Abstract
The power and energy sector transition into decentralized and distributed models brings a set of challenges that need to be overcome. Among them is consumer empowerment, which requires secure economic transactions and reliable system operation. Thus, new technologies and procedures such as blockchain have gained prominence, due to their reliability and privacy-preserving capabilities. These characteristics are essential for the proliferation of energy community markets. Therefore, this paper provides an overview of blockchain technology applicability to consumer-centric electricity markets, highlighting existing projects and initiatives. Additionally, key enablers and barriers to blockchain deployment in local electricity markets are discussed, followed by a roadmap for the comprehensive adoption of such technology.

Abstract

Abstract
Building renewable energy communities (REC) involves investments on generation facilities (such as PV panels), technologies to provide flexibility (such as batteries), management platforms and ICT systems, as well as integrating other flexibility sources such as thermal storage or electric vehicles. The way investments are made by the REC's members and other third parties is in close relationship with the governance models of the REC in terms of energy, flexibility and costs and benefits sharing, which, in the end, constitute the overall REC's business model. This works provides a revision of the main financing mechanisms to invest on and build a REC, and of the associated governance and business models that result from the investments mechanisms selected and its implications on its day by day operation. © 2022 IEEE.

Abstract
This paper proposes an energy community management system for local energy sharing with grid flexibility services to solve the potential grid constraints of the local distribution network. A three-stage model is proposed. Stage 1 is the individual minimization of the energy bill of each prosumer by optimizing the schedules of its battery. The second stage optimizes the energy bill of the energy community by sharing internally the prosumers energy surplus and re-dispatching their batteries, while guaranteeing that each new individual prosumer energy bill is always equal or less than its stage 1 bill. The third stage is performed by the DSO to solve the grid constraints by re-dispatching the batteries, curtailing local generation or reducing consumption. Stage 3 minimizes the impact on stage 2 by minimizing the loss of profit or utility of every prosumer which is compensated accordingly.

Abstract
This paper addresses the design, development and experimental tests of a prototype of fuel gas generation system based on biomass gasification for small-scale applications, around 5?kW. It comprises the small scale downdraft gasifier and the gas cleaning system aiming to clean-up the producer gas to be used in the upstream Internal Combustion Engine (ICE). The design of the downdraft gasifier prototype follows the methodologies that have been reported on the available literature. However, since these methodologies apply to gasifiers with larger rated powers, the adopted methodology is based on the extrapolation of the main parameters used for larger gasifiers design. For runing the ICE the producer gas requires to have a specific gas composition with an acceptable range of impurities. Therefore, a clean-up system was proposed following three stages: in first instance a hot gas clean-up using a cyclone designed to eliminate particles and compounds; then a heat exchanger was used for cooling the gas to condensate tars and water; finally a cold gas clean-up is performed by filtration using two filter steps: the first one using organic material (biomass) and the second one using a polypropylene cartridge filter. Experimental tests were performed using the developed imbert downdraft gasifier prototype, using pellets as feedstock. The preliminary results allow verifying several drawbacks that will difficult an effective integration of the developed prototype for small scale power generation applications based on ICE using low density feedstock.

Abstract
In this paper, for the data set of the Iberian Electricity Market for the period 1 January 2015 to 30 June 2019, 19 different models are considered from econometrics, statistics, and artificial intelligence to explain how electricity markets work. This survey allows us to obtain a more complete, critical view of the most cited models. The machine learning models appear to be very good at selecting the best explanatory variables for the price. They provide an interesting insight into how much the price depends on each variable under a nonlinear perspective. Notwithstanding, it might be necessary to make the results understandable. Both the autoregressive models and the linear regression models can provide clear explanations for each explanatory variable, with special attention given to GARCHX and LASSO regression, which provide a cleaner linear result by removing variables that have a minimal linear impact.