This study develops a methodology to characterise and forecast large consumers’ electricity demand, particularly municipalities, with hundreds of different metered supply points based on the previous characterisation of facilities’ consumption. Demand forecasting allows consumers to improve their participation in electricity markets and manage their electricity consumption. The method considers a classification by different types of metered supply points combined with artificial neural networks to obtain hourly forecasts using well-known parameters such as day types, hourly temperature, the last hour of electricity consumption, and sunrise and sunset time. We apply the methodology to the municipality of Valencia using over five hundred hourly load profiles for a year during 2017 and 2018. Our results present aggregated forecasts with a maximum Mean Absolute Percentage Error of 3.8% per day, outperforming the same forecast without classifying Metered Supply Points. We conclude that a correct electricity demand forecast for a consumer with different types of consumption does not need submetering, but characterising Metered Supply Points is an option with lower costs that allows for better predictions.

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The authors regret <To not have included during the publication process the acknowledgement to a project that has been part of this work and want to include in the acknowledgements part: This work was partially supported by the Grant TED2021-129722B-C31 (ALIVE-DER), funded by MCIN/AEI/10.13039/501100011033 and by “European Union NextGenerationEU/PRTR”. >. The authors would like to apologise for any inconvenience caused.

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Integration of renewable energy sources require an increase in the flexibility of power systems. Demand response is a valuable flexible resource that is not currently being fully exploited. Small and medium industrial consumers can deliver a wide range of underused flexibility resources associated with the electricity consumption in their production processes. Flexible resources should compete in liberalized operation markets to ensure the reliability of the system at a minimum cost. This paper presents a new tool to assist industrial demand response to participate in operation markets and optimize its value. The tool uses a combined physical-mathematical modelling of the industrial demand response and a Parallel Particle Swarm Optimization algorithm specifically tuned for the proposed problem to maximize the profit. The main advantages of the proposed tool are demonstrated in the paper through its application to the participation of a meat factory in the Spanish tertiary reserve market during a whole year using a quarter-hourly time resolution. The enhanced performance of the proposed tool with respect to previous methodologies is shown with these four flexible processes examples, where the maximum available profit obtained in the simultaneous consideration of all different flexible processes is computed. The flexible processes are technical and economically characterized in a way that makes the tool valid for most of the processes in the industry.@en

A transition to a sustainable energy system is essential. In this context, smart grids represent the future of power systems for efficiently integrating renewable energy sources and active consumer participation. Recently, different studies were performed that defined the conceptual architecture of power systems and their agents. However, these conceptual architectures do not overcome all issues for the development of new electricity markets. Thus, a novel conceptual architecture is proposed. The transactions of energy, operation services, and economic flows among the agents proposed are carefully analysed. In this regard, the results allow setting their activities’ boundaries and state their relationships with electricity markets. The suitability of implementing local electricity markets is studied to enforce competition among distributed energy resources by unlocking all the potential that active consumers have. The proposed architecture is designed to offer flexibility and efficiency to the system thanks to a clearly defined way for the exploitation of flexible resources and distributed generation. This upgraded architecture hereby proposed establishes the characteristics of each agent in the forthcoming markets and studies to overcome the barriers to the large deployment of renewable energy sources.@en