Discovering an active subspace in a single-diode solar cell model

Predictions from science and engineering models depend on the values of the model's input parameters. As the number of parameters increases, algorithmic parameter studies like optimization or uncertainty quantification require many more model evaluations. One way to combat this curse of dimensionality is to seek an alternative parameterization with fewer variables that produces comparable predictions. The active subspace is a low-dimensional linear subspace defined by important directions in the model's input space; input perturbations along these directions change the model's prediction more, on average, than perturbations orthogonal to the important directions. We describe a method for checking if a model admits an exploitable active subspace, and we apply this method to a single-diode solar cell model with five input parameters. We find that the maximum power of the solar cell has a dominant one-dimensional active subspace, which enables us to perform thorough parameter studies in one dimension instead of five

Smart Metering System: Developing New Designs to Improve Privacy and Functionality

This PhD project aims to develop a novel smart metering system that plays a dual role: Fulfil basic functions (metering, billing, management of demand for energy in grids) and protect households from privacy intrusions whilst enabling them a degree of freedom. The first two chapters of the thesis will introduce the research background and a detailed literature review on state-of-the-art works for protecting smart meter data. Chapter 3 discusses theory foundations for smart meter data analytics, including machine learning, deep learning, and information theory foundations. The rest of the thesis is split into two parts, ‘Privacy’ and ‘Functionality’, respectively. In the ‘Privacy’ part, the overall smart metering system, as well as privacy configurations, are presented. A threat/adversary model is developed at first. Then a multi-channel smart metering system is designed to reduce the privacy risks of the adversary. Each channel of the system is responsible for one functionality by transmitting different granular smart meter data. In addition, the privacy boundary of the smart meter data in the proposed system is also discovered by introducing a data mining algorithm. By employing the algorithm, a three-level privacy boundary is concluded. Furthermore, a differentially private federated learning-based value-added service platform is designed to provide flexible privacy guarantees to consumers and balance the trade-off between privacy loss and service accuracy. In the ‘Functionality’ part, three feeder-level functionalities: load forecasting, solar energy separation, and energy disaggregation are evaluated. These functionalities will increase thepredictability, visibility, and controllability of the distributed network without utilizing household smart meter data. Finally, the thesis will conclude and summarize the overall system and highlight the contributions and novelties of this project

Bidirectional irradiance transposition based on the Perez model

The Perez irradiance model offers a practical representation of solar irradiance by considering the sky hemisphere as a three-part geometrical framework, namely, the circumsolar disc, the horizon band and the isotropic background. Furthermore, the simplified Perez diffuse irradiance model, commonly known as the Perez transposition model, is one of the most widely adopted models in tilted irradiance modeling. Although the set of model coefficients reported by Perez et al. (1990) is considered to be at an asymptotic level of optimization, later analyses have shown that coefficients which are adjusted to local conditions may perform better than the original set.<p></p> The model coefficients can be adjusted locally based on multiple datasets of diffuse and global irradiance on tilted and horizontal planes. In this paper, we present a different approach to adjust the coefficients, by using only measurements of global irradiance on tilted and horizontal planes from a tropical climate site, Singapore. A complete set of mathematical solutions to the inverse problem, i.e., irradiance transposition from tilt to horizontal, is also proposed. The data can then be used to generate irradiance maps from in-plane irradiance measurements at photovoltaics (PV) systems. Such maps provide relevant information for PV grid integration.<p></p&gt

Turbofan forced mixer-nozzle internal flowfield. Volume 3: A computer code for 3-D mixing in axisymmetric nozzles

A finite difference method is developed for making detailed predictions of three dimensional subsonic turbulent flow in turbofan lobe mixers. The governing equations are solved by a forward-marching solution procedure which corrects an inviscid potential flow solution for viscous and thermal effects, secondary flows, total pressure distortion and losses, internal flow blockage and pressure drop. Test calculations for a turbulent coaxial jet flow verify that the turbulence model performs satisfactorily for this relatively simple flow. Lobe mixer flows are presented for two geometries typical of current mixer design. These calculations included both hot and cold flow conditions, and both matched and mismatched Mach number and total pressure in the fan and turbine streams

Control and Communication Protocols that Enable Smart Building Microgrids

Recent communication, computation, and technology advances coupled with climate change concerns have transformed the near future prospects of electricity transmission, and, more notably, distribution systems and microgrids. Distributed resources (wind and solar generation, combined heat and power) and flexible loads (storage, computing, EV, HVAC) make it imperative to increase investment and improve operational efficiency. Commercial and residential buildings, being the largest energy consumption group among flexible loads in microgrids, have the largest potential and flexibility to provide demand side management. Recent advances in networked systems and the anticipated breakthroughs of the Internet of Things will enable significant advances in demand response capabilities of intelligent load network of power-consuming devices such as HVAC components, water heaters, and buildings. In this paper, a new operating framework, called packetized direct load control (PDLC), is proposed based on the notion of quantization of energy demand. This control protocol is built on top of two communication protocols that carry either complete or binary information regarding the operation status of the appliances. We discuss the optimal demand side operation for both protocols and analytically derive the performance differences between the protocols. We propose an optimal reservation strategy for traditional and renewable energy for the PDLC in both day-ahead and real time markets. In the end we discuss the fundamental trade-off between achieving controllability and endowing flexibility

Operational solar forecasting for the real-time market

Despite the significant progress made in solar forecasting over the last decade, most of the proposed models cannot be readily used by independent system operators (ISOs). This article proposes an operational solar forecasting algorithm that is closely aligned with the real-time market (RTM) forecasting requirements of the California ISO (CAISO). The algorithm first uses the North American Mesoscale (NAM) forecast system to generate hourly forecasts for a 5-h period that are issued 12 h before the actual operating hour, satisfying the lead-time requirement. Subsequently, the world's fastest similarity search algorithm is adopted to downscale the hourly forecasts generated by NAM to a 15-min resolution, satisfying the forecast-resolution requirement. The 5-h-ahead forecasts are repeated every hour, following the actual rolling update rate of CAISO. Both deterministic and probabilistic forecasts generated using the proposed algorithm are empirically evaluated over a period of 2 years at 7 locations in 5 climate zones