Incorporating Practice Theory in Sub-Profile Models for Short Term Aggregated Residential Load Forecasting

Stephen, Bruce; Tang, Xiaoqing; Harvey, Poppy R.; Galloway, Stuart; Jennett, Kyle

doi:10.1109/tsg.2015.2493205

Cited by 147 publications

(89 citation statements)

References 24 publications

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“…These are an ARIMA model with lags of 48 samples, and feed-forward Neural Network with 48 output nodes and 49 input nodes as used in [16], model based on the Gaussian Process (GP) and Gradient Boost Machine (GBM) used by [17], Ensemble Forecast (combination of all used demand forecasting methods by simple unweighted average) [18,19] and finally Persistent forecasting.…”

Section: ) Demand Forecasting: Different Methods Of Demandmentioning

confidence: 99%

Energy storage day-ahead scheduling to reduce grid energy export and increase self-consumption for micro-grid and small power park applications

Sobon

Roscoe

Stephen

2017

2017 52nd International Universities Power Engineering Conference (UPEC)

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This version is available at https://strathprints.strath.ac.uk/61467/ Strathprints is designed to allow users to access the research output of the University of Strathclyde. Unless otherwise explicitly stated on the manuscript, Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Please check the manuscript for details of any other licences that may have been applied. You may not engage in further distribution of the material for any profitmaking activities or any commercial gain. You may freely distribute both the url (https://strathprints.strath.ac.uk/) and the content of this paper for research or private study, educational, or not-for-profit purposes without prior permission or charge.Any correspondence concerning this service should be sent to the Strathprints administrator: strathprints@strath.ac.ukThe Strathprints institutional repository (https://strathprints.strath.ac.uk) is a digital archive of University of Strathclyde research outputs. It has been developed to disseminate open access research outputs, expose data about those outputs, and enable the management and persistent access to Strathclyde's intellectual output.Energy storage day-ahead scheduling to reduce grid energy export and increase self-consumption for micro-grid and small power park applications. AbstractDevelopments in energy storage technology will start to play a prominent role in overcoming the problems of generation intermittency by providing the ability to shift demand to times when generation is available. However, exploiting the potential of this technology requires the design of an optimal charging and discharging schedule to allow its integration with the energy network that brings maximum advantage to both the system and the user. This paper introduces a mathematical model for generation and demand forecasting with energy storage scheduling that can be used for micro-grid and small power park applications. The proposed solution models the physical limitations associated with the energy storage technology used, which will constrain charge and discharge schedules beyond what can be forecast for them. A case study of a community feeder with large PV installations is presented to demonstrate the effectiveness of the model. Day-ahead charge and discharge schedules were produced that increased self-consumption within the system and reduced energy export to the grid. The main contribution of this work is the design of a generic parametrized forecasting and energy storage scheduling tool that will be a platform for further development to specialized storage technology and its potential scalability.

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Section: ) Demand Forecasting: Different Methods Of Demandmentioning

confidence: 99%

Energy storage day-ahead scheduling to reduce grid energy export and increase self-consumption for micro-grid and small power park applications

Sobon

Roscoe

Stephen

2017

2017 52nd International Universities Power Engineering Conference (UPEC)

Self Cite

View full text Add to dashboard Cite

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“…Due to the page limit and many mature approaches that already exist for residential load forecasting [33,34] and portfolio evaluation [35,36], risk management will be the focus of discussion here, along with many recent advances in the research community. In a typical example such as [37], the author utilizes stochastic programming techniques to determine the day-ahead market bidding strategies for retailers with flexible demands to maximize their short-term profit, specifically including a case study based on Sweden's electricity market and consideration of the demand uncertainty of retail customers.…”

Section: Decision Making Of Retailersmentioning

confidence: 99%

The Next-Generation U.S. Retail Electricity Market with Customers and Prosumers—A Bibliographical Survey

et al. 2017

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Due to the rapidly-changing technologies in the power industry, many new references addressing the frameworks and business models of the next-generation retail electricity market are entering the research community. In particular, considering new customers with considerable demand response awareness and so-called prosumers with localized power generation based on distributed energy resources (DERs), the next-generation retail electricity market infrastructure will be a level playing field for local energy transactions, strategic pricing scheme design, new business model design and building an innovative energy ecosystem. Consequently, there is an urgent need to keep track of international experiences and activities taking place in the field of the market mechanism design problem at the distribution level. This paper provides a comprehensive survey of recent technology developments and aims to inspire awareness of the further deregulation of the electricity market, especially in areas close to customers. We mainly bring attention to the more than 90 articles published during the past five years. The collected literature has been divided into different sections to discuss different aspects of the next-generation retail electricity market under the deregulated power industry.

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“…Practice theory is used in [24], [25] to conceptualize and explain energy consumption behaviours, routines and possible flexibility. While formalisms are emerging [26] , a model that quantifies demand changes in reaction to pricing signals has yet to emerge as a standard approach.…”

Section: Predicting User Response To a Price Signalmentioning

confidence: 99%

“…In this work a simple, straight forward method, which takes into account user preferences for the use of the appliance and the responsiveness for each activity towards a price change is presented which can enrich or substitute existing elasticity based approaches. Although inflexible personal routines as highlighted in [26] are not explicitly captured, the presented methods implicitly build on daily routines and a load shift will occur in line with observed temporal preferences for each activity.…”

Section: Predicting User Response To a Price Signalmentioning

confidence: 99%

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Modeling the Effects of Variable Tariffs on Domestic Electric Load Profiles by Use of Occupant Behavior Submodels

Fischer

Stephen

Flunk

et al. 2017

IEEE Trans. Smart Grid

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Abstract-Emerging infrastructure for residential meter communication and data processing carries the potential to control household electrical demand within local power system constraints. Deferral of load control can be incentivised through electricity tariff price structure which can in turn reshape a daily load profile. This paper presents a stochastic bottom-up model designed to predict the change in domestic electricity profile invoked by consumer reaction to electricity unit price, with submodels comprising user behaviour, price response and dependency between behaviour and electric demand. The developed models are used to analyse the demand side management potential of the most relevant energy consuming activities through a simulated German household demonstrating that in the given scenario 8% of the annual electricity demand is shifted, leading to a 35e annual saving. However, a 7% higher than average peak load results from the structure of the tariff signal modelled herein. A discussion on selected aspects for tariff design for categories of typical household appliances is included.

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Incorporating Practice Theory in Sub-Profile Models for Short Term Aggregated Residential Load Forecasting

Cited by 147 publications

References 24 publications

Energy storage day-ahead scheduling to reduce grid energy export and increase self-consumption for micro-grid and small power park applications

Energy storage day-ahead scheduling to reduce grid energy export and increase self-consumption for micro-grid and small power park applications

The Next-Generation U.S. Retail Electricity Market with Customers and Prosumers—A Bibliographical Survey

Modeling the Effects of Variable Tariffs on Domestic Electric Load Profiles by Use of Occupant Behavior Submodels

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