Abstract
Energy communities and self-consumption are considered as major drivers to achieve the decarbonization of the power sector, contributing to further increase the penetration of renewables, and empowering end consumers by turning them more active and flexible regarding electricity consumption. Considering the relevance of this topic and given that the corresponding legislation is recent, this paper summarizes the European Regulation on self-consumption and renewable energy communities and describes, analyses and compares its transposition to the Portuguese and Spanish national regulations. The main regulatory barriers for renewable energy communities' rollout are identified and regulatory improvements are proposed to overcome them.

Abstract
Transmission Expansion Planning (TEP) aims at identifying a list of new assets to be installed on the transmission grid to meet the long-term forecasted demand while ensuring a safe supply over the entire planning horizon. As TEP is a Mixed Integer Non-Linear Problem (MINLP) with a huge search space, in the last years several modern heuristic algorithms were proposed to deal with its challenging characteristics. In this way, this paper describes and evaluates the impact and implementation of four operators that can be easily incorporated in any evolutionary algorithm, namely: Neighborhood Search for Local Improvement (NSLI), Diversity Control (DC), Elitist Reproduction (ER) and Boundary Local Search (BLS). The impact of these operators is assessed and discussed over a hundred simulations using a traditional Genetic Algorithm (GA) and a well-known test system, the RTS 24-bus. Regarding the results, the NSLI and the BLS operator considerably improved the GA performance in solving the TEP problem regarding both the final value of the objective function and the diversity of solutions.

Abstract
One of the major barriers for the retailers is to understand the consumption elasticity they can expect from their contracted demand response (DR) clients. The current trend of DR products provided by retailers are not consumer-specific, which poses additional barriers for the active engagement of consumers in these programs. The elasticity of consumers' demand behavior varies from individual to individual. The utility will benefit from knowing more accurately how changes in its prices will modify the consumption pattern of its clients. This work proposes a functional model for the consumption elasticity of the DR contracted consumers. The model aims to determine the load adjustment the DR consumers can provide to the retailers or utilities for different price levels. The proposed model uses a Bayesian probabilistic approach to identify the actual load adjustment an individual contracted client can provide for different price levels it can experience. The developed framework provides the retailers or utilities with a tool to obtain crucial information on how an individual consumer will respond to different price levels. This approach is able to quantify the likelihood with which the consumer reacts to a DR signal and identify the actual load adjustment an individual contracted DR client provides for different price levels they can experience. This information can be used to maximize the control and reliability of the services the retailer or utility can offer to the System Operators. (c) 2021 Published by Elsevier B.V.

Abstract
Fundamental electricity market models tend to underestimate the real market prices because they do not properly represent the real variable production cost of the generation units, nor the strategic markup that generation companies add to their costs to price the offered energy. This markup can increase bid prices above the marginal cost of the generation units, which may leave bids out of the market, decreasing the total cleared production, but increasing the final market price. This paper proposes a simple procedure, based on the real market outcomes, to estimate these markups and improve CEVESA MIBEL market model by reducing the gap between the simulated and the real market prices.

Abstract
This paper presents a heuristic algorithm to reduce the set of equipment candidates for Transmission Expansion Planning (TEP). Since it is a Constructive Heuristic Algorithm (CHA), the MiniEff algorithm aims at reducing the computational burden involved in the optimization process in a quick and satisfactory way. This approach includes two major blocks. The first one uses the minimum-effort calculation based on DC-OPF analysis to reduce the search space of the TEP problem. Then, the reduced list of investment alternatives is input to the AC-TEP formulation to build the final expansion plan using the Evolutionary Particle Swarm Optimization technique (EPSO). The tests on the developed TEP approach were done using the IEEE 118 Bus System and they demonstrate the gains that were obtained in terms of reducing the computer burden in solving TEP without compromising the quality of the final plans.

Abstract
This paper proposes a real time Walrasian based market design for local electricity trading, considering the roles of the different players, the settlement procedures, and the necessary balance responsibilities with the wholesale market under collective self-consumption rules. A Walrasian mechanism based on consecutive auctions for very short delivery periods is proposed, where the auctioneer defines a price for each of these delivery periods to which peers react by generating and consuming accordingly and informing if they trade with the auctioneer or with their retailer or aggregator. This market has no energy purchase contracts, and energy is billed based on each peer's generation or consumption for each delivery period with the price defined by the auctioneer. © 2022 IEEE.