Abstract
This paper is part one of a two-part series discussing how Regulator requirements for continuity of supply could be incorporated in the reliability analysis of existing electricity networks and future 'smart grids'. The paper uses examples of overall and guaranteed standards of performance from the UK and Italy, specifying requirements that network operators should satisfy with respect to excessively long and/or too frequent supply interruptions. Besides the relevant Regulator requirements, this paper presents input data, parameters and models required for comprehensive reliability assessment, while Part 2 paper presents scenarios and results for test network based on both analytical and probabilistic reliability procedures.

Abstract
This is the second paper in a two-part series discussing how Regulator requirements for continuity of supply could be incorporated in the reliability analysis of existing electricity networks and future 'smart grids'. Part 1 paper presents input data, parameters and models required for a comprehensive assessment of system reliability performance, including an overview of the overall and guaranteed standards of performance in the UK and Italy. This paper presents scenarios and results of both analytical and probabilistic reliability assessment procedures for the test network introduced in Part 1 paper.

Abstract
Stationary batteries are currently seen as an interesting solution to deal with the variability of the renewable energy sources. In the same way as other types of storage, e.g. pumped-hydro units, this new type of storage equipment can improve the use of Renewable Energy Sources (RES). Additionally, the stationary batteries location in the grid is not as physically constrained as other storage systems and can be optimally selected to maximize its overall benefits. This paper proposes a new methodology to represent the unique stochastic behavior of stationary batteries while integrated into an electrical power system. This methodology includes not only the technical restrictions of this type of storage system but also how its operation strategy affects its lifetime. The methodology was tested on a small test system, which is based on the IEEE-RTS 79, using sequential Monte Carlo simulation as its core to accurately reproduce the chronology of events of stationary batteries. The results of the simulation are focused on the potential impacts of these storage devices not only in terms of renewable energy used but also in the adequacy of supply.

Abstract
This paper explores a technique denoted LASCA to solve large scale optimization problems with metaheuristics by reducing the search space dimension with autoassociative neural networks. The technique applies autoencoders as a reversible mapping between the original problem space and a reduced space. A metaheuristic then evolves in the latter, having its objective function assessed in the original space. The technique is illustrated with an application of an Evolutionary Particle Swarm Optimization (EPSO) algorithm to four benchmarking unconstrained optimization functions and to a wind-hydro constrained coordination problem. The new technique allows an improvement in the quality of the solutions attained.

Abstract
This work presents a methodology for selecting the most relevant measurements for real-time power system monitoring. A genetic algorithm is employed to find the meter plan, composed of relevant, real-time measurements and pseudo-measurements that present the best compromise between investment costs and state estimation performance. This is achieved by minimizing both the number of real-time measurements in the power network and the degradation of the estimated states. Performance measures based on the Information Theory are investigated. Simulation results illustrate the performance of the proposed method.

Abstract
The growing penetration of solar power technology at low voltage (LV) level introduces new challenges in the distribution grid operation. Across the world, Distribution System Operators (DSO) are implementing the Smart Grid concept and one key function, in this new paradigm, is solar power forecasting. This paper presents a new forecasting framework, based on vector autoregression theory, that combines spatial-temporal data collected by smart meters and distribution transformer controllers to produce six-hour-ahead forecasts at the residential solar photovoltaic (PV) and secondary substation (i.e., MV/LV substation) levels. This framework has been tested for 44 micro-generation units and 10 secondary substations from the Smart Grid pilot in Evora, Portugal (one demonstration site of the EU Project SuSTAINABLE). A comparison was made with the well-known Autoregressive forecasting Model (AR - univariate model) leading to an improvement between 8% and 12% for the first 3 lead-times.