achieve this goal, this work presents Sim2Win, a data center simulation framework that can replay any set of different power management strategies in the face of any set of markets for power flexibility. A part of the framework is then instantiated and applied to the workload of a real high-performance data center. It uses workload shifting and frequency scaling in order to market their flexibility on the EPEX spot market and the secondary reserve market in Germany. The results show that by using the inherent flexibility of their power profile on the EPEX spot market the considered data center in 2014 could have earned savings of 7.3% of their power bill

Advanced Optimization and Data-Driven Control in Smart Grid

The power grids are continuously evolving over the past decades, where new challenges and opportunities are embraced at the same time. On one hand, the penetration of renewable generations and other distributed energy resources (DER) is growing rapidly, whose different generation and control patterns could significantly impact the daily operation. On the other hand, the new communication, monitoring and regulating devices are gradually installed, which enable more control abilities of the generations, demands, and grids, and the feasibility to deploy more sophisticated control schemes.To leverage the new technique and overcome the new challenges in the smart girds, different optimization and control problems need to be solved for different roles including the system operator, demand, and financial traders. For the system operators, it is critical to maximizing the total social welfare while satisfying the operational constraints. To better coordinate the DER and improve the efficiency of distribution systems, the three-phase optimal power flow (OPF) problem algorithms are developed including the DCOPF algorithm for robustness and the ACOPF algorithm for optimality. Moreover, the deep reinforcement learning-based Volt-VAR control schemes are proposed to better maintain the voltage stability and electricity service quality.For demands resources, minimizing their energy bills will satisfy the energy needs is always their goal. Providing ancillary services by proactively adjusting their total demand is one of the potential choices. Through the provision of the services, the demands can not only receiving incentives from the system operators but also help to improve the reliability and stability of power grids. We develop control schemes specifically for the data centers to provide the phase balancing service in the distribution system and the frequency regulation service in the transmission system. The financial traders, it is desired to maximize their total profits. A better trading strategy with a more accurate forecast model can help increase the traders' gain and further improve the price convergence of the electricity market. Our machine learning based trading framework outperforms the existing approach and lays the foundation for market efficiency evaluation across the markets