Incentive-Based Demand Response Framework for Residential Applications: Design and Real-Life Demonstration

Angelina D Bintoudi,Dimitrios Tzovaras,Paschalis Gkaidatzis,Christos Timplalexis,Athanasios Tryferidis,Lampros Zyglakis,Georgios Isaioglou,Napoleon Bezas,Dimosthenis Ioannidis

doi:10.3390/en14144315

Angelina D Bintoudi, Dimitrios Tzovaras + Show 7 more

Open Access

https://doi.org/10.3390/en14144315

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Abstract

In 2020, residential sector loads reached 25% of the overall electrical consumption in Europe and it is foreseen to stabilise at 29% by 2050. However, this relatively small increase demands, among others, changes in the energy consuming behaviour of households. To achieve this, Demand Response (DR) has been identified as a promising tool for unlocking the hidden flexibility potential of residential consumption. In this work, a holistic incentive-based DR framework aiming towards load shifting is proposed for residential applications. The proposed framework is characterised by several innovative features, mainly the formulation of the optimisation problem, which models user satisfaction and the economic operation of a distributed household portfolio, the customised load forecasting algorithm, which employs an adjusted Gradient Boosting Tree methodology with enhanced feature extraction and, finally, a disaggregation tool, which considers electrical features and time of use information. The DR framework is first validated through simulation to assess the business potential and is then deployed experimentally in real houses in Northern Greece. Results demonstrate that a mean 1.48% relative profit can be achieved via only load shifting of a maximum of three residential appliances, while the experimental application proves the effectiveness of the proposed algorithms in successfully managing the load curves of real houses with several residents. Correlations between market prices and the success of incentive-based load shifting DR programs show how wholesale pricing should be adjusted to ensure the viability of such DR schemes.

Highlights

During the past two decades, Demand-Response (DR) has attracted incremental interest of both the research community and industry, so that new ways of maintaining the supply–demand balance are created via shifting part of this obligation from generation to demand
Just before the end of the day, the User Profiling (UP)-DD sub-module detected whether the users followed the suggested appliance scheduling and estimates their participation rate, which essentially corresponds to the fraction of time participating in the Demand Response (DR) process to the time dictated by the DR-MOO schedule as produced at the beginning of the day
The ODRes framework is composed of several components which employ several different technologies, including optimisation techniques, machine learning, data analytics, hardware and mobile and web-based user interfaces

Summary

Introduction

During the past two decades, Demand-Response (DR) has attracted incremental interest of both the research community and industry, so that new ways of maintaining the supply–demand balance are created via shifting part of this obligation from generation to demand. DR is considered to be a vital building block of Smart Grids (SG) that exploit fully the potential Renewable Energy Sources (RES) [1], energy storage systems (ESS) [2] and electric vehicles (EVs) [3] Due to this fact, the energy markets realised that new business models had to be created so that the aggregated potential of DR could be used towards flattening the load curves [4]. There are significant challenges though in the coordination of a high volume of end consumers, featuring high diversity in electricity consumption patterns To overcome these issues, a large number of customers could be aggregated and represented by one entity that participates in the energy markets [6], that is, an aggregator

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