Annabelle Pratt

Evaluation of 400V DC distribution in telco and data centers to improve energy efficiency

In a typical data center, less than half the energy consumed is delivered to the compute load, with the rest lost in power conversion, distribution and cooling. Traditionally power distribution is at 400/480 V AC in data centers and at -48 V DC in telco facilities. Higher voltage DC has been proposed as an energy efficient distribution option for both types of facilities, and this paper presents an analytical evaluation of several data center power delivery architectures over a range of loads, showing that a 400 V facility-level DC distribution option is the most efficient. The equipment required to support such an architecture is discussed and results from a small scale demonstration are included.

Electric vehicle charge optimization including effects of lithium-ion battery degradation

This paper presents a method for minimizing the cost of electric vehicle (EV) charging given variable electricity costs while also accounting for estimated costs of battery degradation using a simplified lithium-ion battery lifetime model. The simple battery lifetime model, also developed and presented here, estimates both energy capacity fade and power fade due to temperature, state of charge profile, and daily depth of discharge. This model has been validated by comparison with a detailed model [6], which in turn has been validated through comparison to experimental data. The simple model runs quickly in a MATLAB script, allowing for iterative numerical minimization of charge cost. EV charge profiles optimized as described here show a compromise among four trends: charging during low-electricity cost intervals, charging slowly, charging towards the end of the available charge time, and suppression of vehicle-to-grid power exportation. Finally, simulations predict that batteries charged using optimized charging last longer than those charged using typical charging methods, potentially allowing smaller, cheaper batteries to meet vehicle lifetime requirements.

Modeling and Stochastic Control for Home Energy Management

The problem of modeling and stochastic optimization for home energy management is considered. Several different types of load classes are discussed, including heating, ventilation, and air conditioning unit, plug-in hybrid electric vehicle, and deferrable loads such as washer and dryer. A first-order thermal dynamic model is extracted and validated using real measurements collected over an eight months time span. A mixed integer multi-time scale stochastic optimization is formulated for the scheduling of loads of different characteristics. A model predictive control based heuristic is proposed. Numerical simulations coupled with real data measurements are used for performance evaluation and comparison studies.

Accounting for Lithium-Ion Battery Degradation in Electric Vehicle Charging Optimization

This paper presents a method for minimizing the cost of vehicle battery charging given variable electricity costs while also accounting for estimated costs of battery degradation using a simplified lithium-ion battery lifetime model. The simple battery lifetime model, also developed and presented here, estimates both energy capacity fade and power fade and includes effects due to temperature, state of charge profile, and daily depth of discharge. This model has been validated by comparison with a detailed model developed at National Renewable Energy Laboratory, which in turn has been validated through comparison with experimental data. The simple model runs quickly, allowing for iterative numerical minimization of charge cost, implemented on the charger controller. Resulting electric vehicle (EV) charge profiles show a compromise among four trends: 1) charging during low-electricity cost intervals; 2) charging slowly; 3) charging toward the end of the available charge time; and 4) suppression of vehicle-to-grid power exportation. Simulations based on experimental Prius plug-in hybrid EV usage data predict that batteries charged using optimized charging last significantly longer than those charged using typical charging methods, potentially allowing smaller batteries to meet vehicle lifetime requirements. These trends are shown to hold across a wide range of battery sizes and hence are applicable to both EVs and plug-in hybrid EVs.

Modeling and stochastic control for Home Energy Management

The problem of modeling and control for Home Energy Management (HEM) is considered. A first order thermal dynamic model is considered and its parameters are extracted using real measurements over a period of three summer months. The identified model is validated using separate data sets. The extracted model shows certain nonstationarity and non-Gaussianity. However, local approximations using a stationary model are shown to have relatively small modeling and prediction errors. The extracted model is then used for developing a multi-scale multi-stage stochastic optimization framework for the control of the Heating, Ventilation, and Air Conditioning (HVAC) unit, the charging of Plug-in Hybrid Electric Vehicle (PHEV), and the scheduling of deferrable load such as washer/dryer operations. A two time scale Model Predictive Control (MPC) strategy is proposed that minimizes the discomfort level subject to power and budget constraints: at the slow time scale, a power budget is allocated across different appliances at the hourly level; at the fast time scale, sensor measurements are used for the scheduling and control of different loads. Using parameters extracted from the real data, the proposed approach is compared with the simple rule based control strategy typically used in HVAC controllers.

Multi-scale stochastic optimization for Home Energy Management

The problem of scheduling and control of appliances for Home Energy Management (HEM) is considered. A multi-time scale and multi-stage stochastic optimization framework is proposed for the control of the Heating, Ventilation, and Air Conditioning (HVAC) unit, the charging of Plug-in Hybrid Electric Vehicle (PHEV), and the scheduling of deferrable load such as washer/dryer operations. Formulated as a constrained stochastic optimization that incorporates thermal dynamics, temperature measurements, and the real time pricing signal, a model predictive control algorithm is proposed that minimizes customers discomfort level subject to cost and peak power constraints.

Capacitor ripple current in an interleaved PFC converter

To achieve high-power density in power supplies, it is desirable to minimize the physical size of the energy storage capacitor. The capacitance is determined by the energy storage requirement for line outage ride-through and also the ripple current handling capability of the capacitor. Interleaving is well known as an effective method to reduce the capacitor ripple current and in cases where ripple current considerations dominate, it could reduce capacitor size. This paper presents a methodology to calculate the ripple current, both for single phase and for m interleaved phases of power factor correction converters operating with constant load or a DC-DC converter load. Experimental results from a commercial power supply yielded a small error when compared to the calculations, showing that the proposed methodology has enough accuracy to be used as a design tool.

A microgrid modeling and simulation platform for system evaluation on a range of time scales

This paper presents a simulation platform for the modeling and study of microgrid (MG) power systems. Using MathWorks Simulink modeling software, the platform provides a library of tools for designing and simulating the behavior of an MG on time scales from seconds to days. The library includes a collection of power system and power electronics components (sources, loads, switches, etc.) that may be arbitrarily configured. The platform also facilitates the study of energy management systems (EMS), which optimize the behavior of certain controllable sources and loads according to programmed algorithms. User-generated EMS routines can be integrated into an MG model.

Maximizing lithium ion vehicle battery life through optimized partial charging

The limited lifetime, high cost, and large size of current lithium ion batteries are some of the primary obstacles to wider adoption of electric vehicles and plug-in hybrid electric vehicles. Simulations presented in this paper predict that Li-ion battery life can be extended through intelligent charging, especially when predictions of next-day energy needs are used to charge the battery only as needed. As-needed charging minimizes battery degradation by minimizing time spent at high state-of-charge. Preliminary results presented here indicate that the battery of a vehicle used for daily commuting and short errands could see its useable life extended by up to 150 % over unoptimized charging.

Transactive Home Energy Management Systems: The Impact of Their Proliferation on the Electric Grid

Approximately 100 million singlefamily homes in the United States account for 36% of the electricity load, and often they determine the peak system load, especially on hot summer days when residential air-conditioning use is high. Traditional building power profiles are changing. Currently, there is an increased use of energy-efficient building materials and designs, which decreases building loads. In addition, there is an increased adoption of rooftop solar photovoltaic (PV), which leads to bidirectional power flow and significant power ramps as PV output decreases in the late afternoon. Building power profiles are likely to change even more as residential energy storage products proliferate. Therefore, a better understanding of residential electricity demand is key to addressing the envisioned transition of the electric power system from its traditional structure to one that is transactive.