Abstract
Several countries have a significant installed capacity of large-scale reversible hydro power plants. This large-scale storage technology comes with high investments costs, hence the constant search for methods to increase and diversify the sources of revenue. Traditional fixed speed pump storage units typically operate in the day-ahead market to perform price arbitrage and, in specific cases, provide downward replacement reserve (RR). Variable speed pump storage can not only participate in RR but also contribute to frequency restoration reserve (FRR), given their ability to control its operating point in pumping mode. This work proposes a strategy to manage the water resource and maximize the power plant revenue by participating in the day ahead market but also providing ancillary services. Moreover, a model to correctly allocate the water resource throughout the year is presented, as well as an evaluation module to calculate the real revenue of the system.

Abstract
Demand aggregators are new market players that represent a group of consumers in the electricity market. This paper proposes an aggregator model responsible for gathering residential and commercial consumers, which has the role of managing their flexible consumption in the day-ahead electricity market. A methodology to optimize the aggregator's bids is also presented. It optimizes the scheduling of the flexible loads taking simultaneously into account the consumers' preferences and temporal trajectories of forecasted outdoor temperatures and electricity prices. The proposed methodology was tested using a case study with 200 residential and commercial consumers from the Iberian market.

Abstract
With the liberalization of the electricity markets, price forecasting has become crucial for the decision-making process of market agents. The unique features of electricity price, such as non-stationary, non-linearity and high volatility make this a very difficult task. For this reason, rather than a simple point forecast, market participants are more interested in a probabilistic forecast that is essential to estimate the uncertainty involved in the price. By focusing on this issue, the aim of this paper is to analyze the impact of external factors in the electricity price and present a methodology for probabilistic forecasting of day-ahead electricity prices from the Iberian electricity market. The models are built using regression techniques and aim to obtain, for each hour, the quantiles of 5% to 95% by steps of 5%.

Abstract
This paper discusses the potential use of probabilistic wind power forecasting in electricity markets, with focus on the scheduling and dispatch decisions of the system operator. We apply probabilistic kernel density forecasting with a quantile-copula estimator to forecast the probability density function, from which forecasting quantiles and scenarios with temporal dependency of errors are derived. We show how the probabilistic forecasts can be used to schedule energy and operating reserves to accommodate the wind power forecast uncertainty. We simulate the operation of a two-settlement electricity market with clearing of day-ahead and real-time markets for energy and operating reserves. At the day-ahead stage, a deterministic point forecast is input to the commitment and dispatch procedure. Then a probabilistic forecast is used to adjust the commitment status of fast-starting units closer to real time, on the basis of either dynamic operating reserves or stochastic unit commitment. Finally, the real-time dispatch is based on the realized availability of wind power. To evaluate the model in a large-scale real-world setting, we take the power system in Illinois as a test case and compare different scheduling strategies. The results show better performance for dynamic compared with fixed operating reserve requirements. Furthermore, although there are differences in the detailed dispatch results, dynamic operating reserves and stochastic unit commitment give similar results in terms of cost. Overall, we find that probabilistic forecasts can contribute to improve the performance of the power system, both in terms of cost and reliability. Copyright (c) 2012 John Wiley & Sons, Ltd.

Abstract
In this paper, we analyze how demand dispatch combined with the use of probabilistic wind power forecasting can help accommodate large shares of wind power in electricity market operations. We model the operation of day-ahead and real-time electricity markets, which the system operator clears by centralized unit commitment and economic dispatch. We use probabilistic wind power forecasting to estimate dynamic operating reserve requirements, based on the level of uncertainty in the forecast. At the same time, we represent price responsive demand as a dispatchable resource, which adds flexibility in the system operation. In a case study of the power system in Illinois, we find that both demand dispatch and probabilistic wind power forecasting can contribute to efficient operation of electricity markets with large shares of wind power.

Abstract
The system operator is responsible for maintaining a constant balance between generation and load to keep frequency at the nominal value. This fundamental objective is achieved with upward (e.g., synchronized and nonsynchronized generation units) and downward (e.g., demand response, storage) reserve capacity. The system operator needs to define, in advance, the reserve capacity requirements that mitigate the risk of imbalances due to forecast errors and unplanned outages of generation units. The research trend is to apply probabilistic methodologies for setting the reserve requirements based on uncertainty forecasts for renewable generation and load, as well as a probabilistic modeling of units' outages. This chapter describes two probabilistic methods, which share a common modeling framework, for quantifying risk and reserve requirements in two types of electricity markets: (1) sequential markets with the reserves market after the energy market clearing and (2) cooptimization (or joint market clearing) of energy and reserves. Two case studies with real data are presented to illustrate the application of both methodologies.