Abstract
The advances in distribution system automation, smart metering and exploitation of microgrids call for the adoption of more flexible options for power flow calculation. This paper illustrates the structure of a tool implemented to deal with time-dependent and uncertain data, as well as changing network structures during time. The focus is then set on including data uncertainty in probabilistic power flow calculations extended over a time period, in which the changing load and generation patterns are generally represented by correlated random variables. Exemplificative results are shown for a case study application.

Abstract
In this paper a novel scenario generation methodology based on artificial neural networks (ANNs) is proposed. The methodology is flexible and able to generate scenarios for various stochastic variables that are used as input parameters in the stochastic short-term scheduling models. Appropriate techniques for modeling the cross-correlation of the involved stochastic processes and scenario reduction techniques are also incorporated into the proposed approach. The applicability of the methodology is investigated through the creation of electric load, photovoltaic (PV) and wind production scenarios and the performance of the proposed ANN-based methodology is compared to time series-based scenario generation models. Test results on the real-world insular power system of Crete and mainland Greece present the effectiveness of the proposed ANN-based scenario generation methodology.

Abstract
The integration of renewable energy sources (RESs) cannot be postponed given the several techno-economic and environmental factors. However, the intermittent nature of RESs brings several challenges because such sources introduce significant operational variability and uncertainty in the system. Framed in this context a new stochastic mathematical planning model is proposed considering the placement and sizing of energy storage systems and reactive power sources that altogether enable high penetration of RESs. The model employs a linearized AC model, balancing well accuracy with computational burden. The model is tested on a standard distribution network system. Numerical results show that the simultaneous integration of energy storage systems and reactive power sources largely leads to a substantially increased penetration of RESs in distribution systems. Furthermore, dramatic reductions in losses, system costs and emissions are observed. Results analysis also reveal the positive contribution of such a planning on the overall voltage stability in the system.

Abstract
The introduction of smart end-users is a vital point towards the smart grid vision. In this respect, smart households have drawn significant attention recently. Home energy management systems (HEMS) in smart households and transformer energy management units have different objectives that can sometimes be conflicting. Thus, a novel interactive optimum operating strategy that considers all possible aspects in each smart household together with internal bi-directional power flows within the household structure from economic (minimization of household daily electricity usage costs) and technical (avoidance of transformer overloading) perspective is proposed in this study. To the best knowledge of the authors, this is the first study in the literature combining all of the possible operational possibilities in HEMS of smart households together with internal household bi-directional power flows and the technical limitations of a transformer unit serving a neighborhood of smart households under a mixed-integer linear programming (MILP) framework.

Abstract
Although wind power generation has extended a maturity in technology, there are still many concerns regarding the optimal support of regulatory bodies for renewable resources. In this context, the regulatory body should form a market structure or consider market rules and regulations to not only attract investors to renewable power plants, but also provide an efficient market that reflects a safe and clear competition environment. In this paper, an agent-based game-theoretic model is developed to investigate the electricity market behavior under oligopoly circumstances. The proposed model reveals the potential of collusive and strategic behavior of market participants. By employing the proposed model, impacts of different supportive schemes on the behavior of the wind power producer and conventional thermal units are investigated. According to the results obtained, if the regulatory bodies do not consider strategic collusion of market participants, adverse consequences for wind power producers might happen in the long-term horizon. © 2016 IEEE.

Abstract
A coordinated offering strategy between a wind farm and a reversible hydro plant can reduce wind power imbalances, improving the system efficiency whilst decreasing the total imbalances. A stochastic mixed integer linear model is proposed to maximize the profit and the future water value FWV of the system using Conditional Value at Risk (CVaR) for risk-hedging. The offer strategies analyzed are: (i) single wind-reversible hydro offer with a physical connection between wind and hydro units to store spare wind energy, and (ii) separate wind and reversible hydro offers without a physical connection between them. The effect of considering the FWV of the reservoirs is studied for several time horizons: one week (168 h) and one month (720 h) using an illustrative case study. Conclusions are duly drawn from the case study to show the impact of FWV in the results.