Energy

Minimizing Cost Uncertainty with a New Methodology for Use in Policy Making: China's Electricity Pathways

Avrin, Anne-Perrine
Moura, Scott J.
Kammen, Daniel M.
2016

Planning the long-term expansion of a power sector requires anticipating future technologies, fuel costs, and new carbon policies. Many state-of-the-art models rely on exogenous data for cost and performance projections where the inherent uncertainty is either ignored or addressed only with sensitivity analysis and scenarios. For the few models accounting for uncertainty, the transition from the research field to policy making has not occurred because of important practical barriers in the latter field: higher reliance on time-tested models, impossibility to constantly adopt new models,...

Optimal Component Sizing in a Two-Reservoir Passive Energy Harvesting System

Munsing, E.
Cowell, M.
Moura, S.
Wright, P.
2016

We utilize particle swarm optimization to reduce the size of the energy management components in an energy harvesting system, allowing us to eliminate the need for voltage regulators or DC-DC converters without affecting system performance. Prior literature on optimal power management in microelectronics [1, 2] has relied on engineering estimates or exhaustive parameter searches to optimize system design. No prior literature has considered the optimal design of a device with only passive components [3]. By using particle swarm optimization, we demonstrate a 55% reduction in device size...

Stochastic Control of Smart Home Energy Management with Plug-in Electric Vehicle Battery Energy Storage and Photovoltaic Array

Wu, Xiaohua
Hu, Xiaosong
Moura, Scott
Yin, Xiaofeng
Pickert, Volker
2016

Energy management strategies are instrumental in the performance and economy of smart homes integrating renewable energy and energy storage. This article focuses on stochastic energy management of a smart home with PEV (plug-in electric vehicle) energy storage and photovoltaic (PV) array. It is motivated by the challenges associated with sustainable energy supplies and the local energy storage opportunity provided by vehicle electrification. This paper seeks to minimize a consumer's energy charges under a time-of-use tariff, while satisfying home power demand and PEV charging requirements...

Recursive Parameter Estimation of Thermostatically Controlled Loads via Unscented Kalman Filter

Burger, Eric M.
Moura, Scott J.
2016

For thermostatically controlled loads (TCLs) to perform demand response services in real-time markets, online methods for parameter estimation are needed. As the physical characteristics of a TCL change (e.g. the contents of a refrigerator or the occupancy of a conditioned room), it is necessary to update the parameters of the TCL model. Otherwise, the TCL will be incapable of accurately predicting its potential energy demand, thereby decreasing the reliability of a TCL aggregation to perform demand response. In this paper, we investigate the potential of various unscented Kalman filter (...

Generation Following with Thermostatically Controlled Loads via Alternating Direction Method of Multipliers Sharing Algorithm

Burger, Eric M.
Moura, Scott J.
2017
A fundamental requirement of the electric power system is to maintain a continuous and instantaneous balance between generation and load. The intermittency and uncertainty introduced by renewable energy generation requires expanded ancillary services to maintain this balance. In this paper, we examine the potential of thermostatically controlled loads (TCLs), such as refrigerators and electric water heaters, to provide generation following services in real-time energy markets (1–5 min). Previous research in this area has primarily focused on the development of centralized control schemes with...

Hybrid Electrochemical Modeling with Recurrent Neural Networks for Li-ion Batteries

Park, Saehong
Zhang, Dong
Moura, Scott
2017

This paper examines a hybrid battery system modeling framework, where data-oriented recurrent neural network (RNN) and first-principle electrochemical battery model are combined. The data-driven RNN model captures unmodeled dynamics in the electrochemical model. We specifically study a simple RNN model called an Elman network, which has feedback loops in the hidden layer. We analyze and prove convergence of the weight errors for a class of Elman networks and learning update laws. In simulation, we compare our proposed hybrid battery model with reduced electrochemical battery models....

Optimal Charging of Li-ion Batteries via a Single Particle Model with Electrolyte and Thermal Dynamics

Perez, H. E.
Dey, S.
Hu, X.
Moura, S. J.
2017

This article seeks to derive insight on battery charging control using electrochemistry models. Directly using full order complex multi-partial differential equation (PDE) electrochemical battery models is difficult and sometimes impossible to implement. This article develops an approach for obtaining optimal charge control schemes, while ensuring safety through constraint satisfaction. An optimal charge control problem is mathematically formulated via a coupled reduced order electrochemical-thermal model which conserves key electrochemical and thermal state information. The Legendre...

State-of-Charge Estimation for Lithium-Ion Batteries Based on a Nonlinear Fractional Model

Wang, Baojin
Liu, Zhiyuan
Li, Shengbo Eben
Moura, Scott Jason
Peng, Huei
2017

This paper presents a new battery state-of-charge (SOC) estimation method for lithium-ion batteries (LIBs) based on a nonlinear fractional model with incommensurate differentiation orders. A continuous frequency distributed model is used to describe the incommensurate fractional system. A Luenberger-type observer is designed for battery SOCestimation. The observer gain that can stabilize the zero equilibrium of the estimation error system is derived by Lyapunov's direct method. The proposed SOC observer is examined using the real-time experimental data of LIBs. The robustness of the...

A Second Order Cone Programming Model for PEV Fast-Charging Station Planning

Zhang, Hongcai
Moura, Scott
Hu, Zechun
Qi, Wei
Song, Yonghua
2017

This paper studies siting and sizing of plug-in electric vehicle (PEV) fast-charging stations on coupled transportation and power networks. We develop a closed-form service rate model of highway PEV charging stations' service abilities, which considers heterogeneous PEV driving ranges and charging demands.We utilize a modified capacitated flow refueling location model (CFRLM) to explicitly capture time-varying PEV charging demands on the transportation network under driving range constraints. We explore extra constraints of the CFRLM to enhance model accuracy and computational efficiency....

Battery State Estimation for a Single Particle Model With Electrolyte Dynamics

Moura, Scott J.
Argomedo, Federico Bribiesca
Klein, Reinhardt
Mirtabatabaei, Anahita
Krstic, Miroslav
2017

This paper studies a state estimation scheme for a reduced electrochemical battery model, using voltage and current measurements. Real-time electrochemical state information enables high-fidelity monitoring and high-performance operation in advanced battery management systems, for applications such as consumer electronics, electrified vehicles, and grid energy storage. This paper derives a single particle model (SPM) with electrolyte that achieves higher predictive accuracy than the SPM. Next, we propose an estimation scheme and prove estimation error system stability, assuming that the...