Di Wang

Multi-Objective Optimization to Minimize Battery Degradation and Electricity Cost for Demand Response in Datacenters

This paper presents a Lithium-ion battery control framework to achieve minimum health degradation and electricity cost when batteries are used for datacenter demand response (DR). Demand response in datacenters refers to the adjustment of demand for grid electricity to minimize electricity cost. Utilizing batteries for demand response will reduce the electricity cost but might accelerate health degradation. This tradeoff makes battery control for demand response a multi-objective optimization problem. Current research focuses only on minimizing the cost of demand response and does not capture battery transient and degradation dynamics. We address this multi-objective optimization problem using a second-order equivalent circuit model and an empirical capacity fade model of Lithium-ion batteries. To the best of our knowledge, this is the first study to use a nonlinear Lithium-ion battery and health degradation model for health-aware optimal control in the context of datacenters. The optimization problem is solved using a differential evolution (DE) algorithm and repeated for different battery pack sizes. Simulation results furnish a Pareto front that makes it possible to examine tradeoffs between the two optimization objectives and size the battery pack accordingly.Copyright

Battery health-conscious online power management for stochastic datacenter demand response

This paper presents a stochastic control framework for optimizing datacenter power management. The paper focuses on datacenters employing lithium-ion batteries for demand response. The use of batteries for demand response can reduce electricity costs, at the expense of battery degradation. We minimize this degradation using a control policy that takes into account uncertainties in power demand. We perform this optimization using a second-order model of battery charge dynamics, coupled with a physics-based model of battery aging via solid-electrolyte interphase (SEI) growth. To the best of our knowledge, this is the first study that uses battery models capturing diffusion dynamics and nonlinear aging effects, together with a model of demand uncertainty, for datacenter energy management. We formulate this as a stochastic dynamic programming (SDP) problem, where uncertain power demand is modeled as a Markov chain. The resulting control policy keeps grid power within a predefined range while minimizing battery degradation.

A Stochastic Optimal Control Approach for Exploring Tradeoffs between Cost Savings and Battery Aging in Datacenter Demand Response

This brief paper optimizes power management for datacenters employing lithium-ion battery storage, with the specific goal of addressing the tradeoff between: 1) the cost saving achievable through the peak demand shaving and 2) the corresponding battery aging. To the best of the authors’ knowledge, this tradeoff has never been addressed using physics-based models of battery performance and degradation combined with stochastic models of datacenter demand. We build: 1) a Markov chain model of datacenter power demand; 2) a second-order model of battery diffusion/reaction dynamics; and 3) a physics-based model of battery aging via solid electrolyte interphase growth. Together, these models enable the solution of the battery health-conscious demand response problem via stochastic dynamic programming (SDP). A penalty function is used for enforcing a datacenter “power cap” within this SDP problem. By varying this power cap, we traverse the Pareto tradeoff between the cost savings due to demand response and battery health degradation.

Evaluating energy storage for a multitude of uses in the datacenter

Datacenters often are a power utilitys largest consumers, and are expected to participate in several power management scenarios with diverse characteristics in which Energy Storage Devices (ESDs) are expected to play important roles. Different ESD technologies exist, including little explored technologies such as flow batteries, that offer different performance characteristics in cost, size, and environmental impact. While prior works in datacenter ESD literature have considered one of usage aspect, technology, performance metric (typically cost), the whole three-dimensional space is little explored. Towards understanding this design space, this paper presents first such study towards joint characterization of ESD usages based on their provisioning and operating demands, under ideal and realistic ESD technologies, and quantify their impact on datacenter performance. We expect our work can help datacenter operators to characterize this three-dimensional space in a systematic manner, and make design decisions targeted towards cost-effective and environmental impact aware datacenter energy management.