Abstract
This paper proposes a decision support model to define electricity consumers' remuneration structures when providing consumption flexibility, optimized for different load regimes. The proposed model addresses the remuneration of consumers when participating in demand response programs, benefiting or penalizing those who adjust their consumption when needed. The model defines dynamic remuneration values with different natures for the aggregator (e.g. flexibility aggregator or curtailment service provider) and for the consumer. The preferences and perspective of both are considered, by incorporating variables that represent the energy price, the energy production and the flexibility of consumers. The validation is performed using real data from the Iberian market, and results enable to conclude that the proposed model adapts the remuneration values in a way that it is increased according to the consumers' elastic, while being reduced when the generation is higher. Consequently, the model boosts the active consumer participation when flexibility is required, while reaching a solution that represents an acceptable g tradeoff between the aggregators and the consumers. (C) 2022 The Authors. Published by Elsevier Ltd.

Abstract
In the upcoming years, European countries have to make a strong bet on solar energy. Small photovoltaic systems are able to provide energy for several applications like housing, traffic and street lighting, among others. This field is expected to have a big growth, thus taking advantage of the largest renewable energy source existing on the planet, the sun. This paper proposes a computational model able to simulate the behavior of a stand-alone photovoltaic system. The developed model allows to predict PV systems behavior, constituted by the panels, storage system, charge controller and inverter, having as input data the solar radiation and the temperature of the installation site. Several tests are presented that validates the reliability of the developed model.

Abstract

Abstract
The local flexibility market models have emerged as a market-based solution to respond to the challenges that the increase in distributed energy resources caused in the power and energy systems. Using Smart Grid enabling technologies, consumers and prosumers are prepared to respond to any possible demand-side flexibility event. In this scope, this work presents an advanced bidding model for the prosumers/consumers' participation in a local flexibility market to solve existing issues in the local grid. The proposed advanced model consists of a single-sided auction-based clearing method where prosumer offers are ranked and chosen according to the price and other characteristics, such as their location and distance to the problem to be solved. The aim is to prioritize and select the offers that have a more positive impact on the situation to solve at the lowest possible cost. © 2022 IEEE.

Abstract
The growth of renewable energy sources usage at the local level contributes to decentralizing the power and energy systems. Nowadays, there is an increment of residential consumers becoming prosumers able to consume their generation or sell it to the public grid to reduce the electricity bill. This great penetration of electricity compromises the proper functioning of the system. Local electricity markets (LEM) are market platforms aimed at electricity end-users to be able to negotiate and transact it between them, thus becoming active players in the system, being a possible solution to balance local systems. Different approaches for LEM design and implementation are proposed in the literature, usually based on community markets and peer-to-peer. Despite their value, these solutions' scalability is compromised as these are centralized solutions, and processing can become very heavy. In this sense, this work proposes a blockchain-based distributed and decentralized optimal solution for implementing LEM. © 2022 IEEE.

Abstract
The integration of distributed energy resources contributes to accomplishing a balance between the supply and demand inside a local market. The operation of these markets is based on the peer-to-peer negotiations between users, whose cooperation leads to an increase in the social welfare of the community, thus creating a more user-centric market. This work fits in the context of the energy community, where members of a community can exchange energy in peer-to-peer transactions and use the public electricity grid as a backup. The market aims at maximizing the social welfare of the community considering the operational costs of all community members. A particle swarm optimization algorithm implemented in Python is used to solve the problem. © 2022 IEEE.