Buildings are among the largest consumers of electricity in the US. A significant portion of this energy use in buildings can be attributed to HVAC systems used to maintain comfort for occupants. In most cases these building HVAC systems run on fixed schedules and do not employ any fine grained control based on detailed occupancy information. In this paper we present the design and implementation of a presence sensor platform that can be used for accurate occupancy detection at the level of individual offices. Our presence sensor is low-cost, wireless, and incrementally deployable within existing buildings. Using a pilot deployment of our system across ten offices over a two week period we identify significant opportunities for energy savings due to periods of vacancy. Our energy measurements show that our presence node has an estimated battery lifetime of over five years, while detecting occupancy accurately. Furthermore, using a building simulation framework and the occupancy information from our testbed, we show potential energy savings from 10% to 15% using our system.

[ACM Press the 2nd ACM Workshop - Zurich, Switzerland (2010.11.02-2010.11.02)] Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building - BuildSys \'10 - Occupancy-driven energy management for smart building automation

In this study, a significant literature review on peak load shaving strategies has been presented. The impact of three major strategies for peak load shaving, namely demand side management (DSM), integration of energy storage system (ESS), and integration of electric vehicle (EV) to the grid has been discussed in detail. Discussion on possible challenges and future research directions for each type of the strategy has also been included in this review. For the energy storage system, different technologies used for peak load shaving purpose, which include their methods of operation and control have been elaborated further. Finally, the sizing of the ESS storage system is discussed. For the demand side management system, various management methods and challenges associated with DSM implementation have been thoroughly explained. A detailed discussion on the electric vehicle strategy has also been included in the review, which considers the integration, control and operation techniques for implementing the peak load shaving.

A review on peak load shaving strategies

With the recent rapid development of smart grid technology, the distribution grids become more active, and the interaction between transmission and distribution grids becomes more significant. However, in traditional power flow calculations, transmission and distribution grids are separated, which is not suitable for such future smart grids. To achieve a global unified power flow solution to support an integrated analysis for both transmission and distribution grids, we propose a global power flow (GPF) method that considers transmission and distribution grids as a whole in this paper. We construct GPF equations, and develop a master-slave-splitting (MSS) iterative method with convergence guarantee to alleviate boundary mismatches between the transmission and distribution grids. In our method, the GPF problem is split into a transmission power flow and a number of distribution power flow sub-problems, which supports on-line geographically distributed computation. Each sub-problem can be solved using a different power flow algorithm to capture the different features of transmission and distribution grids. An equivalent method is proposed to improve the convergence of the MSS-based GPF calculation for distribution grids that include loops. Numerical simulations validate the effectiveness of the proposed method, in particular when the distribution grid has loops or distributed generators.

Master–Slave-Splitting Based Distributed Global Power Flow Method for Integrated Transmission and Distribution Analysis

The increasing integration of distributed energy resources calls for new planning and operational tools. However, such tools depend on system topology and line parameters, which may be missing or inaccurate in distribution grids. With abundant data, one idea is to use linear regression to find line parameters, based on which topology can be identified. Unfortunately, the linear regression method is accurate only if there is no noise in both the input measurements (e.g., voltage magnitude and phase angle) and output measurements (e.g., active and reactive power). For topology estimation, even with a small error in measurements, the regression-based method is incapable of finding the topology using nonzero line parameters with a proper metric. To model input and output measurement errors simultaneously, we propose the error-in-variables model in a maximum-likelihood estimation framework for joint line parameter and topology estimation. While directly solving the problem is NP-hard, we successfully adapt the problem into a generalized low-rank approximation problem via variable transformation and noise decorrelation. For accurate topology estimation, we let it interact with parameter estimation in a fashion that is similar to expectation-maximization algorithm in machine learning. The proposed PaToPa approach does not require a radial network setting and works for mesh networks. We demonstrate the superior performance in accuracy for our method on IEEE test cases with actual feeder data from Southern California Edison.

PaToPa: A Data-Driven Parameter and Topology Joint Estimation Framework in Distribution Grids

MR. A. A. CAMPBELL SWINTON, in his letter on the above subject in NATURE of December 18, states that “it is hopeless to expect to be able to effect anything of this nature with the heat-engine, for with this we should scarcely reach the 2 per cent, efficiency nearly attained by vegetation.”

Power from the Sun

Transmission line parameters can be estimated from PMU data at the end of two lines. Both linear least squares and total least squares approaches have been suggested in the literature. This paper further develops the TLS approach for estimating positive sequence line parameters. We present an algorithm for appropriate selection of phasor samples (similar to down sampling) so as to contain independent information for better filtering action. Results of parameter estimation will be erroneous if the CVT accuracy is compromised. We propose a Durbin Watson test to detect serial correlation in errors to flag biased CVT measurements. Finally, we compare and contrast TLS and LS estimators and demonstrate superiority of TLS approach using ATP-EMTP simulations.

Line parameter estimation using phasor measurements by the total least squares approach

The authors consider the problem of estimating zero sequence parameters of a transmission line using synchrophasor data. When a set of three-phase transmission lines share, partial or complete, right of way, then their zero sequence models exhibit mutual coupling. As such the zero sequence parameters cannot be estimated by linear least squares or total least squares (TLS) technique which are the preferred methods when dealing with the positive sequence line parameter estimation problem. Further, method design has to factor the constraint of a sparse data set when dealing with zero sequence phasors. Therefore, the authors propose orthogonal distance regression approach for solving the zero sequence parameter estimation problem. This generalises the method of TLS to the non-linear parameter estimation problem considering noise in both the voltage and current synchrophasor measurements. Extensive case studies and comparative evaluations are presented to demonstrate the efficacy of the proposed approach.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-gtd.2017.0057

Estimation of zero sequence parameters of mutually coupled transmission lines from synchrophasor measurements

This paper presents an implementable distributed load flow calculation method for online analysis, for power systems having multiple, geographically separated areas. The system is partitioned into multiple areas, with each area having its own control centre. Adjacent areas share a tie-line and the power flowing in these tie-lines are expected to be known to the control centres concerned before the analysis is started. Since each area may be governed by different companies, it is justified to expect that the power that is traded through a tie-line would be known to the control centres sharing the line. The proposed method uses the fast decoupled load flow approach and it suitably adds elements to the B' and B" matrix to find out the state variables of the boundary buses. Results on the IEEE 14 and 30 bus systems show that the distributed analysis converges as fast as the centralized ones.

Distributed Fast Decoupled Load Flow Analysis

There is a global trend of deploying distributed grid scale storages in power systems for specific applications like, renewable integration, peak load management, congestion management, etc. Due to high capital investment, return on investment (ROI) for grid scale storage for any specific application is not lucrative enough for investors. However, if these storages are used across multiple applications, ROI could be more attractive than expected. In this paper, we focus on exploring the ROI when storage devices are used for applications such as transmission and distribution (T&D) system upgrade deferral and mitigation of renewable energy curtailment. We formulate an optimization problem to trade off the economic advantage of using storage devices versus its loss of life. While the formulation is generic and can handle any kind of storage, we focus on battery storage. We use detailed formulations to estimate loss of battery life to estimate capital expenditure (capex) on battery storage, on a per-day basis.

Kalyan Dasgupta

Papers

Line parameter estimation using phasor measurements by the total least squares approach

Estimation of zero sequence parameters of mutually coupled transmission lines from synchrophasor measurements

Distributed Fast Decoupled Load Flow Analysis

Estimating return on investment for grid scale storage within the economic dispatch framework

Tie-line constrained distributed state estimation