Abstract
In this study a novel two-stage stochastic programming based day-ahead joint energy and reserve scheduling model is developed. Demand-side as a reserve resource is explicitly modeled through responsive load aggregations, as well as large industrial consumers that directly participate in the scheduling procedure. Furthermore, a risk-hedging measure is introduced, namely the Conditional Value-at-Risk (CVaR), to analyze the behavior of energy and reserve scheduling by both the generation and the demand-side for a risk-averse ISO. The proposed methodology is tested on the practical non-interconnected insular power system of Crete, Greece, which is characterized by a significant penetration of Renewable Energy Sources (RES).

Abstract
The high penetration of wind and photovoltaic power in electricity markets will represent a major challenge in the forthcoming years. The main problem of both technologies is the high uncertainty in their production and their dependence on environmental conditions. The coordination between wind and photovoltaic power aims to lower imbalances, reducing their associated penalties. This paper describes two strategies: i) separate wind and photovoltaic strategy and ii) single wind-photovoltaic strategy. The strategies proposed are solved through stochastic mixed integer linear programming. The expected profits are maximized and they are obtained by selling the energy in the day-ahead market. The imbalances are penalized in the balancing market as well. The model is tested for a week, 168 hours, and the data used come from the Spanish electricity market. The results of the case study are discussed, comparing both strategies. Following the discussion, the most important conclusions are presented.

Abstract
The actual electric grid was developed to offer electricity to the clients from centralized generation, so with large-scale distributed renewable generation there is an urgent need for a more flexible, reliable and smarter grid. The wireless technologies are becoming an important asset in the smart grid, particularly the ZigBee devices. These smart devices are gaining increased acceptance, not only for building and home automation, but also for energy management, efficiency optimization and metering services, being able to operate for long periods of time without maintenance needs. In this context, this paper provides new comprehensive field tests using open source tools with ZigBee technologies for monitoring photovoltaic and wind energy systems, and also for building and home energy management. Our experimental results demonstrate the proficiency of ZigBee devices applied in distributed renewable generation and smart metering systems.

Abstract
In the last few years, the efforts made by the scientific community to present efficient and effective tools to forecast the behaviour of wind power have been significant. These techniques in forecasting aim to help wind producers face the competition in deregulated electricity markets, and in this field the short-term tools are one line of defence for all market players to make acceptable bids, ensuring the robustness and reliability of the electricity system. This paper aims to present a review of the state-of-art reported by the scientific community over the last few years. This is based on forecasting wind power approaches in the short-term, in different regions of the world, with a particular focus on statistical, hybrid, intelligent, and time-series approaches, and reports on the most significant findings reported in journals within this field of knowledge.

Abstract
Insular power systems represent an asset and an excellent starting point for the development and analysis of innovative tools and technologies. The integration of renewable energy resources that has taken place in several islands in the south of Europe, particularly in Portugal, has brought more uncertainty to production management. In this work, an innovative scheduling model is proposed, which considers the integration of wind and solar resources in an insular power system in Portugal, with a strong conventional generation basis. This study aims to show the benefits of increasing the integration of renewable energy resources in this insular power system, and the objectives are related to minimizing the time for which conventional generation is in operation, maximizing profits, reducing production costs, and consequently, reducing greenhouse gas emissions.

Abstract
This paper presents a solar power forecasting scheme, which uses spatial and temporal time series data along with a photovoltaic (PV) power conversion model. The PV conversion model uses the forecast of three different variables, namely, irradiance on the tilted plane, ambient temperature, and wind speed, in order to estimate the power produced by a PV plant at the grid connection terminals. The forecast values are obtained using a spatio-temporal method that uses the data recorded from a target meteorological station as well as data of its surrounding stations. The proposed forecasting method exploits the sparsity of correlations between time series data in a collection of stations. The performance of both the PV conversion model and the spatio-temporal algorithm is evaluated using high-resolution real data recorded in various locations in Italy. Comparison with other benchmark methods illustrates that the proposed method significantly improves the solar power forecasts, particularly over short-term horizons.