Fanxin Kong

A Survey on Geographic Load Balancing Based Data Center Power Management in the Smart Grid Environment

Power management is becoming an increasingly important issue for Internet services supported by multiple geo-distributed data centers. These data centers energy consumptions and costs are becoming unacceptably high, and placing a heavy burden on both energy resources and the environment. Emerging smart grid provides a feasible way for dynamic and efficient power management of data centers. Various power management methodologies based on geographic load balancing (GLB) have recently been proposed to effectively utilize several features of smart grid. In this paper, we summarize the motivations, current state of the art, approaches and techniques proposed in the recent research works in this discipline. In all of these works, many perspectives of power management have been addressed using various computer science principles. We specifically elaborate on how researchers are exploiting mathematical tools to address these perspectives. Finally, we point out subject matters that need more attentions from the research community and provide our vision on possible future works along this direction.

Smart Charging for Electric Vehicles: A Survey From the Algorithmic Perspective

Smart interactions among the smart grid, aggregators, and EVs can bring various benefits to all parties involved, e.g., improved reliability and safety for the smart gird, increased profits for the aggregators, as well as enhanced self benefit for EV customers. This survey focuses on viewing this smart interactions from an algorithmic perspective. In particular, important dominating factors for coordinated charging from three different perspectives are studied, in terms of smart grid oriented, aggregator-oriented, and customer-oriented smart charging. Firstly, for smart grid oriented EV charging, we summarize various formulations proposed for load flattening, frequency regulation, and voltage regulation, then explore the nature and substantial similarity among them. Secondly, for aggregator-oriented EV charging, we categorize the algorithmic approaches proposed by research works sharing this perspective as direct and indirect coordinated control, and investigate these approaches in detail. Thirdly, for customer-oriented EV charging, based on a commonly shared objective of reducing charging cost, we generalize different formulations proposed by studied research works. Moreover, various uncertainty issues, e.g., EV fleet uncertainty, electricity price uncertainty, regulation demand uncertainty, etc., have been discussed according to the three perspectives classified. At last, we discuss challenging issues that are commonly confronted during modeling the smart interactions, and outline some future research topics in this exciting area.

Quantity Versus Quality: Optimal Harvesting Wind Power for the Smart Grid

The need to reduce greenhouse gases from our current power systems accelerates the integration of renewable energy sources (for example, wind and solar power). A fundamental difficulty is that renewable energy is usually of high variability. Numerous advancements in technologies and methods for the smart grid are required to mitigate and absorb this variability. In this paper, we focus on one of them: how to plan wind farms with high capacity and low variability locally and distributedly. First, we study the characteristics of both wind resource and wind turbines and propose a novel wind power estimation method based on Gaussian regression. The experimental result shows that our method achieves a more accurate estimation compared to other ones and has a nearly zero error for most of the turbine types. Then, we analyze a tradeoff between wind powers quantity and quality for large-scale wind farms, and find that there is an optimal turbine type for each location as to either the quantity or the quality. We propose an approach to optimally combine different types of wind turbines to balance the tradeoff. Finally, we explore geographical diversity among different locations and develop an extended approach that jointly optimizes the combination of locations and turbine types. Besides applying to plan new wind farms, we also discuss how to adapt the two approaches to decide an upgrade plan for a wind farm and a network of wind farms, respectively. We conduct extensive experiments using two different wind resource data traces for both local and distributed cases. The result shows that the proposed approaches significantly outperform those approaches using a single turbine type and those separately optimizing locations and turbine types. We also provide interesting insights about the quantity-quality balancing.

Green power analysis for Geographical Load Balancing based datacenters

Variability and intermittency of green power is the main obstacle for its utilization. Different from other power consumption, due to the distributed nature, load balancing on geographical range can be used to dispatch computing tasks to the data centers with abundant renewable energy. The premise of this new strategy is: there is always abundant green power at some of the renewable power portfolio, yet this is not always the truth. The stable availability of renewable energy is built on the compensation of different power plants, but due to the constraint of constructed data centers and the on-site powering strategy, this compensation effect has not been fully explored. In this paper, we propose a solution for Renewable Energy Portfolio Optimization (REPO) problem, and take wind farm location selection as an example to stabilize the variable and intermittent wind power. The simulation is conducted based on the real-world climatic traces from 607 candidate wind farms. The optimal renewable energy portfolio can provide stable wind power supply at the price of 70 USD/MWh. When simulated with Google workload trace of May 2011, with installed capacity 4 times of average power demand, REPO can save 59.5% of energy while a combination (on Google data center locations) without consideration of mutual compensation could only save 30%.

GreenPlanning: optimal energy source selection and capacity planning for green datacenters

Cloud service providers such as Microsoft and Google are beginning to power up their datacenters using multiple energy sources. To reduce cost and emission, they incorporate green energy sources into the power supply, while to improve service availability, they back up datacenters using traditional (usually brown) energy sources. However, challenge arises due to distinct characteristics of energy sources used for different goals. How to select optimal energy sources and plan their capacity for constructing datacenters to meet cost, emission and service availability requirement remains to be fully explored. This work provides a holistic solution to address this problem. We present GreenPlanning, a framework to strike a judicious balance among multiple energy sources, grid power and energy storage devices for a datacenter in terms of the above three goals. GreenPlanning investigates different features and operations of a wide spectrum of green and brown energy sources available to datacenters. The framework minimizes the lifetime total cost including both capital and operational cost for a datacenter. We conduct extensive simulations to evaluate GreenPlanning with real-life computational workload and meteorological data traces. Results demonstrate that GreenPlanning can reduce the lifetime total cost and emission by more than 50\% compared to traditional configurations, while still satisfying service availability requirement.

Smart rate control and demand balancing for electric vehicle charging

The anticipated high electric vehicle (EV) penetration motivates many research efforts to alleviate the potential associated grid impact. However, few works discuss the crucial issue: quality of service (QoS) degradation caused by competing for charging resources. This issue arises due to the limitation on power supply and charging space that charging stations can usually provide. Our work studies this issue and proposes an operational scheme that optimizes QoS for EV users while satisfying the stability of the power grid. The scheme consists of two levels. The lower level deals with charging rate control, for which we propose an efficient algorithm with provable QoS-optimal allocation of power supply to EVs. The upper level handles charging demand balancing, for which we design two approximation algorithms that schedule EVs to multiple charging stations. One algorithm is a 3-approximation with polynomial complexity; while the other is a (2+ε)-approximation using a fully polynomial time approximation scheme. Through extensive simulations based on realistic data traces and simulations tools, we demonstrate the efficiency and efficacy of our operational scheme and further provide interesting findings from in-depth analysis of the experimental results.

Distributed Deadline and Renewable Aware Electric Vehicle Demand Response in the Smart Grid

Demand response is an important feature and functionality of the future smart grid. Electric vehicles are recognized as a particularly promising resource for demand response given their high charging demand and flexibility in demand management. Recently, researchers begun to apply market-based solutions to electric vehicle demand response. A clear vision, however, remains elusive because existing works overlook three key issues. (i) The hierarchy among electric vehicles (EVs), charging stations, and electric power companies (EPCs). Previous works assume direct interaction between EVs and EPCs and thus confine to single-level market designs. The designed mechanisms are inapplicable here due to ignoring the role of charging stations in the hierarchy. (ii) Temporal aspects of charging loads. Solely focusing on economic aspects makes significant demand reduction, but electric vehicles would end up with little allocated power due to overlooking their temporal constraints. (iii) Renewable generation co-located with charging stations. Market mechanisms that overlook the uncertainty of renewable would cause much inefficiency in terms of both the economic and temporal aspects. To address these issues, we study a new demand response scheme, i.e, hierarchical demand response for electric vehicles via charging stations. We propose that two-level marketing is suitable to this hierarchical scheme, and design a distributed market mechanism that is compatible with both the economic and temporal aspects of electric vehicle demand response. The market mechanism has a hierarchical decision-making structure by which the charging station leads the market and electric vehicles follow and respond to its actions. An appealing feature of the mechanism is the provable convergence to a unique equilibrium solution. At the equilibrium, neither the charging station or electric vehicles can improve their individual economic and/or temporal performance by changing their own strategies. Furthermore, we present a stochastic optimization based algorithm to optimize economic performance for the charging station at the equilibrium, given the predictions of the co-located renewable generation. The algorithm has provable robust performance guarantee in terms of the variance of the prediction errors. We finally evaluate the designed mechanism via detailed simulations. The results show the efficacy and validate the theoretical analysis for the mechanism.

Optimal energy source selection and capacity planning for green datacenters

Cloud service providers such as Microsoft and Google are beginning to power up their datacenters using multiple energy sources. To reduce cost and emission, they incorporate green energy sources into the power supply, while to improve service availability, they back up datacenters using traditional (usually brown) energy sources. However, challenge arises due to distinct characteristics of energy sources used for different goals. How to select optimal energy sources and plan their capacity for constructing datacenters to meet cost, emission and service availability requirement remains to be fully explored. This work provides a holistic solution to address this problem. We present GreenPlanning, a framework to strike a judicious balance among multiple energy sources, grid power and energy storage devices for a datacenter in terms of the above three goals. GreenPlanning investigates different features and operations of a wide spectrum of green and brown energy sources available to datacenters. The framework minimizes the lifetime total cost including both capital and operational cost for a datacenter. We conduct extensive simulations to evaluate GreenPlanning with real-life computational workload and meteorological data traces. Results demonstrate that GreenPlanning can reduce the lifetime total cost and emission by more than 50% compared to traditional configurations, while still satisfying service availability requirement.

Blowing hard is not all we want: Quantity vs quality of wind power in the smart grid

The growing awareness about global climate change has boosted the need to mitigate greenhouse gas emissions from existing power systems and spurred efforts to accelerate the integration of renewable energy sources (e.g. wind and solar power) into the electrical grid. A fundamental difficulty here is that renewable energy sources are usually of high variability. The electrical grid must absorb this variability through employing many additional operations (e.g., operating reserves, energy storage), which will largely raise the cost of electricity from renewable energy sources. To make it affordable, numerous advancements in technologies and methods for the smart grid are required. In this paper, we will confine ourselves to one of them: how to plan the construction of wind farms with high capacity and low variability locally and distributedly. We first study the characteristics of both wind resources and wind turbines and present a more accurate wind power evaluation method based on Gaussian Regression. Then, we analyze a trade-off between wind powers quantity and quality and propose an approach to optimally combine different types of wind turbines to balance the trade-off for a specific site. Finally, we explore geographical diversity among different sites and develop an extended approach that jointly optimizes the combination of sites and turbine types. Extensive experiments using the realistic historical wind resource data are conducted for either of the local and distributed case. Encouraging results are shown for the proposed approaches and some interesting insights are also provided.

Auc2Charge: An Online Auction Framework for Eectric Vehicle Park-and-Charge

The increasing market share of electric vehicles (EVs) makes large-scale charging stations indispensable infrastructure for integrating EVs into the future smart grid. Thus their operation modes have drawn great attention from researchers. One promising mode called park-and-charge was recently proposed. It allows people to park their EVs at a parking lot, where EVs can get charged during the parking time. This mode has been experimented and demonstrated in small scale. However, the missing of an efficient market mechanism is an important gap preventing its large-scale deployment. Existing pricing policies, e.g., pay-by-use and flat-rate pricing, would jeopardize the efficiency of electricity allocation and the corresponding social welfare in the park-and-charge mode, and thus are inapplicable. To find an efficient mechanism, this paper explores the feasibility and benefits of utilizing auction mechanism in the EV park-and-charge mode. The auction allows EV users to submit and update bids on their charging demand to the charging station, which makes corresponding electricity allocation and pricing decisions. To this end, we propose Auc2Charge, an online auction framework. Auc2Charge is truthful and individual rational. Running in polynomial time, it provides an efficient electricity allocation for EV users with a close-form approximation ratio on system social welfare. Through both theoretical analysis and numerical simulation, we demonstrate the efficacy of Auc2Charge in terms of social welfare and user satisfaction.