Investigating the social efficiency of merchant transmission planning through a non-cooperative game-theoretic framework

Merchant transmission planning is considered as a further step towards the full liberalization of the electricity industry. However, previous modeling approaches do not comprehensively explore its social efficiency as they cannot effectively deal with a large number of merchant companies. This paper addresses this fundamental challenge by adopting a novel non-cooperative game-theoretic approach. Specifically, the number of merchant companies is assumed sufficiently large to be approximated as a continuum. This allows the derivation of mathematical conditions for the existence of a Nash Equilibrium solution of the merchant planning game. By analytically and numerically comparing this solution against the one obtained through the traditional centralized planning approach, the paper demonstrates that merchant planning can maximize social welfare only when the following conditions are satisfied: a) fixed investment costs are neglected and b) the network is radial and does not include any loops. Given that these conditions do not generally hold in reality, these findings suggest that even a fully competitive merchant transmission planning framework, involving the participation of a very large number of competing merchant companies, is not generally capable of maximizing social welfare

Opportunities and restrictions for the local-endogenous development in metropolitan areas of high industrial concentration: the case of Thriasio Pedio in Attica

This paper investigates the development pattern of the urban area of Thriasio Pedio in the metropolitan region of Attica, which is characterised by a high concentration of industrial activities. The local-endogenous development model is discussed in the theoretical review of the paper, in the sense of the local socioeconomic system’s capacity to transform, react to external challenges, promote awareness and import specific forms of social regulation at the local level.On this ground, the main question of the paper concerns the nature of the area’s development and more specifically, whether or not this is defined by endogenous factors (i.e. the operation of locally embedded production systems) along with predetermined exogenous factors (i.e. the allocation of central/metropolitan activities in Thriasio Pedio). The study is supported by the results of a sampling research in representative economic units of the Thriasio Pedio area. The analysis helped us to see whether the various applications of the local-endogenous development pattern, as defined in the paper, are incorporated into the overall productive system of the area. The prerequisites for the reinforcement of the local endogenous capacity were also identified in this analysis

Sustainable and balanced development of insular space: The case of Greece

Islands, as unique ecosystems, are characterized by a particular sensitivity that affects all the fundamental dimensions of the sustainable development. Thus, highlighting the particularities of the operation of these ecosystems is of considerable importance. This helps to determine the factors and limitations of stability regarding insular ecosystems. All these issues are analysed in this paper. Since they are related to a case study of the Greek insular space, the formulation of a strategic framework for the sustainable and balanced development of the islands is discussed. This strategy is structured by specifi c priority axes that aim at tackling limitations and expanding the production base, as well as social development. The policies and mechanisms put in place in order to facilitate these priorities are presented too, as well as the necessary supportive and institutional actions. © 2009 John Wiley & Sons, Ltd and ERP Environment

A machine learning approach for generating and evaluating forecasts on the environmental impact of the buildings sector

The building sector has traditionally accounted for about 40% of global energy-related carbon dioxide (CO2) emissions, as compared to other end-use sectors. Due to this fact, as part of the global effort towards decarbonization, significant resources have been placed on the development of technologies, such as active buildings, in an attempt to achieve reductions in the respective CO2 emissions. Given the uncertainty around the future level of the corresponding CO2 emissions, this work presents an approach based on machine learning to generate forecasts until the year 2050. Several algorithms, such as linear regression, ARIMA, and shallow and deep neural networks, can be used with this approach. In this context, forecasts are produced for different regions across the world, including Brazil, India, China, South Africa, the United States, Great Britain, the world average, and the European Union. Finally, an extensive sensitivity analysis on hyperparameter values as well as the application of a wide variety of metrics are used for evaluating the algorithmic performance