Application of a Bayesian Network Complex System Model Examining the Importance of Customer-Industry Engagement to Peak Electricity Demand Reduction

This paper explores the importance of customer-industry engagement (CIE) to peak energy demand by means of a newly developed Bayesian Network (BN) complex systems model entitled the Residential Electricity Peak Demand Model (REPDM). The REPDM is based on a multi-disciplinary perspective designed to solve the complex problem of residential peak energy demand. The model provides a way to conceptualise and understand the factors that shift and reduce consumer demand in peak times. To gain insight into the importance of customer-industry engagement in affecting residential peak demand, this research investigates intervention impacts and major influences through testing five scenarios using different levels of customer-industry engagement activities. Scenario testing of the model outlines the dependencies between the customer-industry engagement interventions and the probabilities that are estimated to govern the dependencies that influence peak demand. The output from the model shows that there can be a strong interaction between the level of CIE activities and interventions. The influence of CIE activity can increase public and householder support for peak reduction and the model shows how the economic, technical and social interventions can achieve greater peak demand reductions when well-designed with appropriate levels of CIE activities.

National Electricity Generation,Electricity Consumption and Peak Load by Grid (April 2006)

Statistics on electricity generation and time of frequency over-limit;Statistics on electricity consumption and peak load.

National Electricity Generation,Electricity Consumption and Peak Load by Grid (March 2006)

Statistics on electricity generation and time of frequency over-limit;Statistics on electricity consumption and peak load.

A New Quasi 2-Dimensional Analytical Approach to Predicting Ring Junction Voltage,Edge Peak Fields and Optimal Spacing of Planar Junction with Single Floating Field Limiting Ring Structure

WT8.BZ]A new quasi 2-dimensional analytical approach to predicting the ring voltage,edge peak fields and optimal spacing of the planar junction with a single floating field limiting ring structure has been proposed,based on the cylindrical symmetric solution and the critical field concept.The effects of the spacing and reverse voltage on the ring junction voltage and edge peak field profiles have been analyzed.The optimal spacing and the maximum breakdown voltage of the structure have also been obtained.The analytical results are in excellent agreement with that obtained from the 2-D device simulator,MEDICI and the reported result,which proves the presented model valid.

Gate breakdown of high-voltage P-LDMOS and improved methods

The failure experiments of the P-LDMOS （lateral double diffused metal oxide semiconductor） demonstrate that the high peak electrical fields in the channel region of high-voltage P-LDMOS will reinforce the hot-carrier effect, which can greatly reduce the reliability of the P-LDMOS. The electrical field distribution and two field peaks along the channel surface are proposed by Tsuprem-4 and Medici. The reason of resulting in the two electrical field peaks is also discussed. Two ways of reducing the two field peaks, which are to increase the channel length and to reduce the channel concentration, are also presented. The experimental results show that the methods presented can effectively improve the gate breakdown voltage and greatly improve the reliability of the P-LDMOS.

High-resolution peak demand estimation using generalized additive models and deep neural networks

This paper covers predicting high-resolution electricity peak demand features given lower-resolution data.This is a relevant setup as it answers whether limited higher-resolution monitoring helps to estimate future high-resolution peak loads when the high-resolution data is no longer available.That question is particularly interesting for network operators considering replacing high-resolution monitoring by predictive models due to economic considerations.We propose models to predict half-hourly minima and maxima of high-resolution(every minute)electricity load data while model inputs are of a lower resolution(30 min).We combine predictions of generalized additive models(GAM)and deep artificial neural networks(DNN),which are popular in load forecasting.We extensively analyze the prediction models,including the input parameters’importance,focusing on load,weather,and seasonal effects.The proposed method won a data competition organized by Western Power Distribution,a British distribution network operator.In addition,we provide a rigorous evaluation study that goes beyond the competition frame to analyze the models’robustness.The results show that the proposed methods are superior to the competition benchmark concerning the out-of-sample root mean squared error(RMSE).This holds regarding the competition month and the supplementary evaluation study,which covers an additional eleven months.Overall,our proposed model combination reduces the out-of-sample RMSE by 57.4%compared to the benchmark.