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Gated Ensemble Learning Method for Demand-Side Electricity Load Forecasting

Burger, Eric M.
Moura, Scott J.
2015

The forecasting of building electricity demand is certain to play a vital role in the future power grid. Given the deployment of intermittent renewable energy sources and the ever increasing consumption of electricity, the generation of accurate building-level electricity demand forecasts will be valuable to both grid operators and building energy management systems. The literature is rich with forecasting models for individual buildings. However, an ongoing challenge is the development of a broadly applicable method for demand forecasting across geographic locations, seasons, and use-...

Alternative Control Trajectory Representation for the Approximate Convex Optimization of Non-Convex Discrete Energy Systems

Burger, Eric M.
Moura, Scott J.
2016

Energy systems (e.g. ventilation fans, refrigerators, and electrical vehicle chargers) often have binary or discrete states due to hardware limitations and efficiency characteristics. Typically, such systems have additional programmatic constraints, such as minimum dwell times to prevent short cycling. As a result, non-convex techniques, like dynamic programming, are generally required for optimization. Recognizing developments in the field of distributed convex optimization and the potential for energy systems to participate in ancillary power system services, it is advantageous to...

Piecewise Linear Thermal Model and Recursive Parameter Estimation of a Residential Heating System

Burger, Eric M.
Perez, Hector E.
Moura, Scott J.
2016

Model predictive control (MPC) strategies show great potential for improving the performance and energy efficiency of building heating, ventilation, and air-conditioning (HVAC) systems. A challenge in the deployment of such predictive thermostatic control systems is the need to learn accurate models for the thermal characteristics of individual buildings. This necessitates the development of online and data-driven methods for system identification. In this paper, we propose a piecewise linear thermal model of a building. To learn the model, we present a Kalman filter based approach for...

Optimal Charging of Electric Vehicles for Load Shaping: A Dual-Splitting Framework With Explicit Convergence Bounds

Le Floch, Caroline
Belletti, Francois
Moura, Scott
2016

This paper proposes a tailored distributed optimal charging algorithm for plug-in electric vehicles (PEVs). If controlled properly, large PEV populations can enable high penetration of renewables by balancing loads with intermittent generation. The algorithmic challenges include scalability, computation, uncertainty, and constraints on driver mobility and power-system congestion. This paper addresses computation and communication challenges via a scalable distributed optimal charging algorithm. Specifically, we exploit the mathematical structure of the aggregated charging problem to...

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

Perez, H. E.
Hu, X.
Moura, S. J.
2016

This paper 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 paper 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-Gauss-...

Optimal Routing and Charging of Electric Ride-Pooling Vehicles in Urban Networks

Nicolas, Léa
Moura, Scott J.
2016

In this project, we study an Electric Vehicle Routing Problem with Pick-ups and Deliveries, Time Windows, and Recharging Stations on New York City Taxicab data. In order to solve this problem, we divide the problems into three phases: (i) grouping similar customer requests by identifying geographic zones and time slots; (ii) determine groups of passengers to be transported together; (iii) complete the vehicle itinerary between these groups of passengers. The first phase uses the clustering method k-means  on the locations of pick-ups and deliveries of New York City taxicabs in...

Coordination of V2G and Distributed Wind Power Using the Storage-like Aggregate PEV Model

Zhang, Hongcai
Hu, Zechun
Song, Yonghua
Moura, Scott
2016

A plug-in electric vehicle (PEV) fleet utilizing vehicle-to-grid (V2G) technology, i.e., a V2G fleet, can behave as a storage system, e.g., promoting integration of distributed wind power resources. However, because the PEVs' behaviors are stochastic and a V2G fleet's population is large, three technical difficulties hinder the utilization of V2G: charging demand forecasting; ahead-of-time charge and discharge scheduling; real-time charge and discharge power dispatching. This paper utilizes a storage-like aggregate model (SLAM) of a V2G fleet that employs aggregated parameters to represent...

Nonlinear Predictive Energy Management of Residential Buildings with Photovoltaics & Batteries

Sun, Chao
Sun, Fengchun
Moura, Scott J.
2016

This paper studies a nonlinear predictive energy management strategy for a residential building with a rooftop photovoltaic (PV) system and second-life lithium-ion battery energy storage. A key novelty of this manuscript is closing the gap between building energy management formulations, advanced load forecasting techniques, and nonlinear battery/PV models. Additionally, we focus on the fundamental trade-off between lithium-ion battery aging and economic performance in energy management. The energy management problem is formulated as a model predictive controller (MPC). Simulation results...

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...