Abstract
Isolated power systems with high shares of renewables can require additional inertia as a complementary resource to assure the system operation in a dynamic safe region. This paper presents a methodology for the day-ahead Unit Commitment/ Economic Dispatch (UC/ED) for low-inertia power systems including dynamic security constraints for key frequency indicators computed by an Artificial Neural-Network (ANN)-supported Dynamic Security Assessment (DSA) tool. The ANN-supported DSA tool infers the system dynamic performance with respect to key frequency indicators following critical disturbances and computes the additional synchronous inertia that brings the system back to its dynamic security region, by dispatching Synchronous Condensers (SC) if required. The results demonstrate the effectiveness of the methodology proposed by enabling the system operation within safe frequency margins for a set of high relevance fault type contingencies while minimizing the additional costs associated with the SC operation.

Abstract
This work models a distributed community-based market with diverse assets (photovoltaic generators and energy storage systems), accounting for network constraints and adopting the relaxed branch flow model. The market is modeled in a single and fully distributed approach, employing the alternating direction method of multipliers (ADMM) to prevent voltage and line capacity problems in the community network and improve data privacy and reduce the communication burden. Different scenarios, based on the penalty term and the agents' number, are tested to study the efficiency of the algorithm and the convergence rate of the ADMM distributed model. The proposed method is tested on 10-bus, 22-bus, and 33-bus medium voltage radial distribution networks, where each node contains a large prosumer with one or several assets. One important conclusion is that the implemented residual balancing technique improves the efficiency of the ADMM distributed algorithm by increasing the convergence rate and reducing the computational time.

Abstract
Distribution systems planning heavily depends on the predicted future consumptions in the service area. When statistical data exist about past consumptions, probabilistic forecasting methods may be applied, and expected cost/benifit and risk analysis are used to decide between different solutions. In most cases, however, this strategy is not applicable, due mainly to the lack of significant data (new developing areas, rapidly changing situations) and uncertainty of economic and social factors. In the latter case, the use of fuzzy models is an interesting alternative, accommodating expert planner's qualitative judgments about future loads and allowing us to use 'typical' load diagrams in new areas. The paper discusses the main concepts of electric distribution system planning when loads are fuzzy modeled, and presents an illustrative application example. © 1995.

Abstract
This paper describes a research where the main goal was to predict the future values of a time series of the hourly demand of Portugal global electricity consumption in the following day. In a preliminary phase several regression techniques were experimented: K Nearest Neighbors, Multiple Linear Regression, Projection Pursuit Regression, Regression Trees, Multivariate Adaptive Regression Splines and Artificial Neural Networks (ANN). Having the best results been achieved with ANN, this technique was selected as the primary tool for the load forecasting process. The prediction for holidays and days following holidays is analyzed and dealt with. Temperature significance on consumption level is also studied. Results attained support the adopted approach.

Abstract
Electrical distribution utilities have been dealing with the problem of estimation of distribution network load diagrams, either for operation studies or in forecasting models for planning purposes. Load curve assessment is essential for an efficient management of electric distribution systems. However, the only information available for most of the loads (namely LV loads) is related to monthly energy consumption. The general procedure uses measurements in consumers to construct inference engines that predict load curves using commercial information. This paper presents a new approach for this problem, based on Kohonen maps and Artificial Neural Networks (ANN) to estimate load diagrams for the Portuguese distribution utilities. A method for estimating error bars is also proposed in order to provide a high order information about the performance of load curve estimation process. Performance attained is discussed as well as the method to achieve confidence intervals of the main predicted diagrams. © Springer-Verlag Berlin Heidelberg 1997.

Abstract
In this paper, the Unit Commitment (UC) problem is presented and solved, following an innovative approach based on a metaheuristic procedure. The problem consists on deciding which electric generators must be committed, over a given planning horizon, and on defining the production levels that are required for each generator, so that load and spinning reserve requirements are verified, at minimum production costs. Due to its complexity, exact methods proved to be inefficient when real size problems were considered. Therefore, heuristic methods have for long been developed and, in recent years, metaheuristics have also been applied with some success to the problem. Methods like Simulated Annealing, Tabu Search and Evolutionary Programming can be found in several papers, presenting results that are sufficiently interesting to justify further research in the area. In this paper, a resolution framework based on GRASP - Greedy Randomized Adaptive Search Procedure - is presented. To obtain a general optimisation tool, capable of solving different problem variants and of including several objectives, the operations involved in the optimisation process do not consider any particular characteristics of the classical UC problem. Even so, when applied to instances with very particular structures, the computational results show the potential of this approach.