Abstract
This. paper is on the problem of short-term hydro scheduling. We propose a method, based on nonlinear programming, which considers hydroelectric power generation as a nonlinear function of water discharge and of the head. The main contribution of this paper is that the maximum power generation is also considered as head-dependent in order to obtain more realistic and feasible results. The proposed method has been applied successfully to solve a case study based on one of the Portuguese cascaded hydro systems, providing a higher profit at an acceptable computation time in comparison with classical optimization methods based on linear programming that ignore head dependence.

Abstract
A study was made on the problem of short-term operational planning for hydroelectric power systems. Consideration was given to multiple reservoirs in a cascade and the effect that the variation of the head has in the operating efficiency. This non-linear effect coupled with the cascaded hydro configuration tends to produce a complex problem of large dimension. For its resolution an optimization method based on nonlinear programming is considered. The results are compared with the method currently used based on linear programming. The results of the computational simulation show that the non-linear programming is the most suitable optimization method. " 2006 Centro de Informatión Tecnológica.

Abstract
This paper proposes neural networks in combination with wavelet transform for short-term electricity prices forecasting. In the new deregulated framework, producers and consumers require short-term price forecasting to derive their bidding strategies to the electricity market. Accurate forecasting tools are required for producers to maximize their profits and for consumers to maximize their utilities. The accuracy of the price forecasting attained with the proposed approach is thoroughly evaluated, reporting the numerical results from a real-world case study based on the electricity market of mainland Spain. © 2010 CRL Publishing Ltd.

Abstract
This paper analyzes the problem of short-term operational planning of thermoelectric power systems with environmental constraints. The growing worldwide environmental concern to limit emission of greenhouse gases led to the Kyoto Protocol, introducing new constraints in the operation of thermoelectric power systems. The traditional tools that aid decision making must be adapted to this new framework. The problem consist of deciding the commitment of the units and the optimal generation profile for each thermal unit committed throughout the time horizon. For its solution, an optimization method based on dynamic programming and non-linear programming is considered, building a new decision support system in Fortran language which simultaneously considers fuel costs and emissions. The results of the computational simulation applied to a practical case are show and discussed. © 2007 Centro de Información Tecnológica.

Abstract
The intermittence of the renewable sources due to its unpredictability increases the instability of the actual grid and energy supply. Besides, in a deregulated and competitive framework, producers and consumers require short-term forecasting tools to derive their bidding strategies to the electricity market. This paper proposes a novel hybrid computational tool, based on a combination of evolutionary particle swarm optimization with an adaptive-network-based fuzzy inference system, for wind power forecasting and electricity prices forecasting in the short-term. The results from two real-world case studies are presented, in order to illustrate the proficiency of the proposed computational tool.

Abstract
With the increasing penetration of distributed generations (DGs) in three-phase distribution networks, the traditional radial passive distribution networks are gradually transformed into active distribution networks (ADNs) with DGs, accompanied by a dramatic increase in operational scenarios. To fast analyze the massive scenarios of ADNs, this brief proposes a multi-scenario-based power flow analytical method based on the multi-dimensional holomorphic embedding method. First, DGs are partitioned according to their spatial correlation, and then a power flow analytical method is cast for unbalanced three-phase ADNs. Finally, the method is applied to the ADN power flow analysis for multiple scenarios. Comparisons with other power flow calculation methods validate the high efficiency and computational tractability of the proposed method.