Combining real-time and fixed tariffs in the demand response aggregation and remuneration

The current Energy Market is not yet ready for the integration of the Smart Grid context. Concepts such as Demand Response and Distributed Generation, namely renewable energy resources, are not yet included in current business models in order to the system flow properly. Therefore, the authors propose a methodology that gathers all these concepts through the optimization, aggregation and remuneration of resources. The purpose of this paper will be to study the influence of the tariff used for the remuneration and incentive of the participants in the formation of the groups in the aggregation phase. Three studies were performed: aggregation with only the result of the optimization (schedule power for each resource); this result and the fixed tariff associated with each resource; result and a new tariff that considers real-time values.The present work was done and funded in the scope of the following projects: European Union's Horizon 2020 project DOMINOES (grant agreement No 771066), and UID/EEA/00760/2019 funded by FEDER Funds through COMPETE program and by National Funds through FCT.info:eu-repo/semantics/publishedVersio

Artificial Intelligence and Machine Learning Approaches to Energy Demand-Side Response: A Systematic Review

Recent years have seen an increasing interest in Demand Response (DR) as a means to provide flexibility, and hence improve the reliability of energy systems in a cost-effective way. Yet, the high complexity of the tasks associated with DR, combined with their use of large-scale data and the frequent need for near real-time de-cisions, means that Artificial Intelligence (AI) and Machine Learning (ML) — a branch of AI — have recently emerged as key technologies for enabling demand-side response. AI methods can be used to tackle various challenges, ranging from selecting the optimal set of consumers to respond, learning their attributes and pref-erences, dynamic pricing, scheduling and control of devices, learning how to incentivise participants in the DR schemes and how to reward them in a fair and economically efficient way. This work provides an overview of AI methods utilised for DR applications, based on a systematic review of over 160 papers, 40 companies and commercial initiatives, and 21 large-scale projects. The papers are classified with regards to both the AI/ML algorithm(s) used and the application area in energy DR. Next, commercial initiatives are presented (including both start-ups and established companies) and large-scale innovation projects, where AI methods have been used for energy DR. The paper concludes with a discussion of advantages and potential limitations of reviewed AI techniques for different DR tasks, and outlines directions for future research in this fast-growing area