Abstract
The European Foundation for Quality Management (EFQM) 2020 model is a comprehensive and updated business model that encompasses sustainability and shares features with Industry 4.0, emphasizing transformation and improved organizational performance, yet with different theoretical and practical foundations. This research highlights the EFQM 2020 model's novelties and its relationships/implications with the Industry 4.0 paradigm, contributing to the Quality 4.0 body of knowledge. Several linkages between the EFQM 2020 model and Industry 4.0 have been identified, namely, at the criteria level and guidance points, which can support successful digital transformation by combining quality and excellence with Industry 4.0. However, given the model's generic and non-prescriptive nature, there is no specific reference to the nine Industry 4.0 pillars. Additionally, the links between direction and organizational culture and leadership criteria and driving performance and transformation are not evident, which might be a concern for business and technology transformation strategies. Managing knowledge, skills, and capabilities is critical for the successful adoption of Industry 4.0. The EFQM model adds a strategic and technologically unbiased perspective to Industry 4.0, providing an integrated business excellence framework for Quality 4.0. With empirical support of the model application, future research is recommended to develop this subject further.

Abstract
Due to the complexity and great relevance of the transmission network expansion planning (TNEP) for electrical systems, this topic remains on the focus of the academic and industry communities. Therefore, this paper proposes a new approach to deal efficiently with the basic formulation of this problem, combining low computational effort and good quality of the obtained solutions. In this approach four factors contribute to solve TNEP problem more efficiently: (i) the investment decisions are selected using a new Constructive Heuristic Algorithm (CHA); (ii) the proposed CHA includes two stages, using the relaxation of the decision integers variables through the hyperbolic tangent function and the setting of its function's slope; (iii) the performance index that was adopted was modified regarding what was reported in the literature; (iv) the use of the primal-dual interior point optimization technique allows the representation of the nonlinearities in the problem: transmission power losses and the hyperbolic tangent function (investment decision). The quality and effectiveness of the proposed algorithm is verified using two real power systems, where the proposed CHA is able to lead to better quality solutions than the ones reported in the literature.

Abstract

Abstract
The active participation of small-scale prosumers and consumers with demand-response capability and renewable resources can be a potential solution to the environmental issues and flexibility-related challenges. Local energy markets based on peer-to-peer trading is defined as one of solutions to exploit the maximum flexibility potential of prosumers. However, the existing literature that proposed peer-to-peer based local energy markets did not lead to respecting the peers' energy trading preferences simultaneously in the profitable market settlement. To solve this issue, a new local energy market model is presented in which network users can trade with their preferred trading partners within the local market as well as the grid. The proposed trading model includes two levels to consider both the democracy and the profitability of energy trading. At the first level, the model considers the trading preferences of each player to respect the peers' choices. The second level matches the rest of the bids and offers of the local buyers and sellers aiming to maximize the social welfare of all of the players participating in the local market. Our proposed local market is implemented for a test system consisting of fifteen residential players, and the results are compared to other trading models through different comparison criteria such as social-welfare of all players and the net cost of each individual player from consuming electricity. According to the results, the proposed model stands in the second rank compared to the other models that do not simultaneously consider preferences and social welfare of the peers, in terms of social welfare, total profits of the players, and the sustainability and liquidity-based criteria. The proposed model achieves 1416-Cent as the total net energy costs of all peers and the total accepted blocks equaling 76. This means that the proposed local market model can still be profitable and liquid while respecting the players' trading preferences and choices.

Abstract

Abstract
The participation of household prosumers in wholesale electricity markets is very limited, considering the minimum participation limit imposed by most market participation rules. The generation capacity of households has been increasing since the installation of distributed generation from renewable sources in their facilities brings advantages for themselves and the system. Due to the growth of self-consumption, network operators have been putting aside the purchase of electricity from households, and there has been a reduction in the price of these transactions. This paper proposes an innovative model that uses the aggregation of households to reach the minimum limits of electricity volume needed to participate in the wholesale market. In this way, the Aggregator represents the community of households in market sales and purchases. An electricity transactions portfolio optimization model is proposed to enable the Aggregator reaching the decisions on which markets to participate to maximize the market negotiation outcomes, considering the day-ahead market, intra-day market, and retail market. A case study is presented, considering the Iberian wholesale electricity market and the Portuguese retail market. A community of 50 prosumers equipped with photovoltaic generators and individual storage systems is used to carry out the experiments. A cost reduction of 6-11% is achieved when the community of households buys and sells electricity in the wholesale market through the Aggregator.