Abstract

Abstract
In an increasingly globalized world, companies face the challenge of expanding into foreign markets. In the internationalization process there are cultural, governmental, geographic and economic issues that can affect the success of the company in its implementation process overseas. Marketing strategies and the standardization or adaptation levels of each element of the marketing mix can be seen as critical success factors that directly affect business success in the new market. Companies must decide between adapting their marketing strategy to local markets or rather standardizing them globally. Some companies use as a competitive advantage the adaptation of their strategy (partially or totally) through knowledge of the culture of the country to which they are internationalized. Adaptation has been the key to success for a number of brands, and L'Oréal is one of them. This study intends to focus on the internationalization strategy and knowledge of the culture of the countries to which they are internationalized, applied to cultural differences. It also aims to highlight the importance of product innovation in consumer markets at present, and to analyse beauty satisfaction and tendencies amongst millennials.

Abstract
This paper presents a Support Vector Machine (SVM) based approach for energy consumption forecasting. The proposed approach includes the combination of both the historic log of past consumption data and the history of contextual information. By combining variables that influence the electrical energy consumption, such as the temperature, luminosity, seasonality, with the log of consumption data, it is possible for the proposed method by find patterns and correlations between the different sources of data and therefore improves the forecasting performance. A case study based on real data from a pilot microgrid located at the GECAD campus in the Polytechnic of Porto is presented. Data from the pilot buildings are used, and the results are compared to those achieved by several states of the art forecasting approaches. Results show that the proposed method can reach lower forecasting errors than the other considered methods.

Abstract
This paper proposes a model based on particle swarm optimization to aid electricity markets players in the selection of the best player(s) to trade with, to maximize their bilateral contracts outcome. This approach is integrated in a Decision Support System (DSS) for the pre-negotiation of bilateral contracts, which provides a missing feature in the state-of-art, the possible opponents analysis. The DSS determines the best action of all the actions that the supported player can take, by applying a game theory approach. However, the analysis of all actions can easily become very time-consuming in large negotiation scenarios. The proposed approach aims to provide the DSS with an alternative method with the capability of reducing the execution time while keeping the results quality as much as possible. Both approaches are tested in a realistic case study where the supported player could take almost half a million different actions. The results show that the proposed methodology is able to provide optimal and near-optimal solutions with an huge execution time reduction.

Abstract
This paper presents recent research conducted at the JRC Smart Grid Interoperability Lab and analyses key parameters that should be taken into consideration for the development of interoperable and sustainable electricity systems. Increasing energy efficiency aims at reducing the overall energy consumption and consequently lower the stress on the environment by using less energy. The first research activity illustrated is on the use of Advanced Metering Infrastructure as a gateway to improve Demand Response/Demand Side Management. The second one focuses on the use of photovoltaic in a low voltage distribution network and studies the effect of penetration in voltage unbalances. The last one addresses the power quality performance of electric vehicle chargers under low temperature conditions and provides hints for improvements. The paper underlines several factors that could affect the efficiency of systems towards making improvements that increase the stability of the relevant operations.

Abstract
This paper addresses the problem faced by an aggregator of small prosumers, when participating in the energy market. The aggregator exploits the flexibility of prosumers' appliances, in order to reduce its market net costs. Two optimization procedures are proposed. A two-stage stochastic optimization model to support the aggregator in the definition of demand and supply bids. The aim is to minimize the net cost of the aggregator buying and selling energy at day-ahead and real-time market stages. Scenario-based stochastic programing is used to deal with the uncertainty of electricity demand, end-users' behavior, outdoor temperature and renewable generation. The second optimization is a model predictive control method to set the operation of flexible loads in real-time. A case study of 1000 small prosumers from the Iberian market is used to compare four day-ahead bidding strategies and two real-time control strategies, as well as the performance of combined day-ahead and real-time strategies. The numerical results show that the proposed strategies allow the aggregator to reduce the net cost by 14% compared to a benchmark typically used by retailers (inflexible strategy).