Recent ecal_rw items

Blockchains for Decentralized Optimization of Energy Resources in Microgrid Networks

Tue, 14 Mar 2017 00:00:00 +0000

We present an architecture for peer-to-peer energy markets which can guarantee that operational constraints are respected and payments are fairly rendered, without relying on a centralized utility or microgrid aggregator. We demonstrate how to address trust, security, and transparency issues by using blockchains and smart contracts, two emerging technologies which can facilitate decentralized coordination between non-trusting agents. While blockchains are receiving considerable interest as a platform for distributed computation and data management, this is the first work to examine their use to facilitate distributed optimization and control. Using the Alternating Direction Method of Multipliers (ADMM), we pose a decentralized optimal power flow (OPF) model for scheduling a mix of batteries, shapable loads, and deferrable loads on an electricity distribution network. The DERs perform local optimization steps, and a smart contract on the...

Thermal Fault Diagnostics in Lithium-ion Batteries based on a Distributed Parameter Thermal Model

Sun, 30 Oct 2016 00:00:00 +0000

Lithium-ion (Li-ion) battery faults or failure mechanisms are potentially hazardous to battery health, safety and performance. Thermal fault mechanisms represent a critical subset of such failures. To ensure safety and reliability, battery management systems must have the capability of diagnosing these thermal failures. In line with this requirement, we present a Partial Differential Equation (PDE) model-based scheme for diagnosing thermal faults in Li-ion batteries. For this study, we adopt a distributed parameter one-dimensional thermal model for cylindrical battery cells. The diagnostic scheme objective is to detect and estimate the size of the thermal fault. The scheme consists of two PDE observers arranged in cascade with measured surface temperature feedback. The first observer, denoted as Robust Observer, estimates the distributed temperature inside the cell under nominal (healthy) and faulty conditions. The second observer, denoted as Diagnostic Observer, receives this...

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

Tue, 4 Oct 2016 00:00:00 +0000

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 january 2013. The second phase uses exact optimization methods, while the third uses metaheuristics methods.

Battery State Estimation for a Single Particle Model with Electrolyte Dynamics

Sun, 8 May 2016 00:00:00 +0000

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 with electrolyte (SPMe) that achieves higher predictive accuracy than the single particle model (SPM). Next, we propose an estimation scheme and prove estimation error system stability, assuming the total amount of lithium in the cell is known. The state estimation scheme exploits dynamical properties such as marginal stability, local invertibility, and conservation of lithium. Simulations demonstrate the algorithm's performance and limitations.

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

Thu, 28 Jan 2016 00:00:00 +0000

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 develop convex techniques for the approximate optimization of energy systems. In this manuscript, we develop the alternative control trajectory representation -- a novel approach for representing the control of a non-convex discrete system as a convex program. The resulting convex program provides a solution that can be interpreted stochastically for implementation.

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

Thu, 28 Jan 2016 00:00:00 +0000

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 estimating the parameters. Finally, we fit the piecewise linear model to data collected from a residential building with a forced-air heating and ventilation system and validate the accuracy of the trained model.

Building Electricity Load Forecasting via Stacking Ensemble Learning Method with Moving Horizon Optimization

Thu, 3 Dec 2015 00:00:00 +0000

The short-term 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 demand-side electricity 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 electricity forecasting across geographic locations, seasons, and use-types. This paper addresses the need for a generalizable approach to electricity demand forecasting through the formulation of a stacking ensemble learning method. Rather than using a single model to predict electricity demand, our method uses a weighted linear combination of forecasts from multiple sub-models. By learning the model weights in real-time using electricity demand data streams...

Model Predictive Control of Residential Baseboard Heaters with Distributed System Architecture

Thu, 3 Dec 2015 00:00:00 +0000

Typical residential HVAC systems employ mechanical or hard-coded deadband control behaviors that are unresponsive to changing energy costs and weather conditions. In this paper, we investigate the potential of electric baseboard heaters to maintain a comfortable temperature while optimizing electricity consumption given weather forecasts and price data. We first propose a distributed system architecture that utilizes mobile application platforms. We then develop, assemble, and deploy a sensor network and Internet server to collect real-time temperature data from an apartment. With these sensor streams, we identify a thermal model of the apartment. Finally, we propose a model predictive control algorithm and perform a software-in-the-loop simulation of the cloud-based system to demonstrate the economic advantage.

Recursive Parameter Estimation of Thermostatically Controlled Loads via Unscented Kalman Filter

Mon, 30 Nov 2015 00:00:00 +0000

For thermostatically controlled loads (TCLs) to perform demand response services in real-time markets, recursive 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 an unscented Kalman filter (UKF) algorithm to identify a TCL model that is non-linear in the parameters. Experimental results demonstrate the parameter estimation of two residential refrigerators.

Weak Variations Optimal Boundary Control of Hyperbolic PDEs with Application to Traffic Flow and Delay Systems

Wed, 11 Nov 2015 00:00:00 +0000

We investigate optimal boundary control of firstorder hyperbolic PDEs. These equations are ubiquitous in engineered systems, such as traffic flows, fluid flows, heat exchangers, chemical reactors, and oil production systems. We derive linear quadratic regulator (LQR) results using a weak variations approach, recently developed for parabolic PDEs. The distinguishing characteristic of this approach is that it provides a systematic procedure for deriving LQR control laws without semi-group theoretic concepts. Ultimately, these control laws are given by the solution of an associated Riccati PDE. We demonstrate the applicability of these results on two case studies: traffic flow control and input-delayed systems. Finally, we extend the LQR results to solve the output reference tracking problem. Unlike motion planning, these reference tracking equations do not require state trajectory generation.

Nonlinear Predictive Energy Management of Residential Buildings with Photovoltaics & Batteries

Wed, 11 Nov 2015 00:00:00 +0000

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 demonstrate that the proposed control scheme achieves 96%-98% of the optimal performance given perfect forecasts over a long-term horizon. Moreover, the rate of battery capacity loss can be reduced by 25% with negligible losses in economic performance, through an appropriate cost function formulation.

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

Tue, 6 Oct 2015 00:00:00 +0000

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 the expansion of ancillary power system services to maintain such a 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 to 5 minutes). To simulate the non-linear dynamics of hysteretic dead-band systems in a manner suitable for convex optimization, we introduce an alternative control trajectory representation of the TCLs and their discrete input signals. To perform distributed optimization across large populations of TCLs, we propose a variation of the alternating direction method of multipliers (ADMM) algorithm. Based on our simulation results, we have demonstrated the potential for controlling a...