SmartGrid

Electrical power distribution system of the future requires innovations. Some of its key technological and economic challenges lie in the attempt to: make electricity affordable throughout the world, allow local/distributed and time-varying generation, allow storage and load to be seamlessly integrated independent of utility, utilize modern power electronics, use networked distributed computing to provide the dynamic load-supply balance and stability, provide pay-as-you-go payment system for real-time market transactions.

I will present one perspective that touches upon different pieces of the overall solution stack: hardware design, safety, firmware, distributed intelligence, stability and control, economically optimized power distribution and trading mechanisms, systems architecture and communication, etc. Then, I will concentrate on our optimization framework for general AC/DC smart grids, based on the sequential convex programming, and its connection to the "faster" controls that ensure the grid's robustness to noise and unpredicted changes. This will include a discussion on distributed solutions that are based on the proximal message passing, and the properties of our implementation: physical stability, economic optimality of the resulting operating regime, robustness, and scalability with respect to the system size (number of devices attached to a grid), etc.

SmartGrid Seminar Winter 2016: Our speakers will discuss exciting new ideas and technologies that are changing the electricity industry. The theme of the seminar series is on smart grids and energy systems, with speakers from academic institutions and industry. The hour-long seminars, including ample time for discussion, are held at 1:00 pm or 1:15 pm approximately every Thursday.

Global CO2 levels have pierced the 400ppm threshold in 2015. As CO2 levels continue to rise human civilization will face increasing penalties. In fact, even at today's levels the globe is undergoing changes at an unprecedented rate. The electric and energy sectors are gigantic emitters of greenhouse gases (GHGs) and are an obvious target to reduce these GHGs. The main driver of emission reduction in the near future will be a transition of electricity from coal to a combination of wind, solar and natural gas. With these intermittent resources, can the grid be effective? To what extent do we know how the grid will operate with large amounts of variable generations? Are there steps we can take to better understand our options to reduce GHGs? The National Energy with Weather System (NEWS) Simulator was designed around incorporating high resolution (both spatially and temporally) of generators, transmission, weather and demand. The weather data is provided by NOAA and processed to give estimate of power for different technologies. With the NEWS simulator different scenarios can be performed to answer a wide variety of questions. In addition, it can be used to produce plans for expansion of the transmission and generators on the electric grid for utilities and ISOs. The NEWS simulator can also be run in dispatch mode to determine the most cost effective utilization of existing infrastructure in a market format. The present seminar will present details of the NEWS simulator, including the background to major concepts and details of datasets included. The seminar will also show results from several studies carried out within the NEWS framework to answer different questions and determine the robustness and sensitivity of the model.

Instantaneous balance between power generation and demand is a requirement for power grid stability. With increasing use of distributed and intermittent renewable energy sources such as wind and solar power, traditional control paradigm of power generation following demand has to be changed. Power demand must follow the dynamically changing power generation. Inspired by the Hooke's law for mechanical springs, the concept of "electric springs" was developed recently with an aim to ensure secure operation of future power grids. Electric springs are expected to facilitate integration of intermittent renewable energy sources which are interfaced to the power grid through power electronic converters. In the first part of this talk, Professor Ron Hui would introduce the basic concept of electric springs and discuss the motivation behind it. He would describe the proof-of-concept experiments carried out using bench-scale prototype of electric springs to show their potential. He would summarize the recent research activities, planned field trials and potential role of electric springs in smart grids. In the second part of this talk, Dr Balarko Chaudhuri would discuss the future challenges facing the system operators in Great Britain due to the anticipated reduction in system inertia and short circuit level. In this context, the role of electric springs in grid frequency regulation and distributed voltage control will be described at a system level. Two possible configurations of electric springs would be compared in terms of their effectiveness in dealing with under- and over-voltage/frequency problems. The talk will conclude by highlighting the technical challenges towards exploiting the full capability of electric springs which would require collaborative research between experts in control theory, power electronics and power systems.

As never before our modern society depends on a reliable supply of electricity, and for the vast majority of people that electricity comes from the large, interconnected grids that crisscross the world. The smart grid vision is to utilize modern technology to transform the grid, to provide the sustainable electric infrastructure needed to provide us with a bright future. However, there is the potential for what the North America Electric Reliability Corporation (NERC) calls High-Impact, Low-Frequency (HILF) events to plunge us into darkness. This presentation covers one such HILF risk, geomagnetic disturbances (GMDs). GMDs, which are caused by corona mass ejections (CMEs) from the sun, have the potential to affect the power grid. This is due to the CMEs impacting the earth's magnetic field, which in turn can induce quasi-dc electric fields in the earth (with frequencies usually much below 1 Hz). These electric fields then cause geomagnetically induced currents to flow in the high voltage grid that can cause half-cycle saturation in the power transformers, resulting in increased transformer reactive power losses resulting in widespread blackouts and, perhaps, transformer damage. This presentation provides an overview of the impact of GMDs on the grid, shows how this impact can be modeled and visualized, and discusses mitigation strategies.

Recent investments in smart grid technology and infrastructure have the potential to usher in widespread adoption of residential time varying pricing programs. These investments notwithstanding, proactive approaches to increasing active participation in these programs will be required to fully leverage demand response potential. We implement a large-scale randomized control trial in which one group of households is given the option to opt-in to time-based pricing while another group is defaulted into the program but allowed to opt-out. We provide dramatic evidence of a default effect – a significantly higher fraction of households defaulted onto the time-based pricing plan enroll in the program, even though opting out simply involved making a phone call or clicking through to a website. A distinguishing feature of our empirical setting is that we observe follow-on behavior subsequent to the default manipulation. This, in conjunction with randomization of the default provision, allows us to separately identify the subsequent response of "complacent" households (i.e., those who only enroll in time-based pricing if assigned to the opt-out treatment). We find that the complacent households do reduce energy use during higher priced peak periods, though significantly less on average compared to customers who actively opt in. However, with compliers comprising approximately 75 percent of the population, we observe significantly larger average demand reductions among consumers assigned to the opt-out group. We examine the extent to which the behavioral responses we observe lend support to alternative explanations for default effects including switching costs, inattention, and explanations that assume preferences are constructed versus pre-determined and stable across choice contexts.

This work is joint with Peter Cappers, Anna Spurlock and Annika Todd (LBNL) and Catherine Wolfram, Patrick Baylis (UC Berkeley).

SmartGrid speakers will discuss exciting new ideas and technologies that are changing the electricity industry. The theme of the seminar series is on smart grids and energy systems, with speakers from academic institutions and industry. The hour-long seminars, including ample time for discussion, are held at 1:00 pm or 1:15 pm approximately every Thursday.

Abstract TBA:

SmartGrid speakers will discuss exciting new ideas and technologies that are changing the electricity industry. The theme of the seminar series is on smart grids and energy systems, with speakers from academic institutions and industry. The hour-long seminars, including ample time for discussion, are held at 1:00 pm or 1:15 pm approximately every Thursday.

This talk addresses challenges for distribution grids in a city area. A key point is to enable bi-directional interactions between the customers and the power systems within distribution grids. For such interactions, State Grid Corporation of China (SGCC) defines 12 areas for significant investment and technology innovation. We give examples about Beijing distribution grid modernization process, including planning, design, management, and supervision in metropolitan power systems. Field experiences will be discussed, e.g. indoor/underground substation and high voltage overhead/cable transmission line.

SmartGrid speakers will discuss exciting new ideas and technologies that are changing the electricity industry. The theme of the seminar series is on smart grids and energy systems, with speakers from academic institutions and industry. The hour-long seminars, including ample time for discussion, are held at 1:00 pm or 1:15 pm approximately every Thursday.

With the number of Phasor Measurement Units (PMUs) in the North American power grid scaling up into the thousands, system operators are gradually inclining towards distributed cyber-physical architectures for executing wide-area monitoring and control operations using Synchrophasors. Traditional centralized approaches, in fact, are anticipated to become untenable soon due to various factors such as data volume, security, single point of failure, communication overhead, and failure to adhere to real-time deadlines. In this talk I will propose three such distributed communication and computational architectures, and their associated distributed algorithms, for one of the most critical applications run by utilities – namely, wide-area monitoring of power flow oscillations. In these architectures, the estimators located at the control center of a utility company run local convex optimization and consensus algorithms using Alternating Directions Multiplier Method (ADMM), and thereafter communicate with other control centers to reach a global solution. Both synchronous and asynchronous communication will be considered. I will discuss the convergence and accuracy trade-offs for each ADMM implementation, and also illustrate their architectural resiliency against denial-of-service attacks using case studies from the recently federated DETER-WAMS testbed between NC State and University of Southern California.

SmartGrid speakers will discuss exciting new ideas and technologies that are changing the electricity industry. The theme of the seminar series is on smart grids and energy systems, with speakers from academic institutions and industry. The hour-long seminars, including ample time for discussion, are held at 1:00 pm or 1:15 pm approximately every Thursday.

In the study of nonlinear dynamical systems, it is useful to determine the (parameter space) phase boundary that separates stable from unstable behaviour. This presentation will consider algorithms which achieve that goal through the use of shooting methods and Euler homotopy continuation. Firstly, the trajectory is forced to remain in the proximity of an unstable equilibrium point (UEP) for a specified time. That time is then progressively increased, ensuring the trajectory approaches the UEP arbitrarily closely. This process can be used to explore trade-offs between parameters. It also forms the basis for robustness assessment by establishing the minimum distance (in parameter space) from a given operating point to the phase boundary. The various concepts will be illustrated through examples that consider "fault induced delayed voltage recovery" (FIDVR), a vulnerability of distribution networks where residential air-conditioning is prevalent.

Smart electricity meters are currently deployed in millions of households to collect detailed individual electricity consumption data. Compared to traditional electricity data based on aggregated consumption, smart meter data are much more volatile and less predictable. There is a need within the energy industry for probabilistic forecasts of household electricity consumption to quantify the uncertainty of future electricity demand, in order to undertake appropriate planning of generation and distribution. Smart meter data provide the data to meet this need. Much of the existing literature has focused on forecasting the average electric load (often called point forecasting); that is, in forecasting the mean of the future demand distribution, conditional on a number of predictor variables such as calendar and temperature variables. However, it has become increasingly important to forecast not only the conditional mean but the entire distribution of the future demand. In other words, a shift is occurring from point forecasting to probabilistic forecasting. The literature on probabilistic load forecasting is rather sparse, and is even more limited for smart meter data. We adopt a quantile regression approach where a different model is estimated for each quantile of the future distribution by minimizing the pinball loss. We propose to compare different quantile regression methods in terms of forecast accuracy for different quantiles and different forecast horizons. Our experiments will be based on a smart meter dataset collected from 3639 households in Ireland at 30-minute intervals over a period of 1.5 years. We discuss and present initial results together with some planned future work, including peak demand forecasting, hierarchical forecasting and whether considering customer behavior similarities can improve the forecast performance.