This paper aims to present a methodology for optimizing the customers’ portfolio of an aggregator by analysing the compatibility between prosumers (clients). The methodology evaluates two main components: energy and socioeconomic. With this, the intention is to maximise the economic benefits of the aggregator and to establish a personalised tariff according to the user profile. Since this figure was conceived, efforts have been mainly aimed at meeting only and exclusively the energy demand requirements without taking into consideration prosumers socio-economic profiles and the synergies among them. This paper, therefore, presents a methodology in which a compatibility index based on the energy and socioeconomic profile of each client is defined. In addition, the application of this methodology to a practical case is presented.

New demand-side management models have emerged as a result of rising energy prices, the development of artificial intelligence, and the rise of prosumers. The purpose of this research is to use deep learning techniques to predict the energy production and demand of a prosumer network to determine dynamic prices for the local market. Gated Recurrent Units (GRU) and Long Short-Term Memory (LSTM) were two methods that were taken into consideration for forecasting consumer demand and wind and solar energy generation. Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) were used to compare the various approaches. The results demonstrated that GRU, with 0.0273, 0.0158, and 49.8 in RMSE, MAE, and MAPE respectively, is the best method for predicting energy generation and consumption in our datasets. Demand management system dynamic prices were calculated on an hourly basis using input from energy generation and demand forecasts. Finally, an optimization method was developed for establishing the energy planning.

This research is a state of the art of the main barriers and solutions that can be found in the implementation of energy efficiency measures and methods in industry, such as Energy Audits or Energy Management Systems (EMS). It aims to bring together in a single document the lessons learned from the efforts of the European Union over the past decade to increase energy efficiency in industry. The article catalogues and analyses 20 legal, technical, economic, cultural and organisational barriers that are still present today and as an added value it provides with a table of multiple, accessible and current solutions as well as with potential improvement pathways to overcome each of them. The ultimate purpose of this work is to stablish a higher starting point, with more awareness and available solutions, from which to start in order to accelerate the decarbonisation of the industry and subsequently be able to achieve the latest and more ambitious objectives set in the ‘Fit for 55’ package in the EU.

Energy communities represent a new energy management model for the use of local energy resources, in which different social actors participate in search of energy, environmental, social and economic benefits. Nevertheless, there is still a lack of concise guidelines in the literature dealing with the more technical aspects of energy communities. The aim of this article is presenting a methodology to support the design of new energy communities based on energetic, economic and environmental conditions in order to analyse their technical and economic viability. The methodology is supported by Homer Grid modelling software for simulating and optimising possible energy scenarios. This is better suited to energy communities connected to the grid, with renewable energy generation assets from local resources and with an equitable distribution of benefits for all members. The methodology has been applied to a case study in the village of Vinalesa, sizing the community's generation assets as well as the electricity tariff. Their implementation in the case study results in an installation of 870 kW of photovoltaic energy and capacity storage of 1.08 MWh in Lithium-Ion batteries that allows the residents of Vinalesa have additional savings of around 16.4% in their electricity bill.