Abstract
Nascent entrepreneurship has long been studied from a variety of perspectives. A major stream of work by psychologists and sociologists suggests that nascent entrepreneurs have distinctive traits and competences. A second focus for research has been studying the environment in which nascent entrepreneurs operates. Recently, the identification and exploitation of entrepreneurial opportunities has emerged as a third focus. In this paper we will address the following questions: (1) what are the individual characteristics of those individuals who are attracted to becoming an entrepreneur? (2) What are the environmental factors contributing to new venture creation? (3) What are the steps in the creation process? We will attempt to answer these three questions by arguing that the central process of nascent entrepreneurship is centred on opportunity recognition, evaluation and exploitation, and influenced by contextual factors (e.g. external knowledge) and personal characteristics and competences (e.g. internal knowledge).

Abstract
This paper proposes a Building Energy Management System (BEMS) as part of an organization-based Multi-Agent system that models the Smart Home Electricity System (MASHES). The proposed BEMS consists of an Energy Management System (EMS) and a Prediction Engine (PE). The considered Smart Home Electricity System (SHES) consists of different agents, each with different tasks in the system. In this context, smart homes are able to connect to the power grid to sell/buy electrical energy to/from the Local Electricity Market (LEM), and manage electrical energy inside of the smart home. Moreover, a Modified Stochastic Predicted Bands (MSPB) interval optimization method is used to model the uncertainty in the Building Energy Management (BEM) problem. A demand response program (DRP) based on time of use (TOU) rate is also used. The performance of the proposed BEMS is evaluated using a JADE implementation of the proposed organization-based MASHES.

Abstract
This paper models the impacts of Demand Response Programs (DRPs) on the behavior of energy market participants in the electricity markets in the presence of renewable energies. In such oligopolistic environment, market interactions are considered by using a game theoretic model, and the market transactions are cleared by means of a Security Constraint Unit Commitment program (SCUC). One sample is considered from each main category of DRPs consisting of different types of time of use tariffs, real-time pricing, critical peak pricing from Price-Based Demand Response (PBDR), and different types of emergency demand response program tariffs from Incentive Based Demand Response (IBDR) in the presence of the wind farms. It is expected that the numerical results with the presence of renewable energy resources indicate that different types of these DRPs differently affect the oligopolistic behavior of market players that should be studied by the system operators before their implementation. Using Monte Carlo simulation method, several scenarios are generated to show the possible contingencies in Day-Ahead energy market. Then some scenario reduction methods are used for reduction the numbers of scenarios. Finally, a two-stage stochastic model is applied to solve this scheduling in a mixed-integer linear programming through GAMS. Consequently, the effect of demand response in the reduction of the operation cost is proved. The proposed approach is tested on a modified IEEE six-bus system.

Abstract
Hydrothermal systems should be characterized by a transmission-intensive nature in order to deal with climatic phenomena which, for example, can determine dry conditions in one region while there are large rainfalls in another one. Thus, the grid must be robust to deal with the different export/import patterns among regions and accommodate several economic dispatches. This paper describes a multiyear probabilistic Transmission Expansion Planning, TEP, model that uses Evolutionary Particle Swarm Optimization (EPSO) to deal with the uncertainties present in hydrothermal systems. The numerical simulations were conducted using the IEEE 24 bus reliability test system in which the planning horizon is 10 years and the load growth is 2,5% per year. The results highlight the importance of adopting expansion strategies to reduce the risk and consider the inflow variations in this type of systems.

Abstract
This paper addresses the location of parking lots (PLs) to be used for plug-in electric vehicles (PEVs) by using a probabilistic traffic model and taking into account the PL participation in electricity markets. The PLs are used both for grid-to-vehicle and vehicle-to-grid. The system includes private or public charging stations only used for PEV charging. The traffic model considers the partitioning of the territory into areas. The case study is based on traffic and market data referring to Italy.

Abstract