Pattern Recognition Residential Demand Response: An Option for Critical Peak Demand Reduction in New Zealand

Influencing households to adopt sustainable energy consumption behaviour is important to the transition towards a sustainable energy future. However, if one aims at influencing the energy consumption habits of people, one should also be able to estimate the resulting effects on the entire energy system. Residential demand response to reduce load on the electricity network has largely been impeded by information barriers and a lack of proper understanding of consumers’ behaviour. What are not well understood and are of great interest include load disaggregation, the behaviour of customers when responding to demand response request, load shifting models and their impact on the load curve of the utility. There is concern among demand response practitioners, for example, that demand response in the residential sector may simply move the peak problem with scale from one point in time to another. However, unavailability of appliance-level demand data makes it difficult to study this problem. In this paper, a generalized statistical model for generating load curves of the individual residential appliances is presented. These data allow one to identify the relative contribution of the different components of the residential load on a given residential feeder. This model has been combined with demand response survey in a neighbourhood with 400 households in Christchurch, New Zealand, to determine the effect of customers’ behaviour in reducing the neighbourhood’s winter peak demand. The results show that when customers’ are given enhanced information, they would voluntarily act to reduce their peak demand by about 10% during the morning peak hours and 11% during the evening peak hours. The demand responsiveness of the individual appliances is also presented. The effectiveness of customer behaviour modification in maintaining system reliability is also presented

Test Method and Equivalent Circuit Modelling of a PEM Fuel Cell in a Passive State

Anovel test protocol is proposed for fuel cells that are in a nonfunctioning or passive state. Standard fuel-cell test methods are reviewed, along with the equivalent circuit models (ECMs) used to represent functioning or active fuel cells. Standard active tests focus on single cells, while the passive test is shown to be applicable to testing multiple cells. The passive test measures electrical characteristics of the fuel cell in the absence of electrochemical reactions. A simple ECM is developed to describe the cell behavior under the passive test conditions. Circuit model parameters of many series-connected cells can be acquired using the results of a single stack test. Proton exchange membrane fuel cells (PEMFCs) from three manufacturers were tested, ranging in system power from 12–500 W. Test results for each PEMFC exhibited similar behavior that is well predicted by the ECM. A strong similarity between a passive fuel cell and a double layer capacitor is discussed

The Minimum Energy Transport Activity Access Model

A reduction in the energy intensity of private transport is necessary to manage the uncertainties of future availability of oil supplies. The built environment and transport infrastructure of an urban form will determine the extent to which low impact adaptations to these constraints are possible, and hence the resilience of residents to fuel price shocks and constraints. This paper introduces the concept that the underlying geographic form of an urban area and its transport networks is characterised by an Active Mode Accessibility that could service some proportion of the resident transport activity system. The active mode accessibility is a non-dimensional measure of the proportion of trips that can be reached by active modes, given the population demographics of the study area. Greater active mode accessibility implies greater resilience to shocks and constraints. This paper introduces a spatial method for measuring the active mode accessibility within a selected study area, a GIS-based tool for applying the method, and presents two case studies. Model results and analysis are relevant to the redevelopment of existing areas and during the planning of new developments. The Central Christchurch study presents an Active Mode Accessibility of 100%, as there are a wide range of local facilities available for every activity. The study of Rolleston township presents a significantly lower Active Mode Accessibility of 62%, due to a lack of local facilities for many activities, and in particular, education. Although the model is still under development, it clearly indicates that it is not the distribution of facilities, but the lack of local pre-school and secondary education facilities which drastically reduces the resilience of Rolleston. The high facility density of the central city, for all activities, indicates that the residents of the central city area are extremely resilient to both fuel price shocks and constraints

Implementation of Unsteady Sampling Procedures for the Parallel Direct Simulation Monte Carlo Method

accepted for publication 7th March 2008 JCOMP-D-07-00498R1An unsteady sampling routine for a general parallel Direct Simulation Monte Carlo method called PDSC is introduced, allowing the simulation of time-dependent flow problems in the near continuum range. A post-processing procedure called DSMC Rapid Ensemble Averaging Method (DREAM) is developed to improve the statistical scatter in the results while minimising both memory and simulation time. This method builds an ensemble average of repeated runs over small number of sampling intervals prior to the sampling point of interest by restarting the flow using either a Maxwellian distribution based on macroscopic properties for near equilibrium flows (DREAM-I) or output instantaneous particle data obtained by the original unsteady sampling of PDSC for strongly non-equilibrium flows (DREAM-II). The method is validated by simulating shock tube flow and the development of simple Couette flow. Unsteady PDSC is found to accurately predict the flow field in both cases with significantly reduced run-times over single processor code and DREAM greatly reduces the statistical scatter in the results while maintaining accurate particle velocity distributions. Simulations are then conducted of two applications involving the interaction of shocks over wedges. The results of these simulations are compared to experimental data and simulations from the literature where there these are available. In general it was found that ten ensembled runs of DREAM processing could reduce the statistical uncertainty in the raw PDSC data by 2.5-3.3 times, based on the limited number of cases in the present study