Some of the most remarkable issues related to interharmonic theory and modeling are presented. Starting from the basic definitions and concepts, attention is first devoted to interharmonic sources. Then, the interharmonic assessment is considered with particular attention to the problem of the frequency resolution and of the computational burden associated with the analysis of periodic steady-state waveforms. Finally, modeling of different kinds of interharmonic sources and the extension of the classical models developed for power system harmonic analysis to include interharmonics are discussed. Numerical results for the issues presented are given with references to case studies constituted by popular schemes of adjustable speed drives.

Energy storage is traditionally well established in the form of large scale pumped-hydro systems, but nowadays is finding increased attraction in medium and smaller scale systems. Such expansion is entirely complementary to the forecasted wider integration of intermittent renewable resources in future electrical distribution systems (Smart Grids). This paper is intended to offer a useful tool for analyzing potential advantages of distributed energy storages in Smart Grids with reference to both different possible conceivable regulatory schemes and services to be provided. The Smart Grid Operator is assumed to have the ownership and operation of the energy storage systems, and a new cost-based optimization strategy for their optimal placement, sizing and control is proposed. The need to quantify benefits of both the Smart Grid where the energy storage devices are included and the external interconnected grid is explored. Numerical applications to a Medium Voltage test Smart Grid show the advantages of using storage systems related to different options in terms of incentives and services to be provided.

Distribution System Operators have the duty of managing the distribution system safely and efficiently. Distribution revenues are generally based on asset value but revenue systems based to some extent on network performance (e.g. on the level of losses or reliability) are becoming common in many countries. Thus the distribution planner has to maximise the profit of the investments for the system development and to improve the performance of the system as well. In many case the goals are conflicting and multiobjective planning criteria may be adopted to reduce network costs and investments and to maximise some specific performance (e.g. network reliability). Furthermore, with multiobjective programming the distribution planner has the possibility of choosing a trade-off solution in a set of acceptable solutions by applying criteria to minimise risks in uncertain scenarios. These features are particularly useful whether, according to the EU directive EU/2003/54, Embedded Generation (EG) and DSM are considered as possible planning alternatives to be compared with more traditional development actions in the search for the best solution to planning problems. In this context, a methodology is presented based on multiobjective programming and decision theory which helps the decision maker to find the best development plan for the system by using the EG as a development option and by explicitly managing risks and uncertainties. In particular, the proposed methodology solves the problem of maximising the network performance by optimising some power quality indicators, like voltage quality and harmonic distortion, and by minimising the network costs (e.g. energy losses or upgrade investments) with an optimised use of EG. For this reason, the problem of the optimal sizing and siting of EG has been reformulated as a constrained, multiobjective, and nondifferentiable optimisation problem. The main idea is to apply a double trade-off procedure: the first trade-off, by means of a multiobjective ɛ-constrained technique, allows a wide family of good EG siting and sizing solutions for all the scenarios considered (e.g. associated to a different set of wind speed at all the possible locations); the second one allows isolating the most robust solutions. In this way the planner is completely free to drive the optimisation in a certain direction without loosing objectivity and generality. Simulation results obtained by using the proposed procedure on a test system demonstrate that alternatives disregarded by the decision theory application can result the most interesting ones.

A new short-term probabilistic forecasting method is proposed to predict the probability density function of the hourly active power generated by a photovoltaic system. Firstly, the probability density function of the hourly clearness index is forecasted making use of a Bayesian auto regressive time series model; the model takes into account the dependence of the solar radiation on some meteorological variables, such as the cloud cover and humidity. Then, a Monte Carlo simulation procedure is used to evaluate the predictive probability density function of the hourly active power by applying the photovoltaic system model to the random sampling of the clearness index distribution. A numerical application demonstrates the effectiveness and advantages of the proposed forecasting method.

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