A novel real time pricing scheme for demand response in residential distribution systems

Price based technique is one way to handle increase in peak demand and deal with voltage violations in residential distribution systems. This paper proposes an improved real time pricing scheme for residential customers with demand response option. Smart meters and in-home display units are used to broadcast the price and appropriate load adjustment signals. Customers are given an opportunity to respond to the signals and adjust the loads. This scheme helps distribution companies to deal with overloading problems and voltage issues in a more efficient way. Also, variations in wholesale electricity prices are passed on to electricity customers to take collective measure to reduce network peak demand. It is ensured that both customers and utility are benefitted by this scheme.

Energy efficient home with price sensitive stochastically programmable TCAs

Introduction of dynamic pricing in present retail market, considerably affects customers with an increased cost of energy consumption. Therefore, customers are enforced to control their loads according to price variation. This paper proposes a new technique of Home Energy Management, which helps customers to minimize their cost of energy consumption by appropriately controlling their loads. Thermostatically Controllable Appliances (TCAs) such as air conditioner and water heater are focused in this study, as they consume more than 50% of the total household energy consumption. The control process includes stochastic dynamic programming, which incorporated uncertainties in price and demand variation. It leads to an accurate selection of appliance settings. It is followed by a real time control of selected appliances with its optimal settings. Temperature set points of TCAs are adjusted based on price droop which is a reflection of actual cost of energy consumption. Customer satisfaction is maintained within limits using constraint optimization. It is showed that considerable energy savings is achieved.

Every One, Every Day

Creative Statement: “There are those who see Planet Earth as a gigantic living being, one that feeds and nurtures humanity and myriad other species – an entity that must be cared for. Then there are those who see it as a rock full of riches to be pilfered heedlessly in a short-term quest for over-abundance. This ‘cradle to grave’ mentality, it would seem, is taking its toll (unless you’re a virulent disbeliever in climate change). Why not, ask artists Priscilla Bracks and Gavin Sade, take a different approach? To this end they have set out on a near impossible task; to visualise the staggering quantity of carbon produced by Australia every year. Their eerie, glowing plastic cube resembles something straight out of Dr Who or The X Files. And, like the best science fiction, it has technical realities at its heart. Every One, Every Day tangibly illustrates our greenhouse gas output – its 27m3 volume is approximately the amount of green-house gas emitted per capita, daily. Every One, Every Dayis lit by an array of LED’s displaying light patterns representing energy use generated by data from the Australian Energy Market. Every One, Every Day was formed from recycled, polyethylene – used milk bottles – ‘lent’ to the artists by a Visy recycling facility. At the end of the Vivid Festival this plastic will be returned to Visy, where it will re-enter the stream of ‘technical nutrients.’ Could we make another world? One that emulates the continuing cycles of nature? One that uses our ‘technical nutrients’ such as plastic and steel in continual cycles, just like a deciduous tree dropping leaves to compost itself and keep it’s roots warm and moist?” (Ashleigh Crawford. Melbourne – April, 2013) Artistic Research Statement: The research focus of this work is on exploring how to represent complex statistics and data at a human scale, and how produce a work where a large percentage of the materials could be recycled. The surface of Every One, Every Day is clad in tiles made from polyethylene, from primarily recycled milk bottles, ‘lent’ to the artists by the Visy recycling facility in Sydney. The tiles will be returned to Visy for recycling. As such the work can be viewed as an intervention in the industrial ecology of polyethylene, and in the process demonstrates how to sustain cycles of technical materials – by taking the output of a recycling facility back to a manufacturer to produce usable materials. In terms of data visualisation, Every One, Every Day takes the form of a cube with a volume of 27 cubic meters. The annual per capita emissions figures for Australia are cited as ranging between 18 to 25 tons. Assuming the lower figure, 18tons per capital annually, the 27 cubic meters represents approximately one day per capita of CO2 emissions – where CO2 is a gas at 15C and 1 atmosphere of pressure. The work also explores real time data visualisation by using an array of 600 controllable LEDs inside the cube. Illumination patterns are derived from a real time data from the Australian Energy Market, using the dispatch interval price and demand graph for New South Wales. The two variables of demand and price are mapped to properties of the illumination - hue, brightness, movement, frequency etc. The research underpinning the project spanned industrial ecology to data visualization and public art practices. The result is that Every One, Every Day is one of the first public artworks that successfully bring together materials, physical form, and real time data representation in a unified whole.

Demand-side response model to avoid spike of electricity price

The aim of this work is to develop a demand-side-response model, which assists electricity consumers exposed to the market price to independently and proactively manage air-conditioning peak electricity demand. The main contribution of this research is to show how consumers can optimize the energy cost caused by the air conditioning load considering to several cases e.g. normal price, spike price, and the probability of a price spike case. This model also investigated how air-conditioning applies a pre-cooling method when there is a substantial risk of a price spike. The results indicate the potential of the scheme to achieve financial benefits for consumers and target the best economic performance for electrical generation distribution and transmission. The model was tested with Queensland electricity market data from the Australian Energy Market Operator and Brisbane temperature data from the Bureau of Statistics regarding hot days from 2011 to 2012.

Integrating electrical vehicles to demand side response scheme in Queensland Australia

Depleting fossil fuel resources and increased accumulation of greenhouse gas emissions are increasingly making electrical vehicles (EV) attractive option for the transportation sector. However uncontrolled random charging and discharging of EVs may aggravate the problems of an already stressed system during the peak demand and cause voltage problems during low demand. This paper develops a demand side response scheme for properly integrating EVs in the Electrical Network. The scheme enacted upon information on electricity market conditions regularly released by the Australian Energy Market Operator (AEMO) on the internet. The scheme adopts Internet relays and solid state switches to cycle charging and discharging of EVs. Due to the pending time-of-use and real-price programs, financial benefits will represent driving incentives to consumers to implement the scheme. A wide-scale dissemination of the scheme is expected to mitigate excessive peaks on the electrical network with all associated technical, economic and social benefits.

Smart grid-demand side response model for optimization air conditioning

The growing demand of air-conditioning is one of the largest contributors to Australia’s overall electricity consumption. This has started to create peak load supply problems for some electricity utilities particularly in Queensland. This research aimed to develop consumer demand side response model to assist electricity consumers to mitigate peak demand on the electrical network. The model developed demand side response model to allow consumers to manage and control air conditioning for every period, it is called intelligent control. This research investigates optimal response of end-user toward electricity price for several cases in the near future, such as: no spike, spike and probability spike price cases. The results indicate the potential of the scheme to achieve energy savings, reducing electricity bills (costs) to the consumer and targeting best economic performance for electrical generation distribution and transmission.

Smart grid-demand side response model to mitigate prices and peak impact on the electrical system

The aims of this project is to develop demand side response model which assists electricity consumers who are exposed to the market price through aggregator to manage the air-conditioning peak electricity demand. The main contribution of this research is to show how consumers can optimise the energy cost caused by the air-conditioning load considering the electricity market price and network overload. The model is tested with selected characteristics of the room, Queensland electricity market data from Australian Energy Market Operator and data from the Bureau of Statistics on temperatures in Brisbane, during weekdays on hot days from 2011 - 2012.

Demand Response for Residential Appliances in a Smart Electricity Distribution Network: Utility and Customer Perspectives

This thesis introduces advanced Demand Response algorithms for residential appliances to provide benefits for both utility and customers. The algorithms are engaged in scheduling appliances appropriately in a critical peak day to alleviate network peak, adverse voltage conditions and wholesale price spikes also reducing the cost of residential energy consumption. Initially, a demand response technique via customer reward is proposed, where the utility controls appliances to achieve network improvement. Then, an improved real-time pricing scheme is introduced and customers are supported by energy management schedulers to actively participate in it. Finally, the demand response algorithm is improved to provide frequency regulation services.

Demand side response to mitigate electrical peak demand in Eastern and Southern Australia

The aim of this work is to develop a Demand-Side-Response (DSR) model, which assists electricity end-users to be engaged in mitigating peak demands on the electricity network in Eastern and Southern Australia. The proposed innovative model will comprise a technical set-up of a programmable internet relay, a router, solid state switches in addition to the suitable software to control electricity demand at user's premises. The software on appropriate multimedia tool (CD Rom) will be curtailing/shifting electric loads to the most appropriate time of the day following the implemented economic model, which is designed to be maximizing financial benefits to electricity consumers. Additionally the model is targeting a national electrical load be spread-out evenly throughout the year in order to satisfy best economic performance for electricity generation, transmission and distribution. The model is applicable in region managed by the Australian Energy Management Operator (AEMO) covering states of Eastern-, Southern-Australia and Tasmania.

Optimum demand side response of smart grid with renewable energy source and electrical vehicles

The paper presents a demand side response scheme,which assists electricity consumers to proactively control own demands in such a way to deliberately avert congestion periods on the electrical network. The scheme allows shifting loads from peak to low demand periods in an attempt to flattening the national electricity requirement. The scheme can be concurrently used to accommodate the utilization of renewable energy sources,that might be available at user’s premises. In addition the scheme allows a full-capacity utilization of the available electrical infrastructure by organizing a wide-use of electric vehicles. The scheme is applicable in the Eastern and Southern States of Australia managed by the Australian Energy Market Operator. The results indicate the potential of the scheme to achieve energy savings and release capacity to accommodate renewable energy and electrical vehicle technologies.

Demand response for residential appliances via Customer Reward Scheme

This paper proposes a reward based demand response algorithm for residential customers to shave network peaks. Customer survey information is used to calculate various criteria indices reflecting their priority and flexibility. Criteria indices and sensitivity based house ranking is used for appropriate load selection in the feeder for demand response. Customer Rewards (CR) are paid based on load shift and voltage improvement due to load adjustment. The proposed algorithm can be deployed in residential distribution networks using a two-level hierarchical control scheme. Realistic residential load model consisting of non-controllable and controllable appliances is considered in this study. The effectiveness of the proposed demand response scheme on the annual load growth of the feeder is also investigated. Simulation results show that reduced peak demand, improved network voltage performance, and customer satisfaction can be achieved.

Defining expected cost for the air conditioning to avoid a price spike of electricity market under DSR model

This work presents a demand side response model (DSR) which assists small electricity consumers, through an aggregator, exposed to the market price to proactively mitigate price and peak impact on the electrical system. The proposed model allows consumers to manage air-conditioning when as a function of possible price spikes. The main contribution of this research is to demonstrate how consumers can minimise the total expected cost by optimising air-conditioning to account for occurrences of a price spike in the electricity market. This model investigates how pre-cooling method can be used to minimise energy costs when there is a substantial risk of an electricity price spike. The model was tested with Queensland electricity market data from the Australian Energy Market Operator and Brisbane temperature data from the Bureau of Statistics during hot days on weekdays in the period 2011 to 2012.

Modeling dynamic demand response using Monte Carlo simulation and interval mathematics for boundary estimation

With the rapid development of various technologies and applications in smart grid implementation, demand response has attracted growing research interests because of its potentials in enhancing power grid reliability with reduced system operation costs. This paper presents a new demand response model with elastic economic dispatch in a locational marginal pricing market. It models system economic dispatch as a feedback control process, and introduces a flexible and adjustable load cost as a controlled signal to adjust demand response. Compared with the conventional “one time use” static load dispatch model, this dynamic feedback demand response model may adjust the load to a desired level in a finite number of time steps and a proof of convergence is provided. In addition, Monte Carlo simulation and boundary calculation using interval mathematics are applied for describing uncertainty of end-user's response to an independent system operator's expected dispatch. A numerical analysis based on the modified Pennsylvania-Jersey-Maryland power pool five-bus system is introduced for simulation and the results verify the effectiveness of the proposed model. System operators may use the proposed model to obtain insights in demand response processes for their decision-making regarding system load levels and operation conditions.

Estimating short and long-term residential demand for electricity : new evidence from Sri Lanka

This study investigates the short-run dynamics and long-run equilibrium relationship between residential electricity demand and factors influencing demand - per capita income, price of electricity, price of kerosene oil and price of liquefied petroleum gas - using annual data for Sri Lanka for the period, 1960-2007. The study uses unit root, cointegration and error-correction models. The long-run demand elasticities of income, own price and price of kerosene oil (substitute) were estimated to be 0.78, - 0.62, and 0.14 respectively. The short-run elasticities for the same variables were estimated to be 032, - 0.16 and 0.10 respectively. Liquefied petroleum (LP) gas is a substitute for electricity only in the short-run with an elasticity 0.09. The main findings of the paper support the following (1) increasing the price of electricity is not the most effective tool to reduce electricity consumption (2) existing subsidies on electricity consumption can be removed without reducing government revenue (3) the long-run income elasticity of demand shows that any future increase in household incomes is likely to significantly increase the demand for electricity and(4) any power generation plans which consider only current per capita consumption and population growth should be revised taking into account the potential future income increases in order to avoid power shortages ill the country.

Estimating the latent demand for rail transit : a case study in Perth, Western Australia

This paper presents a study on estimating the latent demand for rail transit in Australian context. Based on travel mode-choice modelling, a two-stage analysis approach is proposed, namely market population identification and mode share estimation. A case study is conducted on Midland-Fremantle rail transit corridor in Perth, Western Australia. The required data mainly include journey-to-work trip data from Australian Bureau of Statistics Census 2006 and work-purpose mode-choice model in Perth Strategic Transport Evaluation Model. The market profile is analysed, such as catchment areas, market population, mode shares, mode specific trip distributions and average trip distances. A numerical simulation is performed to test the sensitivity of the transit ridership to the change of fuel price. A corridor-level transit demand function of fuel price is thus obtained and its characteristics of elasticity are discussed. This study explores a viable approach to developing a decision-support tool for the assessment of short-term impacts of policy and operational adjustments on corridor-level demand for rail transit.